Discrete Dynamics in Nature and Society

Discrete Dynamics in Nature and Society / 2017 / Article

Research Article | Open Access

Volume 2017 |Article ID 1703608 | https://doi.org/10.1155/2017/1703608

Keliang Chang, Hong Zhou, Guijing Chen, Huiqin Chen, "Multiobjective Location Routing Problem considering Uncertain Data after Disasters", Discrete Dynamics in Nature and Society, vol. 2017, Article ID 1703608, 7 pages, 2017. https://doi.org/10.1155/2017/1703608

Multiobjective Location Routing Problem considering Uncertain Data after Disasters

Academic Editor: Guang Zhang
Received16 Jan 2017
Accepted06 Mar 2017
Published23 Mar 2017

Abstract

The relief distributions after large disasters play an important role for rescue works. After disasters there is a high degree of uncertainty, such as the demands of disaster points and the damage of paths. The demands of affected points and the velocities between two points on the paths are uncertain in this article, and the robust optimization method is applied to deal with the uncertain parameters. This paper proposes a nonlinear location routing problem with half-time windows and with three objectives. The affected points can be visited more than one time. The goals are the total costs of the transportation, the satisfaction rates of disaster nodes, and the path transport capacities which are denoted by vehicle velocities. Finally, the genetic algorithm is applied to solve a number of numerical examples, and the results show that the genetic algorithm is very stable and effective for this problem.

1. Introduction

In recent years, man-made or natural disasters which caused huge casualties and economic losses occurred frequently in different regions and countries. Timely and effective rescue works are very important after disasters. The emergency logistics management mainly includes two aspects [1]: the facility location problem (FLP) and the vehicle routing problem (VRP). In fact, there is a close relationship between facility location problem and vehicle routing problem about emergency logistics, that is, location routing problem (LRP). Compared with the traditional transportation logistics, the facility location problem and vehicle routing problem about emergency logistics are more challenging and complex [2]. von Boventer [3] combined the facility location problem and the vehicle routing problem. Up to now, there have been abundant research results about LRP [4, 5].

After disasters, there are a high degree of uncertainties in all aspects, such as the relief demands and the path transport capacities. Three major methods for dealing with the uncertain parameters are stochastic programming, fuzzy method, and robust optimization method. Ahmadi-Javid and Seddighi [6] studied a stochastic LRP problem. The materials which were provided by facilities are random variables. Under uncertain demands in the short time after disasters [7], large scale emergency scheduling problems were studied, including the selection of supply points, route selection, the decisions of the transport quantities, and the distribution methods, and the fuzzy set was applied to express the uncertain demands. Zare Mehrjerdi and Nadizadeh [8] studied the LRP problem with capacity restrictions of the vehicles and the distribution centers. Triangular fuzzy number represented the uncertainty demands. Lu [9] considered the robust weighted P-center model, and the travel time and demands of affected points were all uncertain. Wang et al. [10] proposed a dynamic time-space network model. It was a network flow model with multistages and multimaterials. Robust optimization method was applied to deal with the uncertain demand. Wang et al. [11] proposed a multiobjective location routing model with split delivery. It optimized the allocation of reliefs after earthquake. The objectives were to minimize the travel time, to minimize the total costs including the fixed costs of the distribution centers and the vehicles transportation costs and to maximize the path reliability, but the demands of disaster points and the probabilities of available arcs were certain. Koç et al. [12] studied the LRP problem with time windows. It was a certain problem with heterogeneous fleets and with spit delivery, and the objective was to minimize the total costs.

It can be seen from the existing literatures that there are little researches on LRP problem with uncertain parameters which are solved by robust optimization. In this paper, the robust optimization method is used to deal with the uncertain demands and the uncertain velocities between two disaster points on the paths. In addition, the most scholars take the cost as the objective of LRP problem. Actually, the distances and the travel time between two points after disasters will be affected, and we will pay the price. Then, the distance and travel time can be understood as the travel cost. In this paper, the half-time windows constraints are quoted, so the time when the materials reach the demand points cannot be later than the specified time. Thus, the timeliness of emergency rescues is improved. After disasters, all kinds of materials are in short supplies, so it is very important to possess adequate relief supplies. Therefore, the minimization of the total distribution costs is one of the objectives, and the maximization of the worst path satisfaction rates is the second objective. After incidents, the transport network will be destroyed. In order to find a better path, the maximization of path transport capacities is the third objective in this paper.

2. The Problem Description

Generally, the distribution network of reliefs after disasters is described as a graph . is a vertex set, and is a set of candidate distribution centers. We assume that there are no demands for a point in the set . is disaster point set. is effective arc set. is vehicle set. This paper considers the following three objectives:(1)Minimize total costs including fixed costs and vehicle transportation costs.(2)Maximize the minimum material satisfaction rates of demand points.(3)Maximize the transport capacities of the worst path. (The transport capacities are represented by the velocities of the vehicle.)

This paper has the following assumptions:(1)The disaster points and the candidate distribution centers are known and the capacities of the candidate distribution centers are large enough.(2)The available arcs and distances between two points on the transport network are known.(3)Because the reliefs are calculated by volume, different types of relief supplies can be regarded as a kind of material.(4)The relief demands of the demand points are greater than or equal to the amounts of supplies because of the materials shortage after disasters.(5)In this paper, we can only consider the disaster points which can be serviced by vehicles, and the disaster points which can be serviced by the special transportation methods (e.g., helicopters) are ignored.

The parameters and variables used in this paper are introduced in Notations.

3. The Multiobjectives LRP Model

3.1. Objective 1: Minimization of the Total Distribution Cost—

In the location routing problem, it is necessary to determine the number and location of the distribution centers and to arrange the disaster points to the distribution centers, and the corresponding vehicle routing will be decided. Therefore, the total costs of the relief distributions include the fixed costs of distribution centers and the vehicle transport costs.

3.2. Objective 2: Maximization of the Worst Path Satisfaction Rates—

We hope that reliefs can reach the demand point timely and effectively after disasters, so we should consider the relief satisfaction rates of the demand points. We hope to maximize the relief satisfaction rates in affected areas, and the fairness of the relief distributions is taken into account. Therefore, the second objective is maximization of the worst satisfaction rates.where , is called the relief satisfaction rate of demand point . The demands of point are uncertain: .

3.3. Objective 3: Maximization of the Worst Path Transport Capacities—

The original paths after disasters are affected more or less. At this time, we take into account the path transport capacities, and the worst path transport capacities are maximized in this paper. The sum of velocities indicates the path transport capacities. The vehicle velocities are uncertain. Let .

The following formulas are the constraint conditions. In adition, the constraints conditions (4), (5), (6), (7), (8), (9), (16), (17), and (18) of the literature [11] are still used in our study.

Formulas (4) and (5) are the time window constraints. is a great positive number. This article requires that the sum of travel time and the service time is not bigger than the set time. Constraint (6) shows that the total amounts of reliefs delivered from the distribution centers to the disaster points shall not exceed the total amounts of available reliefs. Constraint (7) ensures that the relief volume transported to the affected areas by vehicle cannot exceed the load capacities of vehicles. Constraints (8)-(9) ensure that the decision variables are 0-1 nonnegative variables.

There are subloop elimination constraints in order to avoid the subloop. The constraints proposed by Dror et al. [13] are applied in this paper. Let indicate the outgoing degree of point : .

4. The Solution Method of This Model

4.1. Data Uncertainty Description and Robust Solution

The travel velocities of vehicles on each arc are uncertain, so the interval of the vehicles travel velocity on is . The relief demands on each point are uncertain too. The relief demands on each point are Let be a feasible scheme for the above model, where and . Let be the feasible solutions set, so a scheme corresponds to a group of and a path . indicates Cartesian product of two intervals. is called a scenario. In order to facilitate the description of the problem, , are changed equally. Given a scenario, problems (12) and (13) are certain. In certain problem the objective function 2 isWe need to define the maximum velocities sum of path under the scheme and the scenario . The objective function 3 is expressed by formula (17) under the scheme and the scenario . The robust deviations and about formulas (12) and (16) under the scheme and the scenario are indicated by formulas (18). Let be the optimal scheme of (14) and (17) under scenario . The robust costs of a scheme can be represented by the following problems: The second objective and the third objective of the above problem can be expressed as follows:Formula (21) implies minimizing the maximum of the robust deviations.

4.2. Robust Cost Analysis

Because the uncertain demands and uncertain transport velocities are represented by continuous intervals, the scenario set is an infinite set. Therefore, it is very difficult to get the estimate values of , . To find the worst scenario under scheme is an urgent problem. Let be a scenario introduced by scheme on the point , and is the corresponding path for scheme . The following two assumptions are satisfied:(1)The demands of point equal the corresponding upper bound , and the demands of other points equal the corresponding lower bound .(2)The transport velocities of arc on the path equal the corresponding lower bounds , and the transport velocities of arc on the other paths equal the corresponding upper bounds . and can be used to make the following changes.

Lemma 1. With the above introduction of and , for a scheme ,where and are optimal schemes under the scenarios and .

Proof. Taking the second objective function as an example, the third objective function can be proved by the same method. Let be the optimal solution of problem (14), and is the optimal solution of problem (19).
Main Claim. is the optimal solution of problem (19), so is the worst scenario under scheme , and is the optimal scheme of . Then the following formulas can be obtained:It can be seen that the main claim of Lemma 1 can be proved converting to . We can finish this conversion by the following steps.
Step 1. The upper bound of point substitutes .
Before converting, since is the optimal solution of (19), . Therefore, . In the first conversion step, cannot increase by more than . In addition, in Step , the value of cannot decrease because and cannot increase because is the optimal solution of problem (19). Therefore, the value of does not change in Step .
Step 2. The lower bound substitutes , where , and the values of and do not change. Therefore, remains the optimal solution of (19). Thus, the main claim is proven, as in Lemma 1.

The following theorem is obtained by Lemma 1, which greatly simplifies formulas (19) and (20).

Theorem 2. For any ,

4.3. The Solutions of This Model

Many practical problems need to optimize multiple objectives simultaneously. Sometimes these goals often compete with each other or contradict each other, so the definition of Pareto optimal solution is introduced.

(1) Pareto Dominance. Considering all objectives, if solution is at least as equal as , and better than with at least one objective value, solution dominates (denoted as ). For minimizing if

(2) Pareto Optimum. A solution is called Pareto optimal or nondominated solution if and only if there is no any solution that satisfies .

(3) Pareto Front. Furthermore, if is Pareto optimal (nondominated), then is said to be the nondominated vector. The set of all nondominated vectors is called Pareto front (or nondominated frontier).

The papers [1417] are the common methods to solve multiobjective optimization problems. In this paper, genetic algorithm [17] is applied to solve the uncertain problem with half-time windows. The specific processes are as follows.

(1) Initial Population. According to the characteristics of the LRP problem, each chromosome includes three substrings. We can calculate three objective function values through decomposing [11]. Choose the worst scenario according to Sections 4.1 and 4.2. Transport time is calculated based on the distances between the points and the velocities of the vehicle (Ignoring the distribution time of distribution centers). If the sum of travel time and service time are greater than , the transport capacities of path are punished, and the transport capacities are updated to (transport capacity)/(transport time + service time).

(2) Mutation Operation. Variation vectors can be obtained through mutating. In order to avoid being trapped in local optimum, this paper introduces the inversion sequence variation method for each substring of chromosome [18]. It randomly chooses two notes within a chromosome and then reverses their contents. The following two formulas are the examples. Parent is [5 6∣2 3 7 8 9∣1 4], and child is [5 6∣9 8 7 3 2∣1 4] after reversing.

(3) Crossover Operation. Crossover operation is applied to obtain the trail vector after mutation operator. According to the literature [19], the two points crossover method is used in , and single point crossover method is applied to produce , . , , and which constitute the trail vector .

(4) Selection Operation. The weighted method [20] is applied to sort individuals in population. is selected randomly on interval , and is obtained randomly on interval . Because the satisfaction rate is less than 1, it is easy to lose with the total costs and the total path velocities, so we select on interval randomly. Then, we let ( is the number of disaster points).

(5) Steps of Genetic Algorithm(S1)Generate initial population of size NP randomly.(S2)The variation population is obtained by the variation process, and trail population is obtained by the crossover operation.(S3)Combine the parent population and trial population together to form population .(S4)Compute the objective values for each chromosome in .(S5)Determine the values of , , and and compute the value of and sort individuals based on .(S6)Select the first NP individuals as the next generation population.(S7)Stop the procedure if the generation is bigger than maxgen (maximum of iteration times); else turn to (S2).

5. Numerical Experiment

Parameter setting is ; mutation probability is ; crossover probability is .

The combination of the number of candidate distribution centers and the number of disaster points is

The coordinates of each point are randomly generated in the plane, and the distances from point to point equal the distances from point to point . The fixed costs of the distribution centers (yuan) are randomly generated on interval . The demands of the disaster points and the velocities of the available arcs are uncertain, and uncertain parameters are processed in accordance with the method of Sections 4.1 and 4.2. The required numbers of large and medium and small vehicles for different number of disaster points are shown in Table 1. Let the time when the disaster happened be the zero time. The last time (hour) for each demand point to be served is randomly generated on the interval . Table 2 gives the parameters of the vehicles. Table 3 shows the results of the calculation, and the approximate Pareto front is shown in Figure 1 (3-10).


Vehicle type10 points100 points1000 points

Large vehicle 101001000
Medium vehicle 660600
Small vehicle 990900


Vehicle typeVehicle capacity  (cm3) General velocity  (km/h) Unit transportation cost   (yuan/km)

Large vehicle 50 10.0
Medium vehicle 30 3.1
Small vehicle 201.7


ExamplesTotal costs Satisfaction rate (%) Path velocity
Mean value of approximate Pareto frontsApproximate Pareto solutionMean value of approximate Pareto frontsApproximate Pareto solutionMean value of approximate Pareto frontsApproximate Pareto solution

3-108150.1581155.4855.44643.83331098
3-100346252526364.2862.778644.18761
3-100015819013956059.3559.808986588913
6-100428073897660.5764.4985548575
6-100012956011665054.8960.011114512270
10-100438694856758.9959.628097.48329
10-100013798011896058.6761.138766089437

6. Conclusion and Prospect

In this paper, a multiobjective nonlinear location routing model with half-time windows is proposed. The arrival time of the reliefs to the demand points cannot be later than the specified time, so the timeliness of emergency reliefs is enhanced. The affected points can be visited more than one time in this article. After the disasters, all kinds of materials are in short supplies, so it is very important to have adequate relief supplies. Therefore, the minimization of the total distribution costs is one of the objectives, and the maximization of the worst path satisfaction rates is the second objective. After incidents, the transport network will be influenced. In order to find a better path, the maximization of path transport capacities is the third objective in this paper. After disasters situations are very complex with a high degree of uncertainties, such as demands, transportation time, and path through velocities. Therefore, this paper assumes that the demands of the disaster nodes and the transportation velocities of the available path are uncertain, and the robust optimization is applied to deal with the uncertainty. The genetic algorithm is applied to solve a number of numerical examples; the results show that the algorithm is very stable and effective for this problem. Finally, the method of solving the problem can also apply simulated annealing algorithm or nondominated sorting genetic algorithm.

Notations

(1) Parameters
:The number of candidate distribution centers
:The number of disaster points
:The number of vehicles
:The fixed costs of distribution center ,
:The velocity of arc
:The demands of disaster point ,  
:The available quantities of reliefs on the transport network
:The unit transportation cost of vehicle,
:The load capacities of vehicle ,
:The time of vehicle starting service at point ,   ,  
:The latest service time at point ,  .
(2) The Variables
:1, if the distribution center is set up; 0, otherwise
:1, if the point is in front of point on the path of the vehicle ; 0, otherwise
:1, if the point is on the path of the vehicle ; 0, otherwise
:The relief supply quantities transported by the vehicle to point , ,  
:1, if the last demand point serviced by vehicle is point ; 0, otherwise,  .

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

The research is supported by the Natural Science Foundation of China (Grant no. 71471007).

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Copyright © 2017 Keliang Chang 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.


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