Multi-depot vehicle routing problem for hazardous materials transportation: A fuzzy bilevel programming

Du, Juan; Li, Xiang; Yu, Lean; Ralescu, Dan A.; Zhou, Jiandong

doi:10.1016/j.ins.2017.02.011

Cited by 113 publications

(43 citation statements)

References 32 publications

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“…Bilevel programming is a two-level hierarchical optimization problem, where one problem is nested within another [78]. The first formulation of bilevel programming problems, by H.v.…”

Section: Bilevel Modelmentioning

confidence: 99%

Site Selection Models in Natural Disaster Shelters: A Review

Xü

Qin

et al. 2019

Sustainability

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Constructing natural disaster shelters is important for disaster emergency management, and site selection models provide a feasible technique and method. This paper presents site selection models for natural disaster shelters. A synthesis of the types, objectives, constraints, methods of solutions, targeted disasters and applications of different site selection models for natural disaster shelters is investigated. Shelter location models can be classified as single-objective models, multiobjective models and hierarchical models, according to the objective and hierarchy type. Minimizing the evacuation distance or time, shelter construction cost or number, and the total risk are the general objectives of the models. Intelligent optimization algorithms are widely used to solve the models, instead of the Geographic Information System (GIS) method, due to the complexity of the problem. The results indicate that the following should be the main focuses of future works: How to set a model that can be applied for determining the shelter locations of multiple disasters; how to consider the uncertainty in the models; how to improve the existing algorithms or models to solve large-scale location-allocation problems; and how to develop a new resource-saving model that is consistent with the concept of sustainable development, as advocated by shelter planners and policy makers, which can be applied in real situations. This study allows those undertaking shelter location research to situate their work within the context of shelter planning.

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“…Bilevel programming is a two-level hierarchical optimization problem, where one problem is nested within another [78]. The first formulation of bilevel programming problems, by H.v.…”

Section: Bilevel Modelmentioning

confidence: 99%

Site Selection Models in Natural Disaster Shelters: A Review

Xü

Qin

et al. 2019

Sustainability

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“…In case 1, the risk triangle fuzzy number of the first three hazardous materials depots and 20 customer points in the literature [27] are used as the source data of road segment length, population density, accident rate, and road ramp from each depot to and between customer points. The road segment length is obtained by subtracting the lower limit from the upper limit of the fuzzy number; the population density around the road segment is obtained by subtracting the lower limit from the value with maximum possibilities of the fuzzy number and multiplying by 150; the accident rate is obtained by multiplying the value with maximum possibilities of the fuzzy number by 10 −5 ; the road ramp is obtained by subtracting the value with maximum possibilities from the upper limit of the fuzzy number.…”

Section: Cases Designmentioning

confidence: 99%

“…This optimization problem with two objectives was then transformed into a single objective problem. Du et al [27] developed a fuzzy bilevel programming model in which the upper-level formulation allocated customers to depots and the lower level determined the optimal routing for each depot. However, the study only considered transportation risk minimization as the optimization objective.…”

Section: Introductionmentioning

confidence: 99%

Multi-Depot Vehicle Routing Optimization Considering Energy Consumption for Hazardous Materials Transportation

Mao

et al. 2018

Sustainability

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Focusing on the multi-depot vehicle routing problem (MDVRP) for hazardous materials transportation, this paper presents a multi-objective optimization model to minimize total transportation energy consumption and transportation risk. A two-stage method (TSM) and hybrid multi-objective genetic algorithm (HMOGA) are then developed to solve the model. The TSM is used to find the set of customer points served by each depot through the global search clustering method considering transportation energy consumption, transportation risk, and depot capacity in the first stage, and to determine the service order of customer points to each depot by using a multi-objective genetic algorithm with the banker method to seek dominant individuals and gather distance to keep evolving the population distribution in the second stage, while with the HMOGA, customer points serviced by the depot and the serviced orders are optimized simultaneously. Finally, by experimenting on two cases with three depots and 20 customer points, the results show that both methods can obtain a Pareto solution set, and the hybrid multi-objective genetic algorithm is able to find better vehicle routes in the whole transportation network. Compared with distance as the optimization objective, when energy consumption is the optimization objective, although distance is slightly increased, the number of vehicles and energy consumption are effectively reduced.

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“…In recent years, the growing concern about the effects of pollutions has forced researchers to address the socio-environmental concerns. Du et al [18] considered minimizing the total transportation risk as to the objective function and proposed a fuzzy bilevel programming model to solve the problem of transporting hazardous materials from multiple depots to customers. They introduced a numerical, integration-based fuzzy simulation method combined with four heuristic algorithms (hybrid particle swarm optimization, genetic algorithm, ant colony algorithm, and simulated annealing algorithm) to find the optimal solutions assigning customers to depots and determining the best paths regarding each group of depots and customers.…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Solution Method for Multi-Depot Vehicle Routing-Based Waste Collection Problems

Shi

et al. 2020

Applied Sciences

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This paper addresses waste collection problems in which urban household and solid waste are brought from waste collection points to waste disposal plants. The collection of waste from the collection points herein is modeled as a multi-depot vehicle routing problem (MDVRP), aiming at minimizing the total transportation distance. In this study, we propose a heuristic solution method to address this problem. In this method, we firstly assign waste collection points to waste disposal plants according to the nearest distance, then each plant solves the single-vehicle routing problem (VRP) respectively, assigning customers to vehicles and planning the order in which customers are visited by vehicles. In the latter step, we propose the sector combination optimization (SCO) algorithm to generate multiple initial solutions, and then these initial solutions are improved using the merge-head and drop-tail (MHDT) strategy. After a certain number of iterations, the optimal solution in the last generation is reported. Computational experiments on benchmark instances showed that the initial solutions obtained by the sector combination optimization algorithm were more abundant and better than other iterative algorithms using only one solution for initialization, and the solutions with distance gap were obtained using the merge-head and drop-tail strategy in a lower CPU time compared to the Tabu search algorithm.

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Multi-depot vehicle routing problem for hazardous materials transportation: A fuzzy bilevel programming

Cited by 113 publications

References 32 publications

Site Selection Models in Natural Disaster Shelters: A Review

Site Selection Models in Natural Disaster Shelters: A Review

Multi-Depot Vehicle Routing Optimization Considering Energy Consumption for Hazardous Materials Transportation

A Heuristic Solution Method for Multi-Depot Vehicle Routing-Based Waste Collection Problems

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