research article dynamic optimization model and algorithm...

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2013, Article ID 841458, 6 pageshttp://dx.doi.org/10.1155/2013/841458

Research ArticleDynamic Optimization Model and Algorithm Designfor Emergency Materials Dispatch

Bingbing Dan, Wanhong Zhu, Huabing Li, Yangyang Sang, and Yan Liu

College of Field Engineering, PLA University of Science &Technology, Nanjing 210007, China

Correspondence should be addressed to Bingbing Dan; [email protected]

Received 8 July 2013; Revised 20 October 2013; Accepted 12 November 2013

Academic Editor: Davide Spinello

Copyright © 2013 Bingbing Dan et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Emergency materials dispatch (EMD) is a typical dynamic vehicle routing problem (DVRP) and it concentrates on process strategysolving, which is different from the traditional static vehicle routing problem. Based on the characteristics of emergency materialsdispatch, DVRP changed the EMD into a series of static problems in time axis. Amathematical multiobjective model is established,and the corresponding improved ant colony optimization algorithm is designed to solve the problem. Finally, a numeric exampleis provided to demonstrate the validity and feasibility of this proposed model and algorithm.

1. Introduction

In the emergency situations, such as earthquakes, snow-storms, and wars, ensuring all kinds of materials to beefficiently dispatched is the key problem to the emergencylogistics. And it is also a typical dynamic vehicle routingproblem whose aim is exactly a guarantee of the smoothlogistic workflow process. Meanwhile, emergency materialdispatch problem is a much more complex vehicle routingproblem because of its high efficiency and strict time limita-tion.Therefore, exploring themodelingmethods and efficientsolving algorithms under the dynamic condition is greatlysignificant [1].

The research scope of dynamic vehicle routing problem(DVRP) includes uncertain demands, uncertain networkperformance, uncertain service vehicles, and subjective pref-erence of decisionmakers.The solving strategy can be dividedinto two categories. One is local optimization strategy, whichdeals with the dynamic adjustment of implementation pro-cess and local optimization algorithms.The insertionmethodand k-opt method are often used to redesign the routinglines and vehicles distribution [2]. The other one is restartingoptimization strategy, which is actually a static solutionfor dynamic problems. Once received determined real-timeinformation, the strategy starts from the very beginningagain to find the optimal material dispatch scheme. As one

successful application of restarting optimization strategy,Paraftis used dynamic programming (DP) to deal with dial-a-ride problems with one vehicle [3]. However, this method canonly solve small-scale problems not exceeding ten demandnodes.

Ant colony algorithm (ACA) has the nature advantageto adapt the dynamic environment with inherent robustness.Eyckelhof [4] carried out some research of VRP under thecondition of unchanging node number and changing nodedistance, which indicates the sudden change of traffic con-gestion degree. The preliminary study results show that theoriginal version and some slightly improved versions of ACAare quite good in solving some simple test performances.Wang [5] changed the dynamic vehicle routing problem intoa series of static subproblems based on time axis. The pathpheromone is reinitialized and the new problem with oldunmet demand nodes and new nodes in next dispatch roundis modeled to be solved.

Because emergency logistics is focusing on speed insteadof quantity, the dynamic effective material management isto replace the inventory materials’ lack of mobility. The keypoint of emergency material dispatch in this paper is how toensure the reliability of material supply stability in dynamicenvironment. Based on the characteristics of emergencymaterial dispatch problem, the multiobjective mathematicmodel is established, an improvedACAoptimization strategy

2 Mathematical Problems in Engineering

and algorithm are developed, and a numeric example provedthis method is feasible to generate real-time emergencymaterial dispatch scheme corresponding to each point of thetime axis.

2. Mathematic Model of Emergency MaterialDispatch in Dynamic Environment

As to dynamic events of DVRP, the normal treatments arethe event trigger mechanism and rolling horizon principle.While we adopt the event triggermechanism, the new arrivedmaterial assignment is immediately inserted into the runningdispatch scheme. Meanwhile we adopt the rolling horizonprinciple and divide the operation time into several smallhorizons. In the intervals, the material dispatch scheme isadjusted. The two treatments have both advantages and dis-advantages. The former one may cause the frequent schemealtering, while the latter one may decrease the service quality.

2.1. Strategic Analysis. Because sudden incidents in the emer-gency logistics are various and discrete, the demand ofmaterials is changing with the development of the situation.According to the actual material assignment operation, arolling horizon multitask decision-making emergency mate-rial dispatch model is built based on time axis, and thedispatch scheme is updatedwhile distributing unfinished dis-patch scheme and addon dispatch scheme in these intervals.

In the interval, the event occurrences trigger the simple𝐾 exchange neighborhood optimization and service denialstrategy, while guaranteeing that every undamaged vehiclefinally returns to the logistic center. Based on the instantstatistics information about each node demands of themoment, the dynamic model can be converted to submodels.As the time goes on, thematerial demands of customer nodeswill accumulate because of the frequent scheme altering orthe assignment finishing rate as shown in Figure 1.

The restart strategy based on rolling horizon principlehas three advantages. Firstly, the dispatch scheme can changewith the dynamic network attributes. Because the materialflow is continuous, the new dispatch scheme round canbe adjusted to take full use of limited transport networkresources. Secondly, it can reduce the complexity of problemand generate simpler and easier schemes, inwhich every vehi-cle starts from the logistic center and the initial capacity is theloading capacity. Thirdly, the improved ACA algorithm canspeed up response time by using the information generatedfrom the solving process of former dispatch scheme.

2.2. Variable Parameter Descriptions. The vehicles that havebeen the starting must try their abilities to serve all nodesand the vehicles which are in the logistic center are dis-patched according to actual need. The mathematic model ofemergency material dispatch is built based on some cases asfollows.

(i) Emergency materials are all generic class materialand dispatched as standardized container units whoseunit of measurement is twenty-foot equivalent units(TEU).

1 2 3 4 5 6 7

10

9

8

7

6

5

4

3

2

1

0

Add-on dispatch scheme

Customer nodes

Accu

mul

ated

mat

eria

l dem

ands

Unfinished dispatch scheme

Figure 1: Accumulated material demands of customer nodes.

(ii) A route can only start from the logistic center and endin the logistic center.

(iii) The type of vehicles is single and the lay time isignored. The drive speed is constant and the initialroute weight matrix 𝐴 is represented by the distancebetween nodes.

(iv) The emergency materials should be dispatched intoall customer nodes safely, cheaply, and fast during thetransportation.

(v) The parameters of the mathematic model are allinteger decision variables.

According to the current research situation, the problemto be studied is described as a vector set of 𝐺 = (𝑉,𝐷,𝑊, 𝑅)

at the moment of 𝑡. The problem is a network topology𝑉 = {V

0, V1, V2, . . . , V

𝑛} that contains a logistic center and 𝑁

customer nodes (C node for short), where𝑁 is themaximumnumber of the𝐶 nodes. Each node has an associated location[𝑥𝑖, 𝑦𝑖], and the network is in the Euclidean plane, so the

travel distance 𝑑𝑖𝑗between any two nodes V

𝑖and V

𝑗is the

straightline distance between them.The distance 𝑑𝑖𝑗between

any two nodes of vector𝑉 constitutes the route weight matrix𝐷, which is symmetric and satisfy the triangle inequality:𝑑𝑖𝑗≤ 𝑑𝑖𝑙+𝑑𝑙𝑗. And the travel time can be obtained if you divide

the distance by the drive speed, which is also a constant. Each𝐶 node denotes a service request. All requests will randomlyarrive within a planning fixed horizon. Accumulatedmaterialdemands which contain both unfinished dispatch schemeand addon dispatch scheme are expressed as vector 𝑊 =

{𝑤1, 𝑤2, 𝑤3, . . . , 𝑤

𝑛}, which corresponds to the emergency

material demand of 𝐶 node V𝑖at the moment of 𝑡. Every

vehicle must depart from and return to the logistic center,and the transportation parameter matrix is 𝑅 = (𝐾,𝑄).

Mathematical Problems in Engineering 3

The logistic center has 𝐾 service vehicles whose loadingcapacity is 𝑄 TEU.

Define binary variable:𝑥𝑖𝑗𝑘

= {1 if vehicle 𝑘 drives fromnode V

𝑖to node V

𝑗

0 else.

(1)

2.3. Objective Function

min 𝑧1=

𝐾

∑

𝑘=1

𝑁

∑

𝑗=0

𝑁

∑

𝑖=0

𝑑𝑖𝑗𝑥𝑖𝑗𝑘, (2)

min 𝑧2=

𝐾

∑

𝑘=1

𝑁

∑

𝑗=1

𝑥0𝑗𝑘

. (3)

The model seeks the optimization routes of the currentsnapshot so that the total dispatch service benefit is max-imized. Equation (2) is an object function minimizing thetotal dispatch route length, which also makes the drive costthe lowest. Equation (3) is an object function minimizing thenumber of vehicles that have been put into use in the logisticcenter, which also cuts down greatly the fixed charges. Andthe two targets will guarantee the maximum of emergencymaterial dispatch safety.

In this paper the multiple objectives are integrated into asingle objective with the weight coefficient method, and theweight vector of the two objectives is 𝜔 = (0.354, 0.646),which was obtained from importance comparison matrix inAHP. Considering the data in numeric example, the value of𝑧1ranged from 800 to 1000, and the value of 𝑧

2ranged from 9

to 22.Therefore we used weighted summation of the two datato get the single objective function 𝑍 as follows:

min𝑍 = 0.354 ⋅ lg𝑧1+ 0.646 ⋅ 𝑧

2. (4)

2.4. Constraint Conditions. Consider𝐾

∑

𝑘=1

𝑁

∑

𝑗=1

𝑥0𝑗𝑘

≤ 𝐾; (5)

𝑁

∑

𝑗=0

𝑁

∑

𝑖=0

𝑤𝑗𝑥𝑖𝑗𝑘

≤ 𝑄, 𝑘 ∈ 𝐾, (6)

𝑁

∑

𝑗=1

𝑥0𝑗𝑘

=

𝑁

∑

𝑖=1

𝑥𝑖0𝑘

≤ 1, 𝑘 ∈ 𝐾, (7)

𝐾

∑

𝑘=1

𝑁

∑

𝑖=0

𝑥𝑖𝑗𝑘

= 1; 𝑗 = {1, 2, 3, . . . , 𝑁} , (8)

𝐾

∑

𝑘=1

𝑁

∑

𝑗=0

𝑥𝑖𝑗𝑘

= 1; 𝑖 = {1, 2, 3 . . . , 𝑁} , (9)

𝑖 ̸= 𝑗. (10)

Equation (5) ensures that the total number departed fromthe logistic center is less than the upper bound in every

interval of dispatch scheme. Equation (6) ensures that thesum of total demand of all 𝐶 nodes in a vehicle’s route isless than its loading capacity. Equation (7) can ensure thatall vehicles have departed from the logistic center and finallyreturned to the depot. Equations (8) and (9) ensure that every𝐶 node has one and only one predecessor node and successornode and is visited only once by a vehicle. Expression (10)can ensure the travel route of every vehicle forbid loop node,especially looping in the logistic center.

3. Ant Colony Algorithm Design of DVRP

Dealing with DVRP, the new input information could makethe former optimal dispatch scheme change into the subopti-mal, even the infeasible scheme. In the static environment, itis tolerable to spend more processing time for commandersgetting high quality or optimal dispatch scheme. However,commanders want to obtain the dispatch scheme immedi-ately; the fewer seconds or minutes are the better in anemergency.

The main idea of this algorithm design is to improve theperformance of basic ant colony algorithm by state trans-formation rules using load rating function and pheromoneupdate rules based on ant ranking system [6]. Furthermore,the route pheromone information is reserved while solvingthe former dispatch scheme, so the latter dispatch schemesolving process is speeded up and the emergency materialdispatch scheme is generated.

3.1. State Transformation Rules of ACA. Dorigo M put for-ward the adaptive pseudorandom probability transformationrule in ant-Q algorithm in 1993 [6]. In general approachof basic Dorigo ant algorithm, each of 𝑚 ants constructsseveral routes touring through all the given 𝑁 cities in everygeneration, which makes up the dispatch scheme. Startingat a random city an ant selects the next city using heuristicinformation as well as pheromone information, which servesas a form of memory by indicating which choices were goodin the past. In order to enhance the accuracy and reality ofthe ants while they choose travel routes, this paper proposesthe improved state transformation rule which takes full useof load rating in view of constraint expression (6) as follows.

Consider

𝑆 = {argmax {𝜏𝛼

𝑖𝑗(𝑡) ⋅ 𝜂𝛽

𝑖𝑗(𝑡) ⋅ 𝜇

𝛾

𝑖𝑗(𝑡)} , if𝑅 ≤ 𝑅

0

𝑝𝑘

𝑖𝑗(𝑡) , else,

(11)

𝑝𝑘

𝑖𝑗(𝑡) =

{{{

{{{

{

𝜏𝛼



𝛾

𝑖𝑗(𝑡)

∑𝑠∈allowed𝑘

𝜏𝛼



𝛾

𝑖𝑗(𝑡)

, if 𝑠 ∈ allowed𝑘

0, else.(12)

The probability for ant 𝑘 to append arc (V𝑖, V𝑗) to its partial

solution is then given as 𝑝𝑘

𝑖𝑗(𝑡); 𝜏𝑖𝑗indicates the pheromone

concentration information; 𝜂𝑖𝑗

represents the expectationheuristic information, which equals the reciprocal of 𝑑

𝑖𝑗


and minimizes the total dispatch route length. 𝜇𝑖𝑗repre-

sents the load rating heuristic information, which minimizesthe number of vehicles in use. 𝛼, 𝛽, and 𝛾 are constantsthat determine the relative influence corresponding to thepheromone values, distance values, and load rating valueson the decision of the ant 𝑘. Random variable 𝑅 follows 0-1uniform distribution. Parameter 𝑅

0(0 ≤ 𝑅

0≤ 1) determines

state transformation rule, which also indicates the relativeimportance of exploitation versus exploration. The nodes setallowed

𝑘contains the remaining nodes that have not been

visited so far by ant 𝑘.While ant 𝑘 has 𝑄

𝑖TEU capacity at node V

𝑖, and the

material demand of successor node V𝑗is 𝑤𝑗TEU. Function

𝜇𝑖𝑗= (

𝑄𝑖+ 𝑤𝑗

𝑄) , if 𝜇

𝑖𝑗> 1, then 𝜇

𝑖𝑗= 0. (13)

3.2. Pheromone Update Rules. Once the𝑚 ants of the colonyhave completed their computation, the better dispatch routesare used to globally modify the pheromone trail. In this waya “preferred route” is memorized in the pheromone trailmatrix and future ants will use this information to generatenew solutions in a neighborhood of this preferred route. Forpheromone initialization we set 𝜏

0= 1/(𝑁 − 1) for every arc

(V𝑖, V𝑗) at the beginning of each iteration.

When ant 𝑘 moves from V𝑖to V𝑗, a local updating

is performed on the pheromone matrix, according to thefollowing rule:

𝜏𝑖𝑗 (𝑡 + 1) = (1 − 𝜌

1) ⋅ 𝜏𝑖𝑗 (𝑡) + 𝜌

1𝜏0, (14)

where parameter 𝜌1determines the evaporation rate of

pheromone.The global pheromonematrix adapts the elite ant strategy,

which only uses the optimal and suboptimal dispatch route.And it is updated as follows:

𝜏𝑖𝑗(𝑡 + 1) = (1 − 𝜌

2) ⋅ 𝜏𝑖𝑗(𝑡) + 𝜌

2Δ𝜏𝑖𝑗(𝑡) ,

Δ𝜏𝑖𝑗(𝑡) =

{{

{{

{

𝜙1, if 𝑒

𝑖𝑗∈ optimal dispatch route

𝜙2, if 𝑒

𝑖𝑗∈ suboptimal dispatch route

0, otherwise,

(15)

where parameter 𝜌2determines the evaporation rate of

pheromone, 𝜙1and 𝜙

2are constants, and 𝜙

1> 𝜙2> 0.

3.3. Dynamic Modification Rules of Pheromone Matrix. Dur-ing the emergency material dispatch, service requests arereceived in real time, and serviceability of requesting nodeis immediately verified. The target of this paper is to getthe updated dispatch scheme based on rapid reaction to thedynamic information about the traffic capacity of transportnetwork and demand nodes. However, the situations ofdynamic environment can be divided into three types asfollows:

(i) the change in route weight matrix𝐷,(ii) the change in material demand of customer nodes,

(iii) the change in optimization objectives of materialdispatch.

How to modify the pheromone matrix as much aspossible to adjust the change in route weight matrix is thekey point. Among these three situations, types (i) and (ii)are more common in connectivity and travel time reliabilityalong with the development of the situation. The simpleststrategy is reinitializing all pheromones to 𝜏

0and then

restarting the ant algorithmwhile making the solving processinefficiency, so the current solving process should make fulluse of the previous results of the dispatch scheme calculationin order to reduce the computation time. For description, themodification rules are as follows.(1) Rule 1: Dynamic Distance Weight between Nodes. Inthe case of the distance between any two nodes V

𝑖and V

𝑗

changing,𝑀𝑎𝑥𝐷𝑖𝑠 is themaximumof all the𝑑𝑖𝑗in the former

route weight matrix 𝐷; the pheromone modification rule ofarc (V𝑖, V𝑗) is expressed as follows:

If (𝑑𝑎𝑖

< 𝑃 × 𝑀𝑎𝑥𝐷𝑖𝑠) ∨ (𝑑𝑏𝑖

< 𝑃 × 𝑀𝑎𝑥𝐷𝑖𝑠)

∨ (𝑑𝑎𝑗


∨ (𝑑𝑏𝑗


Then 𝜏𝑖𝑗= 𝜏0⋅ (1 + log(

𝜏𝑖𝑗

𝜏0

)) ,

(16)

where parameter 𝑃 ∈ [0, 1] determines the adjustment range.𝑃 = 0 means no adjustment, while 𝑃 = 1 means allpheromones are changing according to this rule.(2) Rule 2: Dynamic Regulation of Customer Nodes Demands.In the case of the demand 𝑤

𝑖of node V

𝑖changing, 𝑁 is the

number of all customer nodes; the pheromone modificationrule of arc (V

𝑖, V𝑗) is expressed as follows:

𝜏𝑖𝑗= (1 − 𝜃

𝑖) ⋅ 𝜏𝑖𝑗+ 𝜃𝑖⋅

1

𝑁 − 1(17)

(1) 𝜂-strategy [2], as Figure 2. Consider

𝜃𝑖= max{0, 1 −

𝜂

𝜆 ⋅ 𝜂𝑖𝑗

} ; 𝜆 ∈ [0, +∞) ,

𝜂 =1

𝑛 ⋅ (𝑛 − 1)⋅

𝑛

∑

𝑖=1

∑

𝑗=1,𝑗 ̸= 𝑖

𝜂𝑖𝑗.

(18)

Because parameter 𝜂𝑖𝑗equals the reciprocal of𝑑

𝑖𝑗, so it can

be inferred that parameter 𝜃𝑖is inversely proportional to the

distance between node V𝑖and the changed node.

(2) 𝜏-strategy [2], as shown in Figure 3. Consider

𝜃𝑖= min {1, 𝜆 ⋅ 𝜓} ; 𝜆 ∈ [0, +∞) ,

𝜓 = max{

{

{

∏

(𝑥,𝑦)∈𝑃𝑖𝑘

𝜏𝑥𝑦

𝜏max

}

}

}

.

(19)

Mathematical Problems in Engineering 5

0.8

0.6

0.4

0.2

Figure 2: 𝜂-strategy.

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

Figure 3: 𝜏-strategy.

From the equations it can be deduced that the closerarc (V

𝑖, V𝑗) is next to the changed node, the more equalized

pheromone is in the route.To illustrate the 𝜂-strategy and 𝜏-strategy operation, we

used a 10 × 10 grid of cities and set 𝜆 = 1 to visualize howthe distribution of reset values takes place. In Figure 2 thedistribution for the 𝜂-strategy is proportionate to Euclideandistance, while Figure 3 shows that the 𝜏-strategy tends todistribute along the path. By comparing the two strategies,we choose 𝜂-strategy to deal with dynamic regulation ofcustomer nodes demands.(3) Rule 3: Dynamic Regulation of Nodes Number. When theincreased node number equals the decreased number, replacethe nodes and then deal in Rule (1).

(1) When the increased node number is more than thedecreasednumber, directly delete the row and columnin which the node stays.

(2) When the increased node number is less than thedecreased number, insert the new node, calculate thedistance weight, and initialize pheromone to 𝜏

0.

3.4. Detailed Steps of Improved ACA

Step 1. Calculate the dispatch scheme at the initial time of 𝑡0

using improved ACA and generate the solved result.

Step 2. Overwrite the pheromone matrix into the exceldocument “pheromone” until the iteration stops.

Step 3. When calculating the new dispatch scheme, read thedata in excel document “pheromone” and save it initializepheromone matrix.

Step 4. According to the specific changing information ofdynamic environment, update the pheromone matrix withmodification rules in Section 3.2.

Step 5. Calculate the dispatch scheme at the time of 𝑡 usingimproved ACA, generate the new solved result, and then goto and execute Step 2.

4. Experimental Results

Theemergencymaterial dispatch system contains one logisticcenter and 100 demand nodes. The loading capacity of eachvehicle is 200TEU.The static data of numeric example in thispaper adapts the C101 and R101 from Solomon benchmarkat the initial moment. As the situation develops, such asearthquake, snowstorm, and war, each problem has 5 groupsof dynamic data which are used to depict 5 different dynamicdegrees of 10%, 30%, 50%, 70%, and 90%, respectively.We randomly choose the proportional elements of distancematrix𝐷 and nodes demand vector𝑊 from𝐺 = (𝑉,𝐷,𝑊, 𝑅)

to simulate the dynamic environment. The distance matrix isnot symmetric and does not satisfy the triangle inequality anymore. The test data set is designed by modifying benchmarkmethod, which was proposed by Lackner [7] in 2004.

The test work is completed on a desktop computer withIntel Pentium Dual-Core [email protected], 2046 MB RAM,and Windows XP SP3 Professional Operating system. Thealgorithm is coded and compiled usingM file inMatlab 7.8.0.The simulation experimental results are shown in Table 1.

For description convenience, we call the column (1)C1, column (2) C2, and so on. C1 represents the datatype of Solomon’s 100-customer benchmark problems.C2 and C3 are the best known results obtained bythe heuristics for Solomon’s static VRPTW problemsreported in http://www.sintef.no/static/am/opti/projects/top/vrp/bknown.html (last updates: March 24, 2005). C4 DoD isshort for the degree of dynamism, which means the changingnodes’ percentage of all the nodes. Among the data itemsfrom the table, C5, C6, and C7 are the computational resultsfor proposed approach, and they are also the average numberof used vehicles (NV), the average travel distance (length),and the average CPU time (CPU, in s), respectively.The CPUtime is the time used for solving each static version problem.So are the C8, C9, and C10.

Other parameters are set as follows. The ant colony sizeis 50 ants, the number of iterations is 100, and the heuristicfactor is g 𝛼 = 1, 𝛽 = 5, 𝛾 = 1. The evaporation rate of


Table 1: Calculation results of Solomon benchmark problem for various dynamic degrees.

Test data (1) Best known results DoD (4) Basic ACO + restart strategy Improved ACO + pheromone reserved strategyNV (2) Length (3) NV (5) Length (6) CPU (7) NV (8) Length (9) CPU (10)

C101 10 828.94

10% 10 895.77 216.61 10 914.33 57.3330% 10 990.03 208.82 10 962.11 46.8050% 11 1012.34 230.09 11 1001.18 53.9470% 11 1077.60 215.55 11 1031.58 51.9790% 11 1145.45 226.79 11 1039.77 56.00

R101 19 1645.79

10% 20 1795.81 218.72 19 1685.08 76.0230% 20 1812.70 218.55 20 1699.23 76.3150% 21 1900.23 216.39 21 1756.29 78.6270% 21 1950.34 231.28 21 1787.76 74.2290% 21 1991.94 217.12 21 1814.62 72.42

pheromone 𝜌 = 0.4. The state transformation rule parameter𝑅0

= 0.6, and the dynamic modification rule parameters𝑃 = 0.1, 𝜆 = 2.

As shown in Table 1, the basic ACO algorithm with asimple restart strategy uses one more vehicle and generatesthe suboptimal total dispatch route, while its solution timeis far more than other algorithms. The improved ACOalgorithm with pheromone reserved strategy can meet therequirement of quick decisions, which saves an average of50% computation time searching the optimal emergencymaterial dispatch scheme. And the total computation timeis less than 80 seconds in absolute amount dealing with 100-node problem. Because of simplifiedmodeling, this improvedACOalgorithm is superior to the LNS algorithm in [8]. So theimprovedACO algorithmdesigned in this paper is optimal inthe vehicles number and suboptimal in the total route length.

5. Conclusions

Themultiobjective materials dispatch model is built by linearweighted method. The dynamic information generated inthe process of emergency material dispatch is disposed inbatches by rolling horizon strategy, such as traffic capacity oftransport network or demand nodes. Eventually, a numericexample is designed in 5 different dynamic degrees to provethe validity of this model and algorithm. The modifiedant colony optimization algorithm utilizes the pheromonereserved by old material dispatch scheme to initialize theant colony pheromone matrix in seeking for the next newdispatch scheme.Thismethod improves the quality of schemesolutions and obviously the solving efficiency, while generat-ing material dispatch scheme in real time based on currentemergencymaterial dispatchnetwork.However, there are stillsome problems that need to be studied such as split demand,complete set, and multimodal transport of complex dynamicemergency materials dispatch problem.

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