European Journal of Operational Research244(2015)
55–65
Contents lists available at ScienceDirect
European Journal of Operational Research
journal homepage:
https://www.doczj.com/doc/d99387535.html,/locate/ejor
Models,solutions and enabling technologies in humanitarian logistics
Linet?zdamar a,?,Mustafa Alp Ertem b,1
a Industrial and Systems Engineering Department,Yeditepe University,26A?g ustos Yerle?simi Kay??sda?g?Cad.,34755˙Istanbul,Turkey
b Department of Industrial Engineering,?ankaya University,Eski?sehir Yolu29.km,06810Ankara,Turkey
a r t i c l e i n f o
Article history:
Received13February2014
Accepted18November2014
Available online26November2014
Keywords:
Humanitarian logistics
Modeling approaches
Information systems
a b s t r a c t
We present a survey that focuses on the response and recovery planning phases of the disaster lifecycle.
Related mathematical models developed in this area of research are classi?ed in terms of vehicle/network
representation structures and their functionality.The relationships between these characteristics and model
size are discussed.The review provides details on goals,constraints,and structures of available mathematical
models as well as solution methods.In this review,information systems applications in humanitarian logis-
tics are also surveyed,since humanitarian logistics models and their solutions need to be integrated with
information technology to enable their use in practice.
?2014Elsevier B.V.All rights reserved.
1.Introduction
Since the1950s,the number and magnitude of disasters have
grown exponentially,the number of affected people has grown in
proportion(about300million persons per annum on the average
since the1990s)and the annual damage costs have risen to about
0.17percent of the world GDP(Guha-Sapir,Hoyois,&Below,2014).
The International Disaster Database indicates that Asia and the Amer-
icas are the most affected continents by disasters(mostly by?oods,
quakes and storms)with Europe standing as the third most affected
continent.
Recently,researchers have conducted various surveys on disas-
ter management.Altay and Green(2006)emphasize lifecycle phases
in disaster operations management,and later on,Apte(2009)dis-
cusses details of solution methods and limitations of proposed mod-
els designed for each phase.Natarajarathinam,Capar,and Narayanan
(2009)provide a review for supply chains in times of crisis in both
OR/MS and supply chain management journals.Caunhye,Nie,and
Pokharel(2012)review optimization models for pre-disaster facility
location,stock prepositioning,relief distribution,and casualty trans-
portation.de la Torre,Dolinskaya,and Smilowitz(2012)focus on
relief routing models with an emphasis on egalitarian and utilitarian
objectives in disaster response.Galindo and Batta(2013)compare
and contrast the articles in Altay and Green(2006)with recent de-
velopments in humanitarian logistics to highlight whether OR/MS
researchers have addressed the aforementioned literature gaps.
?Corresponding author.Tel.:+902165780450,+902165780000;fax:+90216
5780400.
E-mail addresses:ozdamar.linet@https://www.doczj.com/doc/d99387535.html,(L.?zdamar),alpertem@https://www.doczj.com/doc/d99387535.html,(M.
A.Ertem).
1Tel.:+903122331367;fax:+903122331026.
Anaya-Arenas,Renaud,and Ruiz(2014)discuss location and trans-
portation models and categorize them according to their objectives,
constraints and solution methods.We observe that in these surveys,
recovery phase models are not as emphasized as preparedness and
response models.
Here,we review the logistics models developed for the response
and recovery planning phases in terms of their modeling features
and formulation structures.Selected articles describe various models
that are typical with respect to the latter features.We discuss vehicle
representation styles that impact model solvability in capacitated
vehicle routing models and categorize relevant models accordingly.
We regret that we cannot cite all available papers pertaining to each
category due to space limitations.
We also investigate the technological advances that facilitate the
execution of proposed models and solutions on various types of infor-
mation systems.The latter review leads to the observation that stan-
dard software that integrates different planning phases is needed in
order to achieve unobstructed international humanitarian coopera-
tion.The goal would be to enable global access to such software by all
countries sharing emergency resources.We end the survey with con-
cluding remarks that point out the areas that bear more improvement.
We note that this survey overlaps partially with the one presented
in a keynote speech(?zdamar,EURO-INFORMS Conference,July2nd,
2013,Rome).
2.Concentration areas in humanitarian logistics research
Humanitarian logistics research is dedicated to the following
three planning stages in the disaster lifecycle:(pre-disaster)pre-
paredness phase,(post-disaster)response and recovery phases.In
the pre-disaster phase,preparedness plans and risk-prevention ac-
tions,such as infrastructure and building re-enforcement,reduce https://www.doczj.com/doc/d99387535.html,/10.1016/j.ejor.2014.11.030
0377-2217/?2014Elsevier B.V.All rights reserved.
56L.?zdamar,M.A.Ertem/European Journal of Operational Research244(2015)55–65
Table1
Models for relief delivery and casualty transport.
Citation Model type Objective(s)Constraints Solution methods
Haghani and Oh(1996)DNF OC CV,D,MC,MD,SLD,TW Lagrangean relaxation
?zdamar et al.(2004)DNF CUD CV,D,LS,MC,MD,MM,SLD Lagrangean relaxation,modi?ed
shortest path
Yi and?zdamar(2007)/?zdamar and Yi (2008)/Yi and Kumar(2007)DNF WUD(CUD+CUC)CV,DP,LS,MC,MD,MF,MPS,MPQ,SLD Exact/tour construction/Ant colony
algorithm
Zhan and Liu(2011)DNF ETT,EUD CV,D,DU,LS,RU,SU Two-stage stochastic program with
recourse
Afshar and Haghani(2012)DNF WUD(CUD+CUC)CV,D,LS,MC,MD,PL,SLD Exact
Naja?et al.(2013)DNF,robust
optimization (CUD+CUC)+NV CU,CV,DP,DU,LS,MC,MD,MF,MM,
SLD,SU
Exact
?zdamar(2011)SNF TT CV,DP,LS,MD,MC,MF,PAY,RE,SLD Exact
?zdamar and Demir(2012)SNF TD CV,DP,LS,MC,MD,MF,SLD Hierarchical planning,clustering
Zhang et al.(2012)UNF TT(primary and
secondary disasters)
D,LS,MC,MD,UV LP based heuristic
Barbaroso?g lu and Arda(2004)UNF TC D,DU,MC,MD,MM,RU,SU,UV Two-stage stochastic program with
recourse
Tzeng et al.(2007)UNF EQD,TC,TT D,LS,MC,MD,SLD,UV,2stage supply
chain
Exact
Gu(2011)UNF(fuzzy)UD LS,TTU,TW,UV Exact
Bal??k et al.(2008)RE,dynamic TC,minimax CUD(EQD)CV,D,LS,MC,MD,SD,TW Exact
Lin et al.(2011)RE,dynamic EQD,TT,UD CV,D,MC,SD,SLD,TW,WTW Decomposition,GA
Hsueh et al.(2008)CVR TT,LA CV,DP,TTU,TW,dynamic scheduling Route construction heuristic
Barbaroso?g lu et al.(2002)CVR OC,RT CV,DP,MC,PAY,RE,SD,SLD Hierarchical planning
de Angelis et al.(2007)CVR SD CV,D,MD,PL,TW Exact
Shen et al.(2009)1st stage:stochastic
CVR2nd stage:LP
UD,AT CV,D,DU,RTD,SD,SLD,TTU Exact,Tabu Search
Vitoriano et al.(2009,2011)CVR:goal prog.EQD,OC,RR,RS BUD,CV,DP,MD,STE Exact
Berkoune et al.(2012)CVR TT CV,D,MC,MD,WTW Set enumeration heuristic,GA
Nolz et al.(2011)CVR,covering TSP AP,MCD,TT,UN CV,PC,SD,STE Memetic algorithm
Sheu(2010)–SD D,DU,UV Fuzzy clustering heuristic
Objectives–AP:number of alternative paths,AT:vehicle arrival time,CUC:cumulative unmet demand over time,CUD:cumulative unserved casualties,EQD:equity of satis?ed demand,ETT:Expected Travel Time,EUD:expected unmet demand,FT:?ow time,LA:late arrivals,MCD:maximal demand covering,NV:number of vehicles in transit,OC:operation cost,RR:road reliability,RS:road security,RT:response time,SD:satis?ed demand,TC:transport cost,TD:travel distance,TT:travel time,UD:unmet demand,UN:unreachability of nodes,WUD:weighted unmet demand;constraints–BUD:budget for vehicles,CU:casualty uncertainty,CV:capacitated vehicles,D:delivery only,DP:delivery and pickup, DU:demand uncertainty,LS:limited supplies,MC:multicommodity,MD:multi-depot,MF:medical facilities,MM:multi-mode transport,MPS:medical personnel sharing,MSQ: medical service rates,PAY:helicopter payload,PC:population covering,PL:parking limitation,RE:refueling,RTD:response time deadline,RU:road uncertainty,SD:single depot, STE:sub-tour elimination,SU:supply uncertainty,TTU:travel time uncertainty,TW:delivery time windows,UV:uncapacitated vehicles,WTW:working time windows,SLD: split deliveries(pickups);model types–DNF:dynamic network?ow,TS:Time-Space Network,UNF:uncapacitated network?ow,SNF:static network?ow,CVR:classical vehicle routing,RE:route enumeration.
damage in disaster prone areas.Furthermore,inventory and equip-ment pre-positioning enable fast relief distribution to survivors from pre-stocked commodities(Bal??k&Beamon,2008;Duran,Gutierrez, &Keskinocak2011;Ichoua,2010;Mete&Zabinsky,2010;Ukkusuri &Yushimito,2008).On the other hand,pre-positioning of shelters leads to coordinated evacuation of the population to nearby shelters (Widener&Horner,2011).Pre-disaster models are usually stochas-tic due to the uncertainty of the disaster impact level.Two-stage stochastic programming(Mete&Zabinsky,2010)and stochastic sce-nario analysis approaches(Bal??k&Beamon,2008;Chang,Tseng,& Chen,2007)are frequently utilized to solve these models.
Post-disaster phases involve mass evacuation to shelters(can also be executed as a pre-event activity if notice of disaster is given),relief delivery,casualty transportation,debris collection,road clearing and regional re-development.These activities enhance post-disaster sur-vival rates and economic growth.We review response and recovery logistics models in the following sections.
2.1.The response phase
Response phase logistics models fall under two major categories: a)relief delivery/casualty transport models,b)mass evacuation mod-els.Table1provides a list of models related to relief delivery/casualty transport with regard to their objectives,model particulars and struc-ture,decisions considered,and solution methods.Table2describes mass evacuation models in a similar fashion.In the following sections, we discuss several vehicle representation styles involving capacitated vehicle transport,and then,we classify the literature according to the latter feature and other characteristics.
2.1.1.Vehicle representation styles in models with capacitated vehicles
Relief delivery,casualty transport and public transit mass evacua-tion models are involved with vehicle routing.In relief delivery,mod-els consider the transport of supplies to demand points by capacitated vehicles.Vehicles start their itineraries at warehouses and serve sev-eral demand points on their routes.A trip does not necessarily end at the point of origin.In casualty transport,vehicles visit several loca-tions where casualties are waiting,pick them up and transfer them to one or more medical facilities.In mass evacuation,where threatened populations have to relocate to safe zones,evacuation may take place in personally owned cars or public transit transport.The?rst type of evacuation involves tra?c?ow management rather than individual vehicle routing.The second type of evacuation involves picking up waiting evacuees at bus stops and transferring them to shelters.In this case,bus capacity and vehicle routing issues arise.Vehicle rout-ing in humanitarian logistics context often involves split deliveries and pickups due to the magnitude of demands.
Models that consider capacitated vehicles have restrictions related to vehicle capacity,vehicle-load conformity(such as ambulances for casualties,lorries for food),road reliability,security and accessibil-ity,supply/vehicle availability,depot/shelter/pickup location capac-ity,delivery time windows,re-fueling and parking limitations,split or non-split deliveries/pickups,response time deadlines,budget limits on vehicle?eet size,and vehicle working time windows.The goals stated are usually cost/travel time-,demand statisfaction-,response
L.?zdamar,M.A.Ertem/European Journal of Operational Research244(2015)55–6557
Table2
Models for mass evacuation.
Citation Objectives Model type Model features Solution method
Chiu and Zheng(2007)TTT CTM(LP),CE Multi priority group evacuation,managing tra?c
?ow,destination selection,departure schedules
Exact
Liu et al.(2006)Bi-level:MF,TTT CTM(LP),CE Tra?c?ow and routing Optimization and simulation
Miller-Hooks and Patterson(2004)NCT Time dependent quickest
?ow(LP),CE TS network,time variant travel times and link
capacities
Exact
Lim et al.(2012)TNE(weighted sum
of?ows)Capacitated MCNF(MIP),
CE
TS network,paths,schedules and?ows Dijkstra for paths;max?ow
for?ows(greedy)
Cova and Johnson(2003)TTD MCNF(MIP),CE Lane-based routing,intersection delay
management
Successive shortest path Naghawi and Wolshon(2012)TNE Multi modal MCNF,CE Evacuation routes,?ows Simulation
Cui et al.(2014)TCF Nonconvex nonlinear
MCNF,CE Time variant travel times,contra?ows(rescue
?ows),lane reversal
Exact
Kongsomsaksakul et al.(2005)TET,TTT MINLP:bi-level
location–allocation
model,CE
Pre-?ood evacuation routes and shelter selection GA
Hobeika and Kim(1998)TET,NC Tra?c simulation model,
CE
Routes and schedules Simulation
Campos et al.(2012)MF MCNF(LP):maximum
?ow,CE
Non-intersecting paths to shelters,link capacities Iterative heuristic
Chen and Chou(2009)NCT,DT,TTT,ATS Simulation,PT Bus assignment to routes,single trip,contra?ows Simulation
Perkins,Dabipi,and Han(2001)TTT Simulation,PT Pre-set routes,bus departure times,single trip Simulation Mastrogiannidou et al.(2009)TTD Simulation,PT Bus assignment to pickup stops based on shortest
time,multiple trips
Cluster?rst,route second Bish(2011)TET+MET CVR,RE,PT Split delivery,multi depot,multiple trip Constructive heuristic Murray-Tuite and Mahmassani(2003)TTT,WT CVR,PT Vehicle routes from their point of start in the
network(anywhere)to pickup to shelter
Exact
Liu and Yu(2014)TNE,TTD CVR,PT Bus stop capacity,evacuee to stop allocation,single
trip
Exact
Song et al.(2009)TTD Stochastic CVR,PT Demand uncertainty,evac.deadline,shelter
selection
Simulation based GA
He et al.(2009)TET Stochastic LR(MIP),PT Shelter selection,bus?eet size,routes,demand
uncertainty
GA,hill climbing,ANN
Sayyady(2007),Sayyady and Eksioglu
(2010)
TC,TET,WT DNF-TS(MIP),PT Pedestrian routes,bus routes,single trip Tabu Search
Margulis et al.(2006)TNE RE,PT Bus assignment to preset routes,multiple trips Exact
Na et al.(2012)MET,TC(secondary
evacuation)RE,PT Route assignment(with secondary evacuation),
capacitated buses and hospitals
Exact
Objectives–TTT:Total Travel Time,TTD:Total Travel Distance,TET:Total Evacuation Time,DT:Delay Time,NCT:Network Clearance Time,MET:Maximum Evacuation Time,WT: Waiting Time of evacuees,TCF:Costs of Flows,TC:Travel Cost,ATS:Average Tra?c Speed,TNE:Total Number of Evacuees,NC:Number of Casualties,MF:Maximum Flow;model types–LP:Linear Program,MIP:Mixed Integer Program,BP:Binary Program,MINLP:Mixed Integer Nonlinear Program,CTM:Cell Transmission Model,MCNF:Minimum Cost Network Flow,CE:Car evacuation,PT:Public Transport,LR:Location-Routing,CVR,RE,DNF-TS as de?ned in Table1.
time-,and road-risk related.Among these,egalitarian goals(satisfy-ing all bene?ciaries equally),and utilitarian goals(minimizing sums rather than mini-max objectives)should be differentiated(Bish,2011; Campbell,Vandenbussche,&Hermann,2008;de la Torre et al.,2012; Huang,Smilowitz,&Bal??k et al.,2012).
Below we discuss the styles in which capacitated vehicles are rep-resented in models.They affect the size of the models solved,and therefore,have an impact on solution times.
A.DNF approach.In the Dynamic Network Flow(DNF)approach ve-
hicles are represented as integer valued?ows that are linked with commodity?ows in a multi-period(dynamic)network?ow(NF) model.Vehicle?ow variables have link(from–to nodes)indices and a time index that indicate the time of vehicle traversal over a link.
i.DNF with time–space(DNF-TS)networks.In DNF-TS models
where a time–space(TS)network is used by creating a copy of the network for each time interval in the planning horizon.
This structure leads to(T2N2)vehicle related integer variables where T is the length of planning horizon and N is the number of nodes in the network(Haghani&Oh,1996).
ii.DNF excluding time–space networks.In DNF models where travel times are embedded in equations as time lags(?zdamar, Ekinci,&Kü?ükyazici,2004),the number of vehicle related in-teger variables reduce to(TN2).In all types of DNF models, once the mixed integer DNF is solved,a post-processing stage has to be activated.At this stage,vehicle routes are constructed
from vehicle and commodity?ow information.Yi and?zdamar (2007)accomplish the latter by solving a Linear Program(LP).
B.SNF approach.The Static Network Flow(SNF)is a single period NF
model applied on a rolling horizon basis.All other features of the SNF are similar to those of the DNF model.The SNF model has (N2)integer variables;therefore,it is more e?cient.?zdamar and Pedamallu(2011)compare both DNF and SNF models to show that the sizes of SNF instances that can be solved optimally are twice as those of the DNF instances.
C.RE approach.The Route Enumeration(RE)approach has a pre-
processing stage where all feasible routes between all pairs of supply and demand nodes are constructed.This set of R routes is then fed to a Mixed Integer Program(MIP)as an input vector.Vehi-cles are then optimally assigned to these routes to satisfy demand.
Here,vehicles are represented by binary variables with vehicle, route and time indices.The dynamic RE model has(vRT)binary variables,where v is the number of vehicles(Bal??k,Beamon,& Smilowitz,2008).A setback in this approach is the cardinality of R that can be as large as(2N?2M(N?M))where M is the number of depots in a network with N nodes.In large scale relief networks, the construction of R routes becomes computationally intractable and the solution of the MIP requires substantial computation time.
In public transit mass evacuation models,the Transportation De-partment may assign R pre-de?ned itineraries for all buses.In that case,the cardinality of R is at their discretion.
D.CVR approach.In the Classical Vehicle Routing(CVR)approach each
vehicle is represented by a binary variable that speci?es the link,
58L.?zdamar,M.A.Ertem/European Journal of Operational Research244(2015)55–65
vehicle,and tour indices.This results in(vrN2)binary variables, where r is the number of trips allowed for each vehicle.Routes are explicitly constructed by the CVR model and there is no need for pre-or post-processing stages.Yi and?zdamar(2007)com-pare the CVR model with the SNF model in terms of model size and exact solution time.The researchers observe that the CVR ap-proach in?ates the model size considerably.As a matter of fact,in emergency logistics,the cardinality of vr can be quite large.
2.1.2.Classi?cation of relief delivery and casualty transport literature
A.The dynamic/static network?ow(DNF/DNF-TS/SNF)models.
DNF/DNF-TS models.Models for relief delivery:Cost related goals. Haghani and Oh(1996)consider a DNF-TS model with routing,trans-fer and inventory carryover links.Vehicle and commodity?ows are coupled by vehicle arrival time windows and the goal is to mini-mize operation costs.The solution involves a Lagrangean Relaxation approach that solves very small networks(10nodes).Demand sat-isfaction goals.?zdamar et al.(2004)introduce a multi-modal DNF transport model with limited supplies and dynamic vehicle availabil-ity where the goal is to minimize the cumulative unmet demand over T.Two solution approaches,a Lagrangean Relaxation and a modi-?ed shortest path heuristic,are proposed.Afshar and Haghani(2012) model a seven-layer relief delivery supply chain network(the FEMA standard)using a DNF-TS model where features such as storage space, parking space,and inbound/outbound vehicle?ow limitations are in-troduced.The goal is to minimize cumulative unmet demand over T.
Integrated models for relief delivery and casualty transport:demand satisfaction goals.Yi and?zdamar(2007)introduce a DNF Location-Routing(LR)model with the goal of minimizing the cumulative pri-oritized unmet requests.The model selects and locates temporary medical facilities,enables sharing medical staff among facilities,and imposes?nite medical service rates.The researchers solve scenarios with relief networks of up to60nodes within2minutes of computa-tion time.Yi and?zdamar(2007)solve this model using a two-stage Ant Colony Optimization heuristic where the ants build routes in the ?rst stage and commodity?ows are optimized in the next by solving the maximum?ow problem.Instances with relief networks of up to 80nodes and55vehicles are solved near-optimally.?zdamar and Yi (2008)propose a constructive node-insertion heuristic and solve the same instances faster,but with slightly worse solution quality.
SNF models.Integrated models for relief delivery and casualty trans-port:travel time/distance related goals.?zdamar(2011)proposes a model for coordinating helicopter logistics in medical supply deliv-ery and casualty pickup.The goal is to minimize total?ight time plus stopping times.Fuel feasibility and itinerary length limits are taken care of in the post processing stage.The case study involves a relief network with more than70helipads scattered over Istanbul(Turkey).?zdamar and Demir(2012)solve large-scale relief delivery and ca-sualty pickup scenarios within a hierarchical planning framework. Recursive clustering is applied to demand nodes in the relief network and the method ensures consistency of supply and hospital service availability at different network aggregation levels.The researchers solve scenarios of up to1000nodes near-optimally and verify the ef-?ciency of the approach on a Katrina(New Orleans)?ooding scenario (?zdamar,Demir,Bak?r,&Y?lmaz,2013).
B.The Route Enumeration(RE)models.Relief delivery models:cost re-lated goals.Bal??k et al.(2008)propose a dynamic relief delivery model with limited supplies and vehicle availability constraints.The goal is to minimize transport,backorder,and lost sales costs where critical items such as tents and blankets are backordered.Regularly con-sumed items such as food and water become lost sales when demand is not met.In the?rst stage,the researchers identify all possible sets of demand points that can be served in one trip.Each such set rep-resents nodes on a feasible route.The order of nodes on each route is then optimized by solving the TSP.Then,the set of feasible routes are fed into the model and vehicle assignments are made.Demand and travel time related goals.Lin,Batta,Rogerson,Blatt,and Flanigan (2011)de?ne three goals in their model:minimize cumulative unmet demand,minimize travel time,and minimize the pairwise difference between demand?ll rates at nodes.These are then merged into a single?tness function in a Genetic Algorithm(GA)that enumerates routes.A second solution approach clusters demand nodes?rst and enumerates all routes within each cluster.The GA is found to be slow but its delivery rate is higher than the clustering approach.The clus-tering approach solves a scenario with three million houses grouped into nine clusters within2minutes.
C.The Classical Vehicle Routing(CVR)models.Relief delivery models: Demand related goals.de Angelis,Mecoli,Nikoi,and Storchi(2007) schedule cargo planes that carry out food delivery in Angola.In their model,the number of planes and supplies are limited,overnight park-ing near warehouses is mandatory with limited parking space,split deliveries are not allowed,and take-offs are allowed only in daylight due to security reasons.The goal is to maximize satis?ed demand. The solution to the problem is obtained in2hours by CPLEX.Cost, road security and demand equity goals.Vitoriano,Ortuno,and Tirado (2009),and Vitoriano,Ortuno,Tirado,and Montero(2011)propose a multi-objective model for relief delivery.The goals are minimiz-ing transport and operating costs,minimizing the maximum looting probability(based on the number of vehicles traversing arcs),and minimizing the maximum percentage of unmet demand over nodes. The model contains of a transport budget and sub-tour elimination constraints.The Haiti Quake case study is analyzed with three depots, 12transfer nodes,and nine demand zones to construct the routes of 300trucks.Berkoune,Renaud,Rekik,and Ruiz(2012)impose a vehicle itinerary length limitation in a model where the goal is to minimize travel distance.A GA is proposed and tested on networks with up to 60demand nodes and three suppliers.The gap from the best solution is found to be20percent.
Integrated relief delivery and casualty transport models:Cost and response time related goals.Barbaroso?g lu,?zdamar,and Cevik(2002) propose an integrated CVR model for helicopter pickup and delivery operations and solve very small instances with a two stage hierar-chical planning approach.Travel time related goals.Hsueh,Chen,and Chou(2008)use a delivery/pickup CVR model that considers the re-verse logistics of relief delivery as well.Travel times are assumed to be time-variant.A node insertion heuristic constructs the initial routes and then improved by node exchange with the goal of mini-mizing total travel time.A100node relief network with ten vehicles is tested.
D.Models involving uncertainty.Relief delivery models:Demand and response time related goals.Shen,Dessouky,and Ordó?ez(2009)pro-pose a two-stage CVR type model for antidote distribution in large scale bioterrorist attacks,assuming that demand and travel times are uncertain.Their goals are to minimize unmet demand and satisfy ser-vice deadline to avoid deaths.The pre-planning stage is modeled by chance constrained programming designed for split delivery vehicle routing and solved by Tabu Search.In the operational stage,the so-lution of the previous model is?xed for vehicles and a LP determines loads.Tests are conducted on a network of50demand nodes.Road risk related goals.Zhan and Liu(2011)consider path availability risk using the NF model and minimize both expected travel time and unmet demand.An infrastructure risk in terms of road network uncertainty is discussed in Nolz,Semet,and Doerner(2011).The problem is to locate and deliver water dispensers so as to cover as much of the pop-ulation as possible.Road risks are minimized by the following goals: minimize the maximum hazard over all arcs of each path,maximize the number of alternative paths,minimize the unreachability of all al-ternative paths,and maximize the number of paths whose risk value is below a threshold.The researchers model the problem as a TSP
L.?zdamar,M.A.Ertem/European Journal of Operational Research244(2015)55–6559
with a single capacitated vehicle and solve it using a multi-objective Memetic Algorithm.
Integrated relief delivery and casualty transport models:Demand and congestion related goals.Naja?,Eshghi,and Dullaert(2013)add the goal of minimizing the number of traveling vehicles to the cumulative unmet demand objective in a multi-objective DNF risk model where demand uncertainty exists.Robust Optimization method is used to solve each objective hierarchically.A small case study involving16 nodes,?ve hospitals,and four vehicle types is solved.
https://www.doczj.com/doc/d99387535.html,work?ow models without capacitated vehicles(UNF).In Table 1,we also cite UNF relief delivery models that consider cost related (Barbaroso?g lu&Arda,2004),demand satisfaction related(Gu,2011), and time,cost and equity based(Tzeng,Cheng,&Huang,2007;Zhang, Li,&Liu,2012)objectives.Demand,supply and road uncertainties are considered by Barbaroso?g lu and Arda(2004)who propose a two-stage stochastic program with recourse to minimize costs.Tzeng et al.(2007)propose a deterministic two stage supply chain with split deliveries and limited supply availability.Gu(2011)considers travel time uncertainty and delivery time windows.
2.1.
3.Mass evacuation models
Mass evacuation models concentrate on Car Evacuation(CE)and tra?c?ow management,and also,on evacuation by public transit (PT).CE models are frequently represented by link capacitated Mini-mum Cost Network Flow(MCNF)models with vehicle?ow per hour limitations over links.Tra?c simulation tools are also used to ob-serve the impacts of lane reversals,intersection delay management, and tra?c direction reversals on tra?c?ows.Some models consider contra?ows of rescuers.
PT models optimize bus routes consisting of a depot,several pickup stations and a shelter.These models consist of pickup only vehicle routing problem.In some models bus departure times are also con-sidered to relieve tra?c congestion.Shelter selection may also be-come a part of the problem leading to a Location-Routing(LR)model. In no-notice evacuation,a bus conducts a single trip,however,in short notice evacuation,it can carry out multiple trips.The objectives in PT models can be time related,evacuee number related,cost and ?ow related.Vehicle representation is in accordance with CVR and RE classi?cation.In the following sections,we review relevant CE and PT models according to model functionality and structure.
A.Tra?c?ow management models:Car Evacuation(CE).Cell Trans-mission Models(CTM).CTM(Daganzo,1994)partition the car tra?c network into grids and control the tra?c?ow advancement over adjacent grids(cells).These models are obtained by transforming dif-ferential equations of tra?c?ow into simpler difference equations resulting in an LP.A bi-level CTM is given by Liu,Lai,and Chang (2006)which maximizes tra?c?ow and minimizes travel time.A combined optimization-simulation model is used to route the tra?c ?ow.Chiu and Zheng(2007)consider multi-group evacuation where some groups(e.g.,nurses)have priority over others.Ben-Tal,Chung, Mandala,and Yao(2011)tackle dynamic tra?c assignment problem such that tra?c risks are minimized.The researchers use Robust Op-timization to deal with tra?c?ow uncertainty.
Location–allocation models.Kongsomsaksakul,Yang,and Chen (2005)provide a bi-level location–allocation model for demand allo-cation to shelters and route selection.Both network links and shelters have capacity limits.The researchers use a Genetic Algorithm(GA)to minimize evacuation time and total travel time.
MCNF https://www.doczj.com/doc/d99387535.html,ler-Hooks and Patterson(2004)minimize net-work clearance time in a time dependent quickest?ow model(an LP)where travel times and link capacities are time-variant.Cova and Johnson(2003)discuss an MIP model for lane based evacuation rout-ing where delays due to intersections and lane changes are consid-ered.Solution method is based on successive shortest path algorithm.A mixed integer capacitated maximum?ow model is proposed by Lim,Zangeneh,Baharnemati,and Assavapokee(2012)where the goal is to maximize number of evacuees.Dijkstra’s algorithm is used to ?nd paths and a greedy maximum?ow algorithm is used to deter-mine the?ows.A multimodal NF model is described in Naghawi and Wolshon(2012)where routes are optimized by simulation.Campos, Bandeira,and Bandeira(2012)solve a maximum?ow problem with an iterative heuristic to identify two independent paths form each affected area to shelters.The idea is to minimize intersection de-lays and tra?c congestion.Cui,An,and Zhao(2014)minimize the costs of evacuation and the contra?ows of rescuers as well as lane reversal costs.They solve the nonlinear MCNF model using BARON (GAMS).
B.Public Transit models(PT).Simulation models.Chen and Chou (2009)deal with the assignment of buses to pickup points in a no-notice evacuation.Simulation with contra?ows is used to solve the problem so that tra?c speed,system travel time,and network clear-ance time are minimized.
CVR/LR/RE models.Murray-Tuite and Mahmassani(2003)mini-mize travel time including evacuee waiting time in a CVR model where capacitated vehicles(that can be anywhere in the network) pickup evacuees and transfer them to shelters.Margulis,Charosky, Fernandez,and Centeno(2006)propose a RE model that decides on the number of trips made between given pairs of pickup points and shelter nodes.Bus capacity cannot be exceeded on its trip and the goal is to maximize total number of evacuees.He,Zhang,Song,Wen,and Wu(2009)develop a stochastic LR model with demand uncertainty. Shelter selection,bus?eet size,and bus routes are optimized using a combined metaheuristic(GA,hill climbing and Arti?cial Neural Net-works).Song,He,and Zhang(2009)propose a LR model that selects pickup points among many in the city and optimize bus routes simul-taneously in a no-notice evacuation.Evacuation time has a deadline and the number of evacuees is uncertain.The goal is to minimize total travel time and the researchers propose simulation based GA. Bish(2011)proposes both CVR and RE type models with multiple bus depots,split delivery and capacitated shelters in a short-notice evacuation.A route construction and improvement heuristic is im-plemented.Na,Xueyan,and Mingliang(2012)propose a RE model that minimizes maximum evacuation time and the costs of secondary evacuation that involves re-directing patients to other hospitals.The model assigns routes for capacitated buses,shelter capacities are also limited.Liu and Yu(2014,chap.4)de?ne a CVR model where two sets of decisions are made simultaneously:evacuees are optimally allo-cated to capacitated pickup points(based on walking distances from parking lots)and optimal bus routes are constructed.The goals are to minimize total travel distance and maximize the number of evacuees transported.
DNF-TS models.Sayyady(2007)and Sayyady and Eksioglu(2010) represent waiting time of evacuees over a TS network in a no-notice evacuation.The model is solved by a Tabu Search that identi?es ve-hicle routes with the goal of minimizing total evacuation time,travel time,and waiting time.
2.2.The recovery phase
In the recovery phase,planning issues are concerned with proper damage assessment,an effective reverse logistics system for debris disposal and recycling,and infrastructure rebuilding with minimum cost and duration.Table3summarizes model features for the re-covery phase.Recovery models usually concentrate in two areas: road and other infrastructure restoration,and debris management that includes removal,disposal,and recycling.According to a FEMA report(2007),debris cleanup operations consist of two phases.Dur-ing the?rst phase,the goal is to clear debris from evacuation routes and other important paths to ensure tra?c?ow in affected areas.
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Table3
Models for recovery phase.
Citation Objective(s)Model type/features Solution method
Fetter and Rakes(2012)Debris facility setup and debris logistics cost Debris facility setup,location selection,
transport(MIP)
Exact
Hu and Sheu(2013)Costs of transport,delay risk,psychological Multi-stage debris collection supply chain
(LP)
Exact,Pareto by constraint adding
Chang and Nojima(2001)Measuring travel time extension due to
blocked links in network Network accessibility measure based on shortest path extensions
–
Sohn(2006)Link criticality measure for network
vulnerability Population based Chang and Nojima(2001)
network accessibility index
Measuring link criticality by sequential
link dropping on GIS map
Allen,Liu,and Singer(1993)Measuring areal network accessibility Measure based on average length of all
paths between all pairs of nodes in area
–
Jenelius and Mattsson(2012)Measuring unmet travel demand Grid based accessibility measure GIS software
Chen and Tzeng(1999,2000)1)Minimize travel time,work time,idle time
of work teams Bi-level LRM(CVR):repair due date,dozer
travel times,limited no.of dozers
Multi objective GA
2)Maximize tra?c?ow
Feng and Wang(2003)Maximize repaired road length,number of
repaired links,minimize risk of work teams LRM(CVR,multi-tour TSP):manpower,
dozer clearance capacities
Lingo-VRP package
?zdamar et al(2014)Maximize network accessibility over repair
time Recursive LRM(parallel machine scheduling
model):dozer work sequence with
clearance times and travel times
Accessibility based link group criticality
and makespan related heuristics
Tuzun Aksu and?zdamar(2014)Maximize cumulative number of open paths
over repair time Dynamic PRM(MIP):repair sequence under resource constraints
Exact
Yan and Shih(2009)Minimize maximum repair and delivery costs,
demand satisfaction equity
LRDM(DNF-TS,MIP)Exact,3step heuristic
Maya Duque and S?rensen(2011)Minimize weighted shortest paths between
pairs of regions
LRDM(binary MCNF):repair budget GRASP metaheuristic
Chen et al.(2011)Weighted relief delivery and unmet demand LRDM(CVR):non-split deliveries GA
Liberatore et al.(2012)Minimize relief delivery time,unmet demand,
maximize arc reliability
LRDM(binary MCNF):repair budget Exact,lexicographic optimization Matisziw and Murray(2009)Identify most critical p links(maximize?ow)Path based network vulnerability model
(BP):path aggregation constraints
Exact
Model types–LRM:Link based Repair Model,PRM:Path based Repair Model,LRDM:Link based Repair/Delivery Model,CVR,MCNF,DNF-TS,MIP,LP,BP de?ned as in Tables1 and2.
This operation should start immediately after the disaster strikes and complete within72hours because road blockages impede rescuers, emergency services,and lifeline support(Kobayashi,1995).In the second phase,all other debris are collected,reduced,transported, temporarily stored,recycled,and disposed.These operations could take months.Examples of the?rst and second phases of post dis-aster debris management are given by Lauritzen(1998).In the next two sections,we summarize models for infrastructure restoration and subsequent debris management.
2.2.1.Models for infrastructure restoration
Network accessibility measures.Road restoration activities involve clearing roadside debris and restoring the road network in order to open up evacuation routes and other important paths.A similar anal-ogy can be made for the restoration of other types of infrastructure such as power and?beroptic networks that share similar topolo-gies.Hence,any infrastructure repair operation can be conducted e?ciently by identifying the optimal order in which critical blocked links in the network are cleared.The goal would be to restore ma-jor paths as early as possible while maximizing network accessibility throughout the operation.Thus,network accessibility measures need to be de?ned.Chang and Nojima(2001)de?ne it as the length exten-sion in post-disaster shortest paths between all pairs of locations in a road network.Sohn(2006)enhances this measure by weighing lo-cations according to their populations and tra?c densities.A road or link criticality index calculates the impact of its blockage on network accessibility.Sohn(2006)and Jenelius and Mattsson(2012)adopt a slow sequential approach that closes one link at a time to observe its impact on the whole network.This approach does not take the combined impacts of groups of blocked links into account;hence,the restoration plans based on sequential criticality index ordering might be suboptimal.
Link and path based repair models.Link based repair models rep-resent roads as arcs in the network and path based models represent them as nodes.A path is an ordered set of nodes.Path based models are more compact than link based models and may converge faster because they explicitly consider links shared by multiple paths.These models involve path related goals leading to maximizing network ac-cessibility or minimizing network vulnerability.On the other hand, link based models involve goals such as maximizing length of restored links,maximizing network?ow or minimizing the repair operation’s completion time(makespan).
A.Link based repair models(LRM).CVR type.Chen and Tzeng(2000) propose a bi-level link based model where a due date is imposed for the completion of tasks,travel times between tasks are explic-itly considered,and repair resources are unlimited.First level goals are to minimize travel,work,and idle times of work teams.Second level goal is to maximize tra?c?ow.Chen and Tzeng(1999)use a multi-objective GA to solve this problem on a network with24nodes. Feng and Wang(2003)consider limitations on dozers,manpower, and debris clearing capacity.The goals considered are maximizing total length of accessible links,maximizing total number of restored links,and minimizing risk of work teams.The formulation involves a multi-tour TSP(each link is visited only once).The model is tested on a50node network with ten damaged links.
Parallel machine scheduling type.?zdamar,Tuzun Aksu,and Ergunes(2014)aim to maximize the cumulative network accessibil-ity measure de?ned by Chang and Nojima(2001)over the road repair duration.The researchers propose heuristics that favor both repair operation makespan and network accessibility.Dozer clearance ca-pacities,dozer travel times between tasks,and number of available dozers make up the model restrictions.
B.Link based integrated repair and relief delivery models(LRDM).DNF-TS type.Yan and Shih(2009)propose a link based DNF-TS model with work team trips and relief material?ows over repaired roads.An eq-uity constraint exists for demand satisfaction.The goal is to minimize maximum repair and relief delivery costs.A three-step heuristic is proposed where blocked links are prioritized?rst,worker schedules
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and commodity?ows are optimized next.A46node network with25 repair points is solved in900CPU seconds.
CVR type.Chen,Pe?a-Mora,and Ouyang(2011)propose a non-split delivery CVR model to distribute relief over repaired roads.GA is used to optimize a weighted objective composed of maximizing relief delivery and minimizing unmet demand.
MCNF type.A network?ow model is proposed by Liberatore, Ortu?o,Tirado,Vitoriano,and Scaparra(2012)for relief distribution over a network with broken linkages.A budget constraint is imposed on?xing links implicitly limiting the equipment and the number of work teams used in the operation.Several goals,such as delivery time,satis?ed demand,and reliability of arcs are proposed.Maya Duque and S?rensen(2011)address a similar repair problem under budget constraints using a?xed cost network?ow formulation for minimizing the cost of?ows from each rural center to the nearest regional center.
C.Path based repair models(PRM).Network vulnerability models.A path based approach is adopted by Matisziw and Murray(2009)who identify a?xed number of critical node and arc blockages that would prevent tra?c?ow the most.The model addresses both mitigation and response phase planning(to protect most critical linkages in pre-paredness phase and recover them?rst in response phase)and aims at maximizing?ows over broken source-sink paths.The most im-portant feature of this model is the introduction of path aggregation constraints that dispose of the necessity to enumerate all source-sink paths.The researchers illustrate the mechanics of the model on a network of23nodes and34arcs.
Network accessibility models.Tüzün Aksu and?zdamar(2014)pro-pose a dynamic path based model that identi?es the optimal repair order of blocked links during a three-day work shift given a limited number of equipment.The goal is to open all access paths from each location in the network as early as possible and this maximizes the cumulative network accessibility throughout the restoration opera-tion.Two real road network problems with up to400links(of which 70are broken)are solved within1hour of computation time.
2.2.2.Models for debris management
Brown,Milke,and Seville(2011)provide a wide review on post disaster waste treatment options.Different types of waste result from different disaster categories.While construction and demoli-tion waste(C&D)results from all disaster types,sediments result from hurricanes,quakes and?oods;green waste results from all but ?res;and ash results from quakes and?res.The major contribution to debris comes from C&D.In the survey,the researchers emphasize the environmental,social,economic,and legal aspects that are the concern of the second phase in debris management.In particular,the speed needed in disposal due to health hazards,the requirement of accuracy in calculating disposal costs,psychological effects of allow-ing property owners to deconstruct,and re-use C&D waste(Denhart, 2009),vermin related public hazards(Petersen,2004)are mentioned. The second phase debris management models usually concentrate on locating temporary storage and recycling facilities around the affected area as well as determine debris transportation routes.
Supply chain models.Fetter and Rakes(2012)present a facility lo-cation model for locating temporary disposal and storage reduction facilities to support debris clean-up operations.The objective is to minimize the costs of debris collection,recycling,and disposal as well as the?xed costs of enabling recycling technology at temporary facil-ities.Hu and Sheu(2013)propose a reverse logistics chain for debris disposal with four stages:on site processing and recycling,nearby temporary site processing and recycling,mass processing by perma-nent facilities,disposal and recycling,and reproduction for construc-tion products(bricks).A network model that enables material balance at each stage is developed and the goals are to minimize logistics costs,minimize risk based penalty due to delay of debris removal,and minimize psychological costs of delay.The researchers analyze the Wenchuan quake(2008China)as a case study.They observe that risk induced costs are decreased by37percent at the expense of243 percent of an increase in logistics costs.
https://www.doczj.com/doc/d99387535.html,e of information systems in humanitarian logistics
It is very di?cult,if not impossible,to implement solution proce-dures in real life without a proper information system that bundles mathematical models in a user-friendly interface.Moreover,infor-mation systems have the potential to act as a liaison between practi-tioners and academic researchers in humanitarian logistics as well as among different disaster lifecycle stages.For a literature review for the use of information systems in humanitarian logistics and future research directions,Sangiamkul and van Hillegersberg(2011)and Ortu?o et al.(2013)can be referred.Blecken and Hellingrath(2008) compare?ve practitioner software(i.e.,SUMA,LSS,HELIOS(HELIOS, 2013),LogistiX,Sahana)and conclude that critical decisions such as inventory management,supply planning,routing and scheduling are not addressed by any of them.In addition to the?ve software dis-cussed in Blecken and Hellingrath(2008),Ortu?o et al.(2013)re-view Disaster Management Information System(DMIS),FleetWave R , and Humanitarian Free and Open Source Software(HFOSS)(Tucker, Morelli,&de Lanerolle,2011)as well.Ortu?o et al.(2013)also report the humanitarian relief efforts where the software was used.Howden (2009)explains where information systems can play a role from a disaster operations management lifecycle perspective.Chib and Komathi(2009)extend the framework for technology-community-management model to assess vulnerability in Asian countries for dis-aster recovery.Here,the use of different types of information systems in humanitarian logistics is addressed for the response and recovery phases.In Table4,we provide academic studies and applications that correspond to different technologies.
3.1.Maps and geographic information systems
Similar to the scarcity of relief supplies and equipment;?eld data for the location and impact of the disaster,the availability of the trans-portation infrastructure,and estimated demand quantity are scarce resources in the aftermath of disasters.Maps and Geographic Infor-mation Systems(GIS)are vital to provide these?eld data.GIS research on disaster management,GIS applications,and potential future study topics are listed in Cutter(2003).Disaster risk maps and alerts for up-coming disasters are developed to assist relief operations.A good ex-ample is the Global Disaster Alert and Coordination System(GDACS) of United Nations and European Commission(GDACS,2014).GDACS analyses GIS data,maps,Satellite Mapping Coordination System,?eld data(e.g.,reports,photos/videos,Geo-Pictures),mass and social me-dia information and presents the estimate of the disaster impact,risk, and magnitude via a global map.GDACS is now accessible by a smart phone app called iGDACS.
Several researchers have integrated their models with maps and GIS.Chang et al.(2007)use a scenario planning approach for res-cue center location problem in conjunction with GIS software for ?ood emergencies.Saadatseresht,Mansourian,and Taleai(2009)pro-pose an evolutionary approach using GIS software for solving a spa-tial multi-objective evacuation problem.Mete and Zabinsky(2010) address prepositioning of medical supplies using a simulation and visualization environment called RimSim(Campbell,Mete,Furness, Weghorst,&Zabinsky,2008).Widener and Horner(2011)develop a model for deciding the size and location of aid distribution facilities for post-hurricane settings.Their model utilizes GIS and spatial op-timization technologies to solve p-median and capacitated median type problems.
An application of GIS based techniques for needs assessment is provided in Benini,Conley,Dittemore,and Waksman(2009).The
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Table4
Summary of the use of information systems in humanitarian logistics.
Citation Maps and/or GIS Coordination network Smart phone apps Other technologies
GDACS(2014)GIS data,map Mass and social media iGDACS Satellite,geo-pictures
Chang et al.(2007)GIS N/A N/A N/A
Saadatseresht et al.(2009)GIS N/A N/A Spatial multi-objective optimization Mete and Zabinsky(2010)N/A N/A N/A RimSim(a simulation and visualization
environment)
Widener and Horner(2011)GIS N/A N/A Spatial optimization
Benini et al.(2009)GIS algorithms UN joint logistics cluster N/A NASA satellite imagery
ACAPS(2014)Heat maps Community of practice NOMAD N/A
MapAction(2014)GIS,GMES satellite
imagery,QGIS UN assessment and
coordination team
N/A GoogleEarth,portable satellite data
modem,hand held GPS units
SandyCoworking(2013)GoogleMaps List of available spaces N/A Ushahidi platform
Humanitarian OpenStreetMap(2014)GIS Web portal Available Aerial imagery
Chen et al.(2011)ArcGIS Collaborative GIS Mobile resource requesting ability Resource tracking and management Zografos and Androutsopoulos(2008)GIS N/A N/A Database,human machine interface Jotshi et al.(2009)ArcGIS N/A N/A Data fusion,Tele-Atlas,and HAZUS Demir and?zdamar(2013)ArcGIS N/A Android app On-line user interface
Huang et al.(2013)OpenStreetMap Urban search and rescue SMS Matlab,ArcGIS,Python
Pasupathy and Madina-Borja(2008)N/A American Red Cross N/A Excel,Access,VBA,SQL
Vitoriano et al.(2009)Logistics maps Web based tool N/A Java,Access,GAMS
Tomaszewski et al.(2006)Open source GIS Geo-collaborative web
portal
PDA or tablet MapBuilder API and JetSpeed Portal ?zgüven and?zbay(2013)N/A N/A N/A RFID
Yang et al.(2011)N/A N/A N/A RFID
Assessment Capacities Project(ACAPS)is one example dedicated to better deal with the needs assessment operations in the re-sponse phase(ACAPS,2014).Software such as MapAction depicts the needs and speci?c locations around the disaster area(MapAction, 2014).Crowd sourcing is a tool that was used during the Hurricane Sandy(SandyCoworking,2013).This specialized crowd map helped businesses and individuals to move to safer places by populating information about available spots around the area.Humanitarian OpenStreetMap initiative provides key geographic data to help hu-manitarian responders.It is built by volunteers recording GPS, aerial imagery,and geographic data after a catastrophic event (Humanitarian OpenStreetMap,2014).
3.2.Integrated systems
Information systems are used in a variety of application areas such as damage and needs assessment,locating aid distribution facilities, and vehicle routing in the response and recovery phases.Unfortu-nately,not many studies can be reported that integrate available tech-nologies with proper mathematical models through several disaster lifecycle stages.
A comprehensive study for the response phase is given in Chen et al.(2011)which addresses the challenge of construction equip-ment allocation,management,and routing for urban search and res-cue operations under scarce resource disaster relief environment.The researchers combine three technologies(i.e.,GIS,resource tracking and management,and decision making by a mathematical model)in a framework with mobile resource requesting ability.Zografos and Androutsopoulos(2008)develop an integrated decision support sys-tem(DSS)for hazardous material transportation and emergency re-sponse decisions.The implemented DSS has logistics,crisis manage-ment,database,GIS,and human machine interface subsystems.
Jotshi,Gong,and Batta(2009)use data fusion to dispatch and route emergency vehicles with the aim of minimizing the response time;hence,maximizing the life expectancy.Data fusion technique is used in conjunction with ArcGIS,Tele-Atlas,and HAZUS technolo-gies.Demir and?zdamar(2013)describe a hierarchical relief delivery and casualty pickup coordination system using dynamic data trans-ferred from ArcGIS database.The system provides on-line user inter-face supporting access to the database from Android based hand de-vices.Huang et al.(2013)integrated visualization of real time crowd-sourcing data on OpenStreetMap with SMS messages for demand estimation.Huang et al.(2013)implemented a routing heuristic for urban search and rescue teams.
An example of integrated software packages applied for perfor-mance management in the American Red Cross(ARC)is explained by Pasupathy and Madina-Borja(2008).Equipped with a Data En-velopment Analysis model,this tool was implemented to measure the performances of about1000ARC chapters with respect to cri-teria such as?nancial resources,capacity creation,service delivery, and customer effectiveness.Vitoriano et al.(2009)introduce a multi-objective model with integration of Java,GAMS,and Microsoft Access to support transport operations in disaster relief.
DMIS(World Disaster Report,2013,p.123)is an integrated plat-form that provides real time data about disasters and tools for opera-tions of International Federation of Red Cross and Red Crescent(IFRC). This web based tool is only available for IFRC staff.Although not devel-oped speci?cally for humanitarian logistics,FleetWave R (FleetWave, 2014)is a web-based enterprise?eet management software used by large humanitarian organizations such as IFRC and World Food Pro-gramme(WFP)(Ortu?o et al.,2013).
3.3.Coordination networks
Coordination is one of the biggest challenges in humanitarian logistics(Bal??k,Beamon,Krejci,Muramatsu,&Ramirez,2010).Sev-eral stakeholders compete for scarce resources in a chaotic https://www.doczj.com/doc/d99387535.html,rmation systems provide a unique opportunity to over-come coordination challenges.There are speci?c tools for coordi-nation some of which were discussed in Section3.1.Bartell,Lap-penbusch,Kemp,and Haselkorn(2006)give a review about the use of information systems in effective coordination and communica-tion Tomaszewski,MacEachren,Pezanowski,Liu,and Turton(2006) present an example with a geo-collaborative web portal to address the coordination and collaboration challenges in a disaster relief environ-ment.The researchers use open source software such as MapBuilder API and JetSpeed Portal.
Open source software community took responsibility to help hu-manitarian logisticians in the?eld.Humanitarian Free and Open Source Software(HFOSS)project has been working on both en-gaging undergrad students in FOSS community as well as doing good with good computer science practices.Portable Open Source
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Information Tool(POSIT)and Collabit are two successful tools devel-oped by HFOSS project contributors(Ortu?o et al.,2013;Tucker et al., 2011).
In addition to speci?c tools,there exist non-governmental orga-nizations that aim at increasing the e?ciency of disaster response and recovery by using Information and Communication Technology at a greater scale(Aidmatrix,2014;NetHope,2013).The Aidmatrix Network R for Humanitarian Relief provides comprehensive supply chain management software including modules such as planning, procurement,transportation,warehouse,online ordering,distribu-tion,?eet management,asset management,and distribution.The In-ternational Network of Crisis Mappers(Crisis Mappers Net,2014)is a network of various stakeholders working at the crossing of disasters, novel technologies,crowd-sourcing,and crisis mapping.This network has more than7000members in over160countries,who are a?li-ated with over3000different institutions from academic researchers to actual practitioners on the?eld.Logistics Cluster approach is also a successful example in coordinating scarce logistical resources in a dis-aster area.The information?ow among humanitarian organizations is managed and up-to-date maps about the available warehouses, trucks,etc.around the disaster area are released through a logistics cluster(Tomasini&Van Wassenhove,2005).
3.4.Other technologies
Remote sensing techniques such as satellite or camera image pro-cessing are used for damage(Pesaresi,Gerhardinger,&Haag,2007) and needs assessment(Tatham,2009).An example for using an un-manned aerial vehicle system for needs assessment is illustrated in Tatham(2009).Radio Frequency Identi?cation(RFID)sensors are also investigated for their use in humanitarian logistics settings.Yang, Yang,and Yang(2011)design a sensor network with a hybrid imple-mentation of active and passive RFID tags for humanitarian logistics center management.?zgüven and?zbay(2013)discuss both off-line and on-line use of RFID information to manage inventory for human-itarian relief efforts.
From the discussions above,we observe that disaster management software cover a wide portfolio of dispersed applications;however, holistic software that integrate all phases of the disaster lifecycle are yet to be developed.Moreover,some software those seem to offer an integrated solution are not freely available to humanitarian aid workers all around the world.
4.Conclusion
According to the claims of climate scientists and the trend in disaster frequency during the recent decades,during the21st cen-tury,residents of the earth will suffer severely from the environ-mental outcomes of the20th century’s technological advances(IPCC Report,2014).Even the most optimistic views on the progress of global warming have demoralizing aspects in terms of clean water availability,temperature rise,sea level changes,droughts,?oods,wild ?res,and earthquakes.Global awareness of the forthcoming calami-ties has risen due to extensive media coverage of climate change and of the high impact disasters that took place in the last decade.Aca-demicians have duly responded by developing mathematical models and solution algorithms that deal with different aspects of disasters. Information technologies such as GIS and remote sensing entered the ?eld of disaster management along with mobile hand devices that enable access to/from people on the street.
The detailed discussions held here on the modeling aspects of the literature implicate that though preparedness plans can be zone based and rough,response and recovery plans have to include street address level of detail in order to construct routes for relief delivery, repair equipment delivery,detailed itineraries for rescuers,etc.Unfor-tunately,algorithms for commercial routing problems do not satisfy the needs of humanitarian logistics;therefore,methods that deal with large scale disasters are not readily available.Though the proposed logistics planning models have good practical features,most of the models’computational burdens might prevent them from being used in actual disasters.From practical and academic perspectives,on-line algorithms that aim at both service quality and optimal resource uti-lization would be most welcome in high impact disasters.
Another area of improvement would be developing more solv-able models that treat all aspects of recovery in an integrated manner including debris transportation,road repair,relief delivery,and evac-uation during the?rst phase of recovery.Although integrated models exist,the models are either oversimpli?ed or too complex and un-solvable.Proposed metaheuristics are not yet tested on realistic size networks.
As discussed in Section3,the developed enabling technologies that should make solution methods available to aid practitioners are quite dispersed in their functionality and accessibility.Although there have been several attempts in recent years to integrate some type of in-formation systems into the humanitarian logistics models,the use of technology is still not prevalent in humanitarian logistics.Practitioner software lack sophisticated mathematical models,hence optimal re-sults,but have nice graphical user interfaces and fast solutions.On the other hand,few academic researchers attempt to integrate their mathematical models into a decision support system equipped with GIS,maps,real-time data,and a user-friendly interface.Although it is not very easy to combine the best values of practitioners and academic researchers,the use of technology is a must in order to achieve success in humanitarian logistics operations.A holistic approach such as web-based Enterprise Resource Planning(ERP)systems is yet to be seen in this area.The commercial versions of such systems are readily avail-able but they need to be adapted to disaster context.Unfortunately, most of the disaster response costs are born by governments and private damage costs by insurance companies.Unless disasters use up country GDPs more and more,government funding will probably not be made available to build a disaster-ERP,i.e.,Disaster Resource Planning(DRP)software.The?rst step is to calculate governmental disaster costs correctly and conduct a gap analysis to validate build-ing the DRP.In order to optimize response phase resource utilization, the DRP software and database should be globally accessible.Inter-national cooperation should be promoted to design a global disaster supply chain managed by the DRP,setting political differences aside. The challenges that lie ahead of a successful implementation is lack of real-time data,fragmented nature of humanitarian organizations, lack of commonly accepted interoperability standards among differ-ent humanitarian organizations,and not realizing the importance to balance good bookkeeping practices and to act fast in emergency re-lief operations.These barriers are rather concerned with the human way of doing things than with the lack of available technologies.We believe that they can be overcome with adequate determination.
Acknowledgments
This work was partially supported by The Scienti?c and Tech-nological Research Council of Turkey(TUBITAK)under grant num-bers110M578and113M493.The authors would also like to thank the anonymous referees for their helpful comments and suggested improvements.
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光的折射与色散 本节知识点： 1. 折射：光从一种介质 斜射 入另一种介质时，传播方向发生偏折，这种现象叫做光的折射 ● 当发生折射现象时，一定也发生了反射现象。 ● 当光线 垂直 射向两种物质的界面时，传播方向 不发生改变。 2. 光的折射规律： （1）在折射现象中，折射光线、入射光线和法线都在同一个平面内； （2）光从空气斜射入水中或其他介质中时，折射光线向法线方向偏折（折射角＜入射角）； （3）光从水或其他介质中斜射入空气中时，折射光线向界面方向偏折（折射角＞入射角）。 （4）光从空气垂直射入（或其他介质射出），折射角=入射角= 0 度。 ● 在折射现象中，光路是可逆的。 ● 在光的折射现象中，入射角增大，折射角也随之增大。 ● 在光的折射现象中，介质的密度越小，光速越大，与法线形成的角越大。 3. 折射的现象： ① 从岸上向水中看，水好像很浅，沿着看见鱼的方向叉，却叉不到；从水中看岸上的东 西，好像变高了。 ② 筷子在水中好像“折”了。 ③ 海市蜃楼 ④ 彩虹 4. 从岸边看水中鱼N 的光路图（图2-10）： 图2-10 N N' 空气 水 N N' 空气 水 N 水 空气 O 水 空气 O N 图2-9 入射角 折射角 折射角 入射角
● 图中的N 点是鱼所在的真正位置，N'点是我们看到的鱼，从图中可以得知，我们看到的鱼比实 际位置高。 ● 像点就是两条折射光线的反向延长线的交点。 ● 在完成折射的光路图时可画一条垂直于介质交界面的光线，便于绘制。 练习：☆池水看起来比实际的 浅 是因为光从 水中斜射向 空气中时发生折射，折射角大于入射角。 ☆蓝天白云在湖中形成倒影，水中鱼儿在“云中”自由穿行。这里我们看到的水中的白云是由 光的反射 而形成的 虚像 ，看到的鱼儿是由是由光的折射而形成的 虚像 。 5. 光的色散：光的色散属于折射现象。 ● 1666年， 英国 物理学家 牛顿 用 三棱镜 使太阳光发生了 色散（图2-11）。 ● 太阳光通过棱镜后，被分解成各种颜色的光，用一个白屏来承接，在白屏上就形成一条颜色依 次是 的彩带。 ● 牛顿的实验说明 白光是由各种色光混合而成 。 6.色光的三原色： 红 、 绿 、 蓝 。 红、绿、蓝三种色光，按不同比例混合，可以产生各种颜色的光。（图2-12） 光的色散 色光的三原色 颜料的三原色 7.物体的颜色： ● 透明物体的颜色由 透过它的色光来决定 来决定。 如图2-13，如果在白屏前放置一块红色玻璃，则白屏上其他颜色的光消失，只留下红色。这表明，其他色光都被红色玻璃吸收了，只有红光能够透过。 ● 不透明物体的颜色由 他反射的色光来决定。 如图2-13，如果把一张绿纸贴在白屏上，则在绿纸上看不到彩色光带，只有被绿光照射的地方是亮的（反射绿光），其他地方是暗的（不反射光）。 红 紫 图2-12 图2-11
房地产公司工程管理部职责[120166] 房地产公司工程管理部职责 一、工程管理部的职能： 1.工程管理程部做为公司负责工程技术管理的职能部门，负责公司房地产开发项目的工程项目管理; 2.参与项目策划、规划设计等前期工作，配合公司有关部门做好招投标、预决算等工作; 3.负责工程进度的管理，使工程项目在计划工期内完成; 4.按照公司制定的”精品”战略，进行高质量的工程管理，创建精品项目; 5.采用科学、合理和先进的管理方法，控制建造成本，降低工程造价。 二、工程管理部职责 1.建立健全工程管理制度，制定符合公司发展的工程项目管理模式和业务流程; 2.在施工图设计阶段，负责与设计院进行本专业具体的业务联系，确保结构专业施工图质量满足公司和项目的要求; 3.在施工阶段，负责同设计院就设计图纸、设计变更等内容，进行具体的业务联系，提高解决图纸问题和现场问题的效率，确保工程的正常施工; 4.跟踪工程项目的前期工作，负责开工前的现场准备工作，搞好现场三通一平; 5.负责施工图审查交底和图纸会审工作; 6.制定工程项目施工组织设计审查要点，负责施工组织设计和有关技术方案的审查; 7.根据项目特点，确定工程项目管理模式，落实安排项目管理人员; 8.定期对工程的进展进行检查督促，解决存在的问题; 9.定期对部门内部、监理单位、施工单位的工作质量进行检查; 10.按照公司绩效考核管理办法，对员工进行绩效考核; 11.配合监理单位对现场管理、施工安全、质量、进度等进行检查，主持或参与对工程事故进行调查处理;
12.对设计变更、修改、现场施工签证进行技术审查，按公司和部门的管理制度履行相应职责; 13.配合公司其他部门有关文件的研讨，提出本部门的建议和意见; 14.配合物料供应部对到场甲供甲控材料设备的接收和使用进行监控; 15.负责公司工程技术资料的管理，并作为公司档案管理的补充; 16.负责现场资料的收集，为索赔和反索赔管理工作提供事实依据; 17.为成本控制部的决算审查工作提供现场情况说明; 18.收集整理工程有关资料，参加公司招投标工作，并为其提供工程方面的建议; 19.组织项目竣工验收和移交，配合物业管理公司的售后服务; 20.有计划有步骤地对部门员工进行岗位培训，提高其综合业务素质和项目管理水平; 21.对工程项目部的工作进行检查、督促，以保证工程计划的完成; 22.根据公司合同管理办法，参与工程合同的审查，对有关工程技术条款提出建议和意见; 23.配合策划销售部，对现场售楼处和样板房的施工进行管理。 感谢您的阅读！
学生姓名 年级 八年级 授课时间 教师姓名 课时 2 课 题 同步学案：4.3光的折射和光的色散 教学目标 一、 知识与技能 1.知道光的折射规律； 2.知道一些常见的折射现象； 3.会根据要求作出有关于折射的光路图； 4.知道光的色散； 5.知道色光的三原色； 6.知道红外线以及紫外线的一些常见的应用 二、 过程与方法 1.通过操作、观察探究光折射时的特点的实验，逐步掌握分析、总结实验现象的一 般步骤 三、 情感、态度与价值观 1. 通过操作、观察探究光折射时的特点的实验，从中认识到一般的物理规律都是通过实验所得的 2. 通过学习光的折射以及光的色散，认识到很多的物理现象都是源于日常生活 重 点 1.光的折射规律；2.有关光折射的作图 难 点 1.光从空气中斜射入水中，与光从水中斜射入空气中的区别； 2.光通过三棱镜时的光路图 一、 光的折射 1.光的折射规律：光从空气中斜射入水中或其他介质中时，折射光线向法线偏折，折射角小于入射角。当入射角增大时，折射角也增大。当光从空气垂直射入水中或其他介质时，传播方向不变。 2.在折射现象中，光路是可逆的。 3.光的折射现象：①池水变浅、②水中的筷子会“折断”、③海市蜃楼等。 4.折射的光路图： 二、 光的色散 知识梳理 空气 O N 水 入射光线 折射光线 折射光线 水 空气 N O i r 入射角 折射角 入射光线 水 r N O i 入射角 折射角 空气 入射光线 折射光线
1. 太阳光通过棱镜后被分解成各种颜色的光的现象就是光的色散。 2. 白光通过棱镜能够分解成红、橙、黄、绿、蓝、靛、紫。这说明，白光由各种色光混合而成的。 3. 色光的三原色：把红、绿、蓝三种色光按不同比例混合后，可以产生各种颜色的光，因此把红、绿、蓝叫做色光的三原色。 4. 看不见的光：在太阳的可见光谱当中，在红光之外是红外线，在紫光之外是紫外线，用人眼都看不见的。 5. 红外线的应用：①红外胶片、②红外线夜视仪、③电视机遥控器等；紫外线的应用：①验钞机、②紫外线灯（杀菌消毒）、③促进骨骼生长和身体健康等。 知识点一：折射现象 1.光的折射规律：光从空气中斜射入_______或__________时，_______光线向法线_______，折射角______入射角。当入射角增大时，折射角也______。当光从空气______射入水中或其他介质时，传播方向______。 2.在折射现象中，光路是_________。 3.光的折射现象：①_____________、②________________、③_____________等。 例题组1： 【例1】如图所示的四种现象中，属于光的折射现象的是（ ） 【例2】光线射到平行的厚玻璃板上，在它的上、下表面发生折射，在下图中正确的是（ ） A.甲 B.乙 C.丙 D.丁 【例3】一盏探照灯装在东面看台上，灯光朝西斜向下投射到有水的游泳池底的中央，在将水逐渐灌满的过程中，池底的光斑将（ ） 甲 乙 丁 丙
案例三 ARIMA 模型的建立 一、实验目的 了解ARIMA 模型的特点和建模过程，了解AR ，MA 和ARIMA 模型三者之间的区别与联系，掌握如何利用自相关系数和偏自相关系数对ARIMA 模型进行识别，利用最小二乘法等方法对ARIMA 模型进行估计，利用信息准则对估计的ARIMA 模型进行诊断，以及如何利用ARIMA 模型进行预测。掌握在实证研究如何运用Eviews 软件进行ARIMA 模型的识别、诊断、估计和预测。 二、基本概念 所谓ARIMA 模型，是指将非平稳时间序列转化为平稳时间序列，然后将平稳的时间序列建立ARMA 模型。ARIMA 模型根据原序列是否平稳以及回归中所含部分的不同，包括移动平均过程（MA ）、自回归过程（AR ）、自回归移动平均过程（ARMA ）以及ARIMA 过程。 在ARIMA 模型的识别过程中，我们主要用到两个工具：自相关函数ACF ，偏自相关函数PACF 以及它们各自的相关图。对于一个序列{}t X 而言，它的第j 阶自相关系数j ρ为它的j 阶自协方差除以方差，即j ρ＝j 0γγ ，它是关于滞后期j 的函数，因此我们也称之为自相关函数，通常记ACF(j )。偏自相关函数PACF(j )度量了消除中间滞后项影响后两滞后变量之间的相关关系。 三、实验内容及要求 1、实验内容： （1）根据时序图的形状，采用相应的方法把非平稳序列平稳化； （2）对经过平稳化后的1950年到2007年中国进出口贸易总额数据运用经典B-J 方法论建立合适的ARIMA （,,p d q ）模型，并能够利用此模型进行进出口贸易总额的预测。 2、实验要求： （1）深刻理解非平稳时间序列的概念和ARIMA 模型的建模思想； （2）如何通过观察自相关，偏自相关系数及其图形，利用最小二乘法，以及信息准则建立合适的ARIMA 模型；如何利用ARIMA 模型进行预测； （3）熟练掌握相关Eviews 操作，读懂模型参数估计结果。 四、实验指导 1、模型识别 （1）数据录入 打开Eviews 软件，选择“File”菜单中的“New --Workfile”选项，在“Workfile structure type ”栏选择“Dated –regular frequency ”，在“Date specification ”栏中分别选择“Annual ”(年数据) ，分别在起始年输入1950，终止年输入2007，点击ok ，见图3-1，这样就建立了一个工作文件。点击File/Import ，找到相应的Excel 数据集，导入即可。
光的折射和色散 一、光的折射 1、定义：光从一种介质斜射入另一种介质时，传播方向发生偏折的现象。 注：光在同种介质中传播，当介质不均匀时，光的传播方向亦会发生变化。 二、的折射规律： 折射角：折射光线和法线间的夹角 定律：（1）光折射中，折射光线、入射光线、法线光线在同一平面内，法线居中。 （2）折射角随入射角的增大而增大； （3）光从空气斜射入水或其他介质时，折射光线向法线方向偏折即入射角大于折射角；光从水或其它介质斜射入空气中时，折射光线远离法线及入射角小于折射角。 （4）斜射时，总是空气中的角大；垂直入射时，折射角和入射角都等于0°,光的传播方向不改变 （5）当光射到两介质的分界面时，反射、折射同时发生 （6）光的折射中光路可逆。 三、用光的折射规律解释折射现象 （1）水中的鱼的位置看起来比实际位置高一些（鱼实际在看到位置的后下方） （2）由于光的折射，池水看起来比实际的浅一些；水中的人看岸上的景物的位置比实际位置高些；夏天看到天上的星斗的位置比星斗实际位置高些；透过厚玻璃看钢笔，笔杆好像错位了；斜放在水中的筷子好像向上弯折了。人们利用光的折射看见水中物体的像是虚像（折射光线反向延长线的交点） 四、光的色散 1、定义：太阳光通过三棱镜后，依次被分解成红、橙、黄、绿、蓝、靛、紫七种颜色，这种现象叫光的色散。 注：白光是由各种色光混合而成的复色光。 2、色光的三基色 光的三基色：红、绿、蓝 注：其它色光可由这三种色光混合而成；世界上没有黑光； 颜料的三原色：红、黄、蓝 3、物体的颜色 透明物体的颜色由它透过的色光决定（什么颜色透过什么颜色的光）；不透明体的颜色由它反射的色光决定（什么颜色反射什么颜色的光，吸收其它颜色的光，白色物体发射所有颜色的光，黑色吸收所有颜色的光） 基础巩固 1．关于光的折射，下列说法正确的是（） A．光从一种介质进入另一种介质时，可能发生折射 B．只有光斜射到两种不同介质的界面时才可能发生折射 C．光从空气射向水，折射光路是可逆的，光从水射向空气，折射光路是不可逆的 D．在发生光的折射时一定发生反射，反射角可能大于入射角 2．一束光从空气斜射水中时，当入射角增大时，则（） A．反射角和折射角都增大，折射角大于入射角 B．反射角和折射角都增大，折射角小于入射角 C．反射角不变，折射角增大，折射角小于入射角 D．反射角不变，折射角增大，折射角大于入射角
工程部管理制度 （一） 工程部工作原则： 求真务实、精干高效的原则 （二） 工程部人员分工： 1、工程部经理：主持工程部全面工作。主要负责工程部全面工作和对各专业工程师有关工程质量、进度、安全、文明施工、成本控制等管理工作进行领导、监督、检查。 2、部门经理白珂：协助总工工作。主要负责招投标工作以及工程的整个工程质量、进度、安全、文明施工、成本控制等工作以及做好监理公司、各总分包施工单位之间的领导协调工作等。 3、安装工程师（空缺）：协助部门经理做好整个施工现场的管理工作，主要负责所有安装工程的质量、进度、安全、文明施工、成本控制等工作。 4、技术员、安全员王苏霏：协助工程部经理搞好招投标工作，工程管理往来函件的收发记录,工程、安全资料的收集整理和管理的有关资料等，零星工程造价的初审。 根据公司的总体要求和工程部目前的工作任务量，在目前仅有一个工程项目的情况下，工程部的编制为3人 （三） 工程部部门职责： 1、 配合总经理、办公室、销售部等部门进行项目前期运作。 2、 工程部全面负责工程开工前的准备及审查工作。 3、 负责工程项目招投标工作。 4、 协调施工总包、监理、设计及相关单位之间的关系。 5、 负责对总包、监理的管理工作。 6、 负责工程施工过程中质量、进度、安全、文明施工、投资成本控制等管理。 7、 负责工程竣工验收及移交工作。 8、 对工程管理过程中的文件、资料进行管理。 9、上级领导交办的其他事情。 （二）工程部经理岗位职责： 1、 负责工程部的日常管理工作。 2、 配合总经理、办公室、销售部等部门进行项目前期运作，提出合理化建议。 3、 负责组织工程的招投标工作。 （1） 对总包、监理单位进行考察、评价。 （2） 组织编制招投标文件，选择投标单位或进行邀标。
工程部管理制度及措施 一、工程部的组织机构设置 （一）工程部组织机构的设置原则： 1、精干高效的原则 2、灵活弹性的原则 （二）工程部组织机构设置 1、工程部的编制 根据公司的总体要求和工程部目前的工作任务量，工程部的编制为４人。随着工程任务量的增加和其他工程项目的开展，工程部的编制会进行调整。根据开展项目的情况每个项目增加4人。 2、工程部的岗位设置 工程部设置工程部经理1个，项目管理负责人１个；土建装修工程、预算员、资料管理员岗位１个；水、电、暖工程岗位各１个。
以后每增加一个项目相应增加四个岗位：项目管理负责人一个，各专业管理人员各一个。 项目竣工移交后工程部项目管理岗位随之撤消，根据工程部实际情况和项目人员的表现情况进行工程部岗位重新调 整安排。 二、工程部部门及岗位职责 （一）工程部部门职责： 1、配合企划部、前期部、销售部等部门进行项目前期运作。 2、工程部全面负责工程开工后的准备及审查工作。 3、负责工程项目和监理单位招投标工作。 4、协调承包商、监理、设计及相关单位之间的关系。 5、负责对承包商、监理、设计单位的管理工作。 6、负责工程施工过程中质量、进度、现场及投资的控制管理。 7、负责工程竣工验收及移交工作。 8、对工程管理过程中的文件、资料进行管理。 （二）工程部经理岗位职责： 1、负责工程部的日常管理工作。
2、配合前期部、策划部、销售部等部门进行项目前期运作，提出合理化建议。 3、负责组织工程的招投标工作。 （1）对承包商、监理单位进行考察、评价。 （2）组织编制招投标文件，选择投标单位或进行邀标。（3）组织投标单位进行现场踏勘和答疑。 （4）组织评标和开标工作，确定中标单位。 （5）参与合同谈判与合同的签定。 4、负责项目管理。 （1）负责项目的人员管理。包括人员的调配、考核、奖惩等方面的管理。 （2）项目的目标管理：对项目的整体目标进行明确下达，并将目标进行分解，做到责任到位，并对目标完成情况进行监督检查和调整。 （3）对项目施工准备、施工进度、质量、现场管理、投资控制进行审核、监督检查。 （4）对施工过程中出现的重大问题进行决策和处理。（5）负责审核施工材料的选用和对材料供应商的评价。
实验26 棱镜的色散关系 折射率是描述透明介质的重要光学常数。折射率与介质的分子结构、密度、温度、浓度等有关，也与光的波长有关，折射率是波长的函数。 测量介质折射率的方法很多，我们已学过用最小偏向角法测棱镜折射率，本实验介绍在任意入射角下测量棱镜折射率的方法。 实验目的 1．学会用自准法调整分光计，测量三棱镜顶角； 2．学会在任意入射角下测定棱镜材料的折射率； 3．了解棱镜的色散关系。 实验仪器 分光计、三棱镜、汞灯 实验原理 1．色散关系 光与物质相互作用的一个表现是，介质中的光速与波长有关，即折射率与波长有关。这种现象叫做色散。牛顿（I.Newton ）发现了光的色散现象。他令一束近乎平行的白光通过玻璃棱镜，在棱镜后的屏上得到一条彩色光带。光的色散表明，不同颜色（波长）光的折射率不同。即折射率n 是波长的函数 )(λf n = 为表征介质折射率随波长变化的程度，我们引进色散率ν，它在数值上等于介质对于波长差为1单位的两光的折射率之差，即 2121n n n νλλλ -?==-? （1） 或 d d ()d d n f λν λλ == （2） 表示折射率n 与波长关系的色散曲线， 首先是从实验上获得的。早期，对常用的 介质进行测量，发现它们的色散曲线十分 相似，如图1所示。波长增加时，折射率 和色散率都减小，这样的色散称为正常色 散。所有不带颜色的透明介质，在可见光 区域内，都表现为正常色散，即紫光折射率比红光大些。可以猜想，色散曲线显示出某种具有普遍意义的规律。[1] 描述正常色散的公式是柯西（A.L.Cauchy ）于1836年提出的： 24B C n A λλ=++ （3） 这是一个经验公式。式中A 、B 和C 是由所研究的介质材料的特性决定的常数，叫做 图1 色散曲线
光的折射和色散 1. 如图所示，是光在空气和玻璃两种介质中传播的情形，下列说法 正确的是（） A．入射角等于30°B．折射角等于50° C．NN′是界面D．MM′的右边是玻璃 2．如图所示的四幅图片中，其中一幅所反映的光学原理与其它三幅 不同的是( ) A．瞄准鱼下方叉鱼 B．放大镜 C．湖中的倒影 D．水碗中的筷子 3.下列现象中属于光的折射现象的是( ) A．小孔成像B．岸边的树木在水中的倒影 C．海市蜃楼D．我们能从不同方向看到银幕上的像 4.由于光的折射而产生的现象是( ) A．路灯下人的影子B．白云在湖中的倒影C．黑板反光，看不清粉笔字 D．水中物体的位置看起来比实际的位置高一些 5.（11绵阳）以下现象，反映了光的色散现象的是( ) A.雨后天空，弧状光带 B.岸边树木，水中倒立 C.水中铅笔，水面折断 D.井底之蛙，所见甚小 6.古人在夕阳西下的时候吟出“柳絮飞来片片红”的诗句．洁白的柳絮这时看上去却是红色的，这 是因为柳絮( ) A．发出红光B．发出红外线C．反射夕阳的红光 D．折射夕阳的红光 7.下列现象属于光的色散的是( ) A.“海市蜃楼” B.雨后的天空出现彩虹 C.“水中倒影” D.日食的形成 8.电视机的遥控器用红外线来传递信息，实现对电视机的控制，如图 所示，不把遥控器对准电视机的控制窗口，按一下按纽，有时也可以 控制电视机这是利用了（） A．光的直线传播 B．光的反射 C．光的折射 D．光的色散 9.某校新建成一个喷水池，在池底中央安装了一只射灯。射灯发出的一束光 照在右边的池壁上，当池内无水时，站在池旁左边的人，看到在S点形成一 个亮斑，如图5所示，现往池内灌水，水面升至a位置时，人看到亮斑的位 置在P点；如果水面升至b位置时，人看到亮斑的位置在Q点，则 ( ) A．P点在S点的下方，Q 点在S点的上方B．P点在S点的上方，Q 点在S点的下方 C．P点在S点的上方，Q 点在S点的上方D．P点在S点的下方，Q 点在S点的下方 10.如图，让一束太阳光照射三棱镜，射出的光射到竖直放置的白屏上。以下说法正确的是：( ) A．如果在白屏与棱镜之间竖直放一块红色玻璃，则白屏上其他颜色的光消失，只留下红色 B．如果在白屏与棱镜之间竖直放一块蓝色玻璃，则白屏上蓝色光 消失，留下其他颜色的光 C．如果把一张红纸贴在白屏上，则在红纸上看到彩色光带 D．如果把一张绿纸贴在白屏上，则在绿纸上看到除绿光外的其他 颜色的光 11.以下各种单色光中，属于三原色光之一的是（） A. 红光 B. 橙光 C. 黄光 D. 紫光 二、填空题 1. 香城泉都，万国咸宁，缓缓穿城而过的温泉河是一道靓丽的风景线。 各式现代建筑依河矗立，充满时代气息，如图9所示。建筑物在河中的 “倒影”是由光的__ __所形成的虚像，这些“倒影”看起来比建筑 物本身“暗”一些，主要是因为有一部分光发生__ __进入了水中。 2.一束光从空气斜射到某液面上发生反射和折射，入射光线与液面成 30°角(如图)，反射光线与折射光线的夹角为83°，则反射角的大小 为_______，折射角的大小为_________。 3.阳春三月，漫步于蝴蝶泉湖畔，你会看到“蝶在水中飞，鱼在云中游” 的美景，“蝶在水中飞”中的“蝶”是由光的形成的，“鱼在云 中游”中的“鱼”是由光的形成的 4.同学们在中考考室力，能从不同方向考到监考老师在黑板上所写的 “本堂考室科目：XX、考室时间：XX”等提示语，这是因为光发生了 反射的缘故。把一支铅笔斜插入盛水的玻璃杯里，看上去铅笔好像在水面上折断了，这种现象是由 于光的现象引起的。 5.茂名是美丽的海滨城市，到处都有天然湖或人造湖，湖中映着岸边树木的倒影是由于光的 ________所形成，湖岸边绽放有各种颜色的花朵，其中黄花________ (选填“吸收”或“反射”) 黄色光，湖边水中浸没的石块，看起来比实际位置浅了，这是由于光的________缘故。 6、如果一个物体能反射所有色光，则该物体呈现色；如果一个物体能吸收所有色光， 则该物体呈现色。 7．现在的多媒体教室大都安装了表面粗糙的高清玻纤白幕布，这样可以利用光的使教 室里各座位上的同学都能看到画面，而且这种白幕布能反射的光，使同学 们能看到色彩正常的画面。 8.一束太阳光通过三棱镜折射后，被分解成七种颜色的光，在白色光屏上形成一条七彩光带，如图 所示，这个现象叫光的．如果将白色光屏换成红色光屏，我们将（选填 “能”或“不能”）看到七彩光带． 9. 将太阳光通过三棱镜后，用一个白屏来承接，在白屏上形成一条彩色的光带， 这个现象称为光的＿＿＿＿；用白屏来承接是因为它能＿＿＿所有色光． （12大连）17.阳光下看到一朵花是红色的，是因为这朵花反射光；透 过蓝色的玻璃看这朵花，则这朵花呈现色。
房地产公司工程管理部职责1 一、工程管理部的职能：1?工程管理程部做为公司负责工程技术管理的职能部门，负责公司房地产开发项目的工程项目管理； 2?参与项目策划、规划设计等前期工作，配合公司有关部门做好招投标、预决算等工作； 3. 负责工程进度的管理，使工程项目在计划工期内完成；4?按照公司制定的”精品”战略， 进行高质量的工程管理，创建精品项目；5?采用科学、合理和先进的管理方法，控制建造成本，降低工程造价。 二、工程管理部职责 I ?建立健全工程管理制度，制定符合公司发展的工程项目管理模式和业务流程；2?在施工图 设计阶段，负责与设计院进行本专业具体的业务联系，确保结构专业施工图质量满足公司和项目的要求； 3?在施工阶段，负责同设计院就设计图纸、设计变更等内容，进行具体的业务联系，提高解决图纸问题和现场问题的效率，确保工程的正常施工； 4?跟踪工程项目的前期工作，负责开工前的现场准备工作，搞好现场三通一平； 5. 负责施工图审查交底和图纸会审工作； 6. 制定工程项目施工组织设计审查要点，负责施工组织设计和有关技术方案的审查； 7 ?根据项目特点，确定工程项目管理模式，落实安排项目管理人员；& 定期对工程的进展进 行检查督促，解决存在的问题； 9. 定期对部门内部、监理单位、施工单位的工作质量进行检查； 10 ?按照公司绩效考核管理办法，对员工进行绩效考核； II ?配合监理单位对现场管理、施工安全、质量、进度等进行检查，主持或参与对工程事故进行调查处理； 12. 对设计变更、修改、现场施工签证进行技术审查，按公司和部门的管理制度履行相应职责； 13. 配合公司其他部门有关文件的 研讨，提出本部门的建议和意见； 14. 配合物料供应部对到场甲供甲控材料设备的接收和使用进行监控； 15 ?负责公司工程技术资料的管理，并作为公司档案管理的补充； 16. 负责现场资料的收集，为索赔和反索赔管理工作提供事实依据； 17. 为成本控制部的决算审查工作提供现场情况说明； 18 ?收集整理工程有关资料，参加公司招投标工作，并为其提供工程方面的建议；
光的色散与折射复习题 一、选择题 1、如下图所示，一束光线斜射入容器中，在P处形成一光斑，在向容器里逐渐加满水的过 程中光斑将 A．向左移动后静止 B．向右移动后静止 C．先向左移动再向右移动 D．仍在原来位置 2、下列现象中属于光的折射现象的是 A．路灯下、地面上出现人的影子 B．水池底看起来比实际浅 C．平静的水面映出桥的“倒影” D．白天、浓密的树荫下出现很多圆形光斑 3、观赏水面风景照片时，总发现景物的“倒影”比本身暗一些，这是由于 A．“倒影”比真实景物清晰度差 B．入射水面的光线有一部分折射入水中 C．光线被反射掉一部分 D．冲洗照片的质量有问题 4、秋高气爽的夜晚，当我们仰望天空时会觉得星光在闪烁不定，这主要是因为 A、星光在运动 B、地球在绕太阳公转; C、地球自转; D、大气的密度分布不稳定，星光经过大气层后，折射的方向随大气密度的变化而变化. 5、如图1所示，人眼在A处看见河里B处有一条鱼，若从A处射出一束激光能照射到鱼的身上，则激光应对哪一点射出（） A.B上方 B. B点 C. B下方 D. 左方或右方 6、蓝天上飘着白云，平静清澈的池塘中鱼自由游动。人向池塘中看去，好像鱼在白云中游动，关于人看到的鱼和白云，下列说法正确的是( ) A．鱼是实物，白云是光反射形成的虚像 B．鱼是实物，白云是光折射形成的虚像 C．鱼是光反射形成的虚像，白云是光折射形成的虚像D．鱼是光折射形成的虚像，白云是光反射形成的虚像 7、在图中，画出了光线射在空气与玻璃的界面处发生反射和折射的四幅光路图，其中正确的是
8、如图所示，是我们看到的筷子斜插入水中的情况，其中正确的是（） 9、潜入游泳池水中的运动员仰头看体育馆墙壁上的灯，他看到的灯的位置（） A.比实际位置高 B.比实际位置低 C.与实际位置一样高 D.条件不足无法判断 10、下列成语所反映的情景中，属于光的折射现象的是: A．镜花水月; B．坐井观天; C．海市蜃楼; D．立竿见影. 11、如图所示，平静的水面上有一座石桥，桥孔看上去是圆形的．当水面上升时，看到桥孔的形状是下图中的 12、光线从水中射入空气，反射光线O(A)与水面之间的夹角为60°。关于入射角α、折射光线与水面之间的夹角的说法正确的是( ) (A) α=30°,β<60°(B)α=60°,β>60° (C) α=30°,β>60°(D)α=60°，β<60° 13、我们看到大海平静时，海水呈蓝色，其主要原因是: A.海水里含有盐分; B．蔚蓝色天空照映的结果; C．太阳光经漫反射产生的效果 D．日光中其他色光多被海水吸收，人眼看到的主要是海水反射的蓝光. 14、在太阳光下我们能看到鲜艳的黄色的花是因为： A、花能发出黄色的光 B、花能反射太阳光中的黄色光
工程部管理办法 第一部分岗位职责 工程部经理（副经理）岗位职责 一、服从公司主管工程副总的工作安排，组织工程项目部各专业工程师开展各项管理工作。 二、配合公司其它部门进行项目前期运作，提出合理化建议。 三、参与和协助工程的招投标工作，参与公司对承包商、监理单位进行考察、评价；组织投标单位对现场进行踏勘和答疑；参与公司规定X围内工程的招标、评标和开标工作。 四、协调工程各相关责任单位的工作开展。 五、协调项目业主与工程参与单位工作，将业主意见及时传递到设计、监理、施工及设备供应商，同时应确保工程的功能性满足业主的项目投资要求，根据项目业主的要求，有针对性的对工程实施管理。 六、负责工程监理的管理，审核《项目监理规划》《监理实施细则》，根据监理聘用合同对监理单位的工作进行监督检查和考核，权限X围内及时解决监理单位提出的工程实施与工程管理过程中的重要问题，协助对监理费用控制与结算。 七、协助并参与公司对设计单位的选择，参与公司对设计方案的选择工作，组织工程部各专业工程师进行图纸预审，组织设计及时对施工中各方提出的变更要求进行审查控制。 八、组织开展整个施工过程中的日常工作： a)制定工程质量控制计划、质量控制目标、安全生产目标、文明施工目标及成本控制目标； b)组织图纸预审及会审，审核施工组织设计及重大施工专项方案； c)组织协调定位、验线、地勘、文勘、三通一平等开工前准备工作；
d)组织各相关责任主体召开施工协调会议，及时协调解决施工过程中出现的各种问题，确保施工计划的正常进行； e)审核承包商进度报表，给出工程进度款支付意见； f)深入施工现场，监督专业工程师做好施工节点记录和工程质量检查，严格按照设计图纸、各项技术规X标准施工、做好工程验收、评定工作； g)依照工作程序做好与相关部门的横向联系，积极接受上级及相关部门的监督检查； h)协调部门内部各专业工程师的各项工作，平衡各岗位工作强度； i)组织项目部内部会议，参加公司相关会议，将上级指示及时准确传达并贯彻实施； j)审核项目工程的档案资料，组织竣工验收，及时配合报送质检站验收； k)组织已竣工工程和物业公司的交接工作； l)认真完成主管领导交办的临时工作，并将结果及时反馈； 专业工程师岗位职责 一、服从工程部经理、副经理的各项工作安排； 二、做好工程开工前的准备工作，熟悉掌握施工图和设计意图、施工工艺和操作规程、工程质量验收标准及有关的法规和条例； 三、参与图纸会审、设计交底工作，负责交底记录整理、签认和发放，跟踪处理图纸会审中提出的问题； 四、对监理单位的各项管理资料进行审查，并提出审查意见；对监理的管理工作执行情况进行监督，并及时反馈相关信息到工程部主管经理处； 五、对施工总承包单位的施工组织设计及各专项方案进行审查，对重大技术措施、经济方案、安全措施、新技术、新材料、新工艺的应用进行重点审查，提出审查意见。监督施工方案的执行情况，并形成文字记录；
光的折射与色散 --by无敌好看的仙女老师 一、光的折射 1、光从一种介质斜射入另一种介质时，传播方向发生偏折。 2、光在同种不均匀的介质中传播时，光的传播方向也会发生偏折。（海市蜃楼） 3、折射角：折射光线和法线间的夹角。（不是与水平面的夹角！！！） 二、光的折射定律 1、在光的折射中，①三线共面；②两线分居（空气远--空气中的光线远离法线）； ③两角不等（角气大--空气中的角更大） 2、垂直入射时，折射角和入射角都等于0°,光的传播方向不改变 3、折射角随入射角的增大而增大 4、当光射到两介质的分界面时，反射、折射同时发生 5、光的折射现象及其应用 （1）水中的鱼的位置看起来比实际位置高一些（鱼实际在看到位置的后下方）； （2）由于光的折射，池水看起来比实际的浅一些； （3）水中的人看岸上的景物的位置比实际位置高些； （4）透过厚玻璃看钢笔，笔杆好像错位了； （5）斜放在水中的筷子好像向上弯折了；（要求会作光路图） （6）生活中的折射现象：日出、海市蜃楼、星星眨眼 三、光的色散： 1、色散现象：红、橙、黄、绿、蓝、靛、紫。 2、光的色散现象说明： 白光是由各种色光混合而成的，是复色光。 不同色光偏折程度不同，红光的偏折程度最大，紫光的偏折程度最小。 3、透明体的颜色由它透过的色光决定（什么颜色透过什么颜色的光）； 4、不透明体的颜色由它反射的色光决定（什么颜色反射什么颜色的光，吸收其它颜色的光，白色物体反射所有颜色的光，黑色物体吸收所有颜色的光） 例：一张白纸上画了一匹红色的马、绿色的草、红色的花、黑色的石头，现在暗室里用绿光看画，会看见黑色的马，黑色的石头，还有黑色的花在绿色的纸上，看不见草（草、纸都为绿色）
江永中合实业发展有限公司工程管理部 部门职能及岗位设置 一、部门使命 作为公司工程管理的职能部门，工程管理部主要负责公司房地产开发项目的技术、质量、进度以及安全管理工作，确保项目工程顺利进行。 二、部门职能 1．建立健全工程管理制度，制定符合公司发展的工程项目管理模式和业务流程。 2．参与项目策划、规划设计等前期工作，配合公司有关部门做好开发报建、招投标、合同签订等工作。 3．负责公司开发项目的进度管理，使项目在计划期限内完成。 4．负责公司开发项目的技术、质量管理，组织图纸会审和设计交底，审核施工方案，使项目满足相应技术规范和质量标准的规定，同时满足公司相应的质量要求。 5．负责公司开发项目的安全管理，做好对勘察、设计、监理、施工单位安全管理工作的监督。 6．负责公司开发项目的档案资料管理，做好项目图纸、资料文件的收集、整理、传阅、归档及移交工作。
7．采用科学、合理、先进的管理方法，审核设计变更和工程签证，控制建造成本，降低工程造价。 8．组织各阶段工程验收，配合公司相关部门做好项目结算工作。 9．配合物业公司验房、交房，做好物业移交和保修工作。 10．做好与质量监督检查站等政府职能机构的对外协调工作。三、岗位设置 四、岗位职责 1.工程管理部经理 直接领导：公司总经理 岗位职责： 1）全面履行部门职责； 2）负责本部门的队伍建设、团队建设，确保良好的工作氛围及工作作风；
3）负责规范部门内部管理，建立、健全部门规章制度、工作规程；4）全面执行房地产开发建设规章制度，并检查、督促部门员工执行；5）总体负责公司开发项目进度管理、质量管理、成本控制、安全文明施工管理、施工及监理单位管理、外部关系协调等工作； 6）代表公司行使合同权利、履行合同义务，督促施工单位全面履行义务； 7）督促、检查、审核项目开发综合策划方案、施工组织设计、监理规划、监理细则、专项施工方案，确保项目建设总体策划方案切实可行； 8）督促、检查、审核项目开发建设计划、招标立项计划、设计出图计划、施工进度计划及甲供材料申报计划； 9）督促、检查开发项目的台帐管理、资料管理等基础工作； 10）审定工程签证、进度申请、结算申请及组织审定竣工资料；11）督促、检查部门员工熟悉掌握合同、图纸及规范，组织相关单位、部门进行图纸会审； 12）主持处理重大工程质量事故、安全事故； 13）协调公司各部门，及时解决制约工程的较大问题； 14）负责与交通、质监、安监等政府职能部门的协调工作； 15）完成公司总经理布置的其他工作。 2.专业工程师（土建、水暖、电气） 直接领导：工程管理部经理 岗位职责：
物理总复习：光的折射、全反射、色散 编稿：李传安审稿：张金虎 【考纲要求】 1、知道折射率的概念，能运用折射定律进行相关计算； 2、知道光的色散现象，不同频率的光在同一介质中传播速度与频率的关系； 3、知道全反射现象及其条件，会进行相关计算，了解全反射的应用； 4、会测定玻璃砖的折射率。 【知识络】 【考点梳理】 考点一、折射及折射定律 要点诠释： 1、光的折射 光射到两种介质的界面处，一部分进入到另一种介质中，并且改变原来的传播方向的现象叫做光的折射。 2、折射定律 折射光线跟入射光线和法线在同一平面上，并且分别位于法线两侧，入射角i的正弦跟 折射角r的正弦成正比，即sin sin i r =常数。 3、折射率 光从真空射入某种介质发生折射时，入射角i的正弦跟折射角r的正弦之比，叫做这种 介质的折射率，即 sin sin i n r =。 它还等于光在真空中的速度c跟光在这种介质中的速度v之比，即 c n v =。 对折射定律的理解： （1）任何介质的折射率均大于1。 （2）相比较而言，折射率大的物质叫光密介质，折射率小的物质叫光疏介质，因此光疏介质或光密介质是相对的。 （3）介质的折射率表明了介质的折光能力，是由介质的属性决定的，不同的介质有不同的折射率，所以光在不同的介质中的速度不同。 （4）光的折射现象中，光路是可逆的。 考点二、全反射和临界角 要点诠释： 1、全反射 光从光密介质射入光疏介质时，在界面处，一部分光被反射，另一部分光被折射到另一种介质中，随着入射角的增大，折射角增大，且折射光线能量减弱，反射光线能量增强，当入射角增大到某一角度时，使折射角等于90°，折射光线消失，只剩下反射光线，这种现象叫做全反射。 2、临界角 在全反射中，当折射角等于90°时的入射角叫临界角。
房地产工程管理部职责 Company number：【0089WT-8898YT-W8CCB-BUUT-202108】
公司工程管理部职责1 一、工程管理部的职能： 1．工程管理程部做为公司负责工程技术管理的职能部门，负责公司房地产开发项目的工程项目管理； 2．参与项目策划、规划设计等前期工作，配合公司有关部门做好招投标、预决算等工作； 3．负责工程进度的管理，使工程项目在工期内完成； 4．按照公司制定的"精品"战略，进行高质量的工程管理，创建精品项目； 5．采用科学、合理和先进的管理方法，控制建造成本，降低工程造价。 二、工程管理部职责 1．建立健全工程管理，制定符合公司发展的工程项目管理模式和业务流程； 2．在图设计阶段，负责与设计院进行本专业具体的业务联系，确保结构专业施工图质量满足公司和项目的要求； 3．在施工阶段，负责同设计院就设计图纸、设计变更等内容，进行具体的业务联系，提高解决图纸问题和现场问题的效率，确保工程的正常施工； 4．跟踪工程项目的前期工作，负责开工前的现场准备工作，搞好现场三通一平； 5．负责施工图审查交底和图纸会审工作； 6．制定工程项目施工组织设计审查要点，负责施工组织设计和有关技术方案的审查； 7．根据项目特点，确定工程项目管理模式，落实安排项目管理人员； 8．定期对工程的进展进行检查督促，解决存在的问题； 9．定期对部门内部、监理单位、施工单位的工作质量进行检查； 10．按照公司绩效考核管理办法，对员工进行绩效考核； 11．配合监理单位对现场管理、施工安全、质量、进度等进行检查，主持或参与对工程事故进行调查处理； 12．对设计变更、修改、现场施工签证进行技术审查，按公司和部门的管理制度履行相应职责； 13．配合公司其他部门有关文件的研讨，提出本部门的和意见； 14．配合物料供应部对到场甲供甲控材料设备的接收和使用进行监控； 15．负责公司工程技术的管理，并作为公司档案管理的补充； 16．负责现场资料的收集，为索赔和反索赔管理工作提供事实依据； 17．为成本控制部的决算审查工作提供现场情况说明； 18．收集整理工程有关资料，参加公司招投标工作，并为其提供工程方面的建议； 19．组织项目竣工验收和移交，配合公司的售后服务； 20．有计划有步骤地对部门员工进行岗位，提高其综合业务素质和项目管理水平； 21．对工程项目部的工作进行检查、督促，以保证工程计划的完成； 22．根据公司管理办法，参与工程合同的审查，对有关工程技术条款提出建议和意见； 23．配合策划销售部，对现场售楼处和样板房的施工进行管理。
光的折射与色散 知识点一：光的折射 1. 光的折射：光从一种介质________另一种介质中时，传播方向发生________的现象。 2. 基本概念： ________：入射光线和折射界面的交点，用字母“________”表示； ________：过入射点与折射界面垂直的直线，如图中NN’，用________表示； ________：从一种介质照射到折射界面的光线，如图中________； ________：入射光线与法线的夹角，如图中________； ________：从折射界面射入另一种介质的光线，如图中________； ________：折射光线与法线的夹角，如图中________． 3. 折射定律： （1）________与________、________在同一平面内； （2）折射光线和入射光线分居________两侧； （3）光从空气斜射到水或玻璃中时，折射角________入射角；光从水或玻璃中斜射到空气中时，折射角________入射角； （4）入射角________时，折射角也随着________； （5）当光线垂直射向介质表面时传播方向________． 画出光从空气中斜射入水中的折射现象光路图， 画出光从水中斜射入空气中的折射现象光路图 画出光垂直射入水中的光路图学物理，找老杜
4. 折射现象： a) _____________ b) 鱼看起来_________，实际位置更__________ c) ____________________ d) _____________________
知识点二：色散 1. 光的色散：太阳光通过三棱镜后，分解成______、______、______、______、______、_____、_____的现象叫做光的色散。白光不是单色光，而是由多种色光混合而成的_______光。 2. 色光三原色：红、________、________ 3. 颜料三原色：红、________、________ 4. 物体的颜色 a) 不透明物体的颜色由它________的色光决定； b) 透明体的颜色由________它的色光决定。