Robust multi‐objective vehicle routing problem with time windows for hazardous materials transportation

Men, Jinkun; Jiang, Peng; Xu, Hao; Zheng, Shuguo; Kong, Yaguang; Hou, Pingzhi; Wu, Feng

doi:10.1049/iet-its.2019.0332

Cited by 11 publications

(7 citation statements)

References 32 publications

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“…NSGA-II is an improved multiobjective optimization algorithm proposed by Professor Deb [16]. It is widely used by scholars in transportation optimization [17,18] because of its good distribution and fast convergence. e algorithm proposes a fast nondominated sorting operator, introduces the strategy of saving elites, and replaces sharing with "crowding distance."…”

Section: Algorithmmentioning

confidence: 99%

Multiobjective Optimization of Airport Ferry Vehicle Scheduling during Peak Hours Based on NSGA-II

Ding

Xie

et al. 2022

Security and Communication Networks

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Airport ferry vehicles are used to transport passengers between the far apron and terminals. During the peak hours of flight arrival or departure, the demand for ferry vehicles will increase, and improper scheduling will result in significant delays. In this study, we construct a 0–1 integer programming scheduling model for ferry vehicles with multiple objectives: minimize flight delays and vehicle travel distances and balance vehicle workloads when there is an insufficient number of ferry vehicles. To solve the multiobjective model, this study selects the second version of the nondominated sorting genetic algorithm-II as the main framework and designs a modified coding and decoding scheme to solve the constraint problem in the model. The proposed model and algorithm are verified using actual data from Kunming Changshui International Airport, China. Compared with the scheduling method of manual first come first serve, the total delay time of the proposed model is reduced by 24.52%, the transit distance by 6.63%, and the balance index by 24.05%, which helps the airport solve the problem of insufficient ferry vehicles during peak hours. Comparison results show that the proposed model has a shorter calculation time and better distribution of solution sets than three state-of-the-art multiobjective optimization algorithms: MOPSO, MOACO, and SPEA-II. This research can help airports reduce flight delays, vehicle purchases, and operation costs.

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Section: Algorithmmentioning

confidence: 99%

Multiobjective Optimization of Airport Ferry Vehicle Scheduling during Peak Hours Based on NSGA-II

Ding

Xie

et al. 2022

Security and Communication Networks

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“…In [1], the authors deeply discussed the advantages of GA in solving VRPTW-related problems through existing literature. GA is a robust and effective algorithm that is computationally simple and easy to implement and adapt to different problems; GA is tested on the VRPTW benchmark (56 VRPTW instances [2]), the results of which indicate the GA remains a strong competitor to other heuristic algorithms.…”

Section: Stage Ii: Enhanced Genetic Algorithmmentioning

confidence: 99%

“…Vehicle routing problem (VRP) has been researched more and more popular over the past few years. It is widely used in the fields of city transportation [1], supplies distribution [2], and commercial logistics [3]. With global warming, the destruction of the ecological environment is becoming more and more serious, and the frequency of natural disasters is also increasing.…”

Section: Introductionmentioning

confidence: 99%

A two‐stage algorithm for vehicle routing problem with charging relief in post‐disaster

Liu

et al. 2023

IET Intelligent Trans Sys

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This paper first investigates emergency transportation for power recovery in post‐disaster. The problem is formulated as a mixed‐integer linear programming model called vehicle routing problem with charging relief (VRPCR). The battery state of charge () implies the working hours that the battery can provide. The goal is to make a set of shelters charge before the battery of shelters reaches the minimum bound over time. To this end, a two‐stage algorithm is developed to deal with the problem. In stage I, a reduced road network is obtained from a leading road network by the A‐star search algorithm. Subsequently, to determine the order of power delivery with charging operations at shelters by enhanced genetic algorithm (EGA) in stage II. To evaluate this strategy, the detailed complexity analysis of the three algorithms and results tested on a realistic disaster scenario shows the performance of the A‐star search algorithm for VRPCR that outperforms the Dijkstra and Floyd algorithms. In addition, the EGA is applied to Solomon's benchmarks compared with the state‐of‐the‐art heuristic algorithms, which indicates a better performance of EGA. A real case obtained from a disaster scenario in Ichihara City, Japan is also conducted. Simulation results demonstrate that the method can achieve satisfactory solutions.

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“…Bahri, Amor, and Talbi (2016) defined the uncertain customer requirements as a triangular fuzzy number and used the β-robustness approach to solve the multi-depot vehicle scheduling and route planning problem, with the objective to minimise the total travelled distance and total tardiness. Men et al (2020) set up an uncertain set containing 32 potential incident scenarios with transportation risk parameters and developed two versions of robust criterion to transform a hazardous material MDVRP with time windows into robust models, pursuing the balance between the number of vehicles and the transportation risk.…”

Section: Robust Mdvrpmentioning

confidence: 99%

Multi-objective robust optimisation model for MDVRPLS in refined oil distribution

Xiang

Lin

et al. 2021

International Journal of Production Research

136

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At depots with refined oil shortage, arranging a reasonable distribution scheme with limited supply affects operation costs, demand satisfaction rate of gasoline stations (hereafter, "station satisfaction"), and overtime penalty. This study considers the refined oil distribution problem with shortages using a multi-objective optimisation approach from the perspective of decision makers of oil marketing companies. The modelling and solving process involves (i) formulation of a crisp multi-depot vehicle routing model with limited supply (MDVRPLS) which considers station priority and soft time windows, (ii) development of a robust optimisation model (ROM) to manage uncertainty in demand, and (iii) the proposal of a multi-objective particle swarm optimisation (MOPSO)algorithm. Results of numerical experiments show that (i) the crisp model can better balance operation costs, station satisfaction, and overtime penalty, which produces 3.33% and 4.60% incerease in station satisfaction at an increased unit cost and overtime penalty respectively; (ii) ROM successfully addresses uncertainty in demand compared to the crisp model, which requires an additional 8.81% in cost and 12.85% in penalty; and (iii) the MOPSO manages these MDVRPLS models more effectively than other heuristic algorithms. Therefore, applying ROM of refined oil supply shortage to the management significantly improves the efficiency and resists the disturbance caused by external uncertainties, providing scope for efficient distribution of scarce resources.

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Robust multi‐objective vehicle routing problem with time windows for hazardous materials transportation

Cited by 11 publications

References 32 publications

Multiobjective Optimization of Airport Ferry Vehicle Scheduling during Peak Hours Based on NSGA-II

Multiobjective Optimization of Airport Ferry Vehicle Scheduling during Peak Hours Based on NSGA-II

A two‐stage algorithm for vehicle routing problem with charging relief in post‐disaster

Multi-objective robust optimisation model for MDVRPLS in refined oil distribution

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