A particle swarm optimization algorithm with crossover for vehicle routing problem with time windows

Jiang, Weigang; Zhang, Yuanbiao; Xie, Jianwen

doi:10.1109/scis.2009.4927022

Cited by 13 publications

(8 citation statements)

References 7 publications

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“…Since most green problems are belonging of multi-objective optimization, PSO can be employed in particular problems. Although in multinomial function particle populations will quickly lose diversity and PSO will cause premature convergence and trap to local front rather than global ones [35]- [37], some researchers proposed some operators (like crossover) or hybridize by other algorithm to overcome partly the problem [35], [38]- [40]. Liu et al [38] and Masrom et al [39] hybridized PSO and GA to solve vehicle routing problem as a nonlinear transportation problem which the final algorithms can be used for logistics network optimization in GrSC problems.…”

Section: A Particle Swarm Optimizationmentioning

confidence: 99%

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

Sadrnia¹,

Soltani²,

Zulkifli³

et al. 2014

IJET

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Abstract-In the recent years, there is a significant attention among researchers and practitioners to environmental issues and green supply chain (GrSC) because of legislations and profit oriented motivations.Because of considering environmental issues GrSC besides the economic variables, the models are very difficult to find optimal. Hence, complicated green problems call for development of modern optimization methods and algorithms to solve optimization models by efficient techniques. In recent decades, meta-heuristic algorithms have been developed to overcome the problem that most of them are inspired from nature. Some of the algorithms have been inspired from natural generation, some of them inspired from swarm behavior, and others simulate natural processes. In this research we summarize the recent advance evolutionary optimization algorithms and swarm intelligence algorithms which are applied to GrSC and green logistics. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.Index Terms-Evolutionary algorithms, green supply chain, meta-heuristic, swarm intelligence algorithms.

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Section: A Particle Swarm Optimizationmentioning

confidence: 99%

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

Sadrnia¹,

Soltani²,

Zulkifli³

et al. 2014

IJET

View full text Add to dashboard Cite

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“…Hard time window is that each task must be completed within the specific time. However, soft time window is that if a task can not be completed within the specific time, given a punishment [9]. VRPTW can be generally described as: given a logistics distribution center O , own K vehicles, the capacity of each vehicle is denoted ( 1,2, , ) …”

Section: Description and Mathematic Modelmentioning

confidence: 99%

Research on Improved Hybrid Particle Swarm Optimization for Vehicle Routing Problem with Time Windows

Wang

Fan

et al. 2010

2010 International Conference on Artificial Intelligence and Computational Intelligence

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Vehicle routing problem with time window (VRPTW) is a very important combinatorial optimization and nonlinear programming problem and it has important practical value in the field of transportation, distribution and logistics. Traditional intelligent optimization algorithms have the common defects of early convergence and easily falling into local optimal solution in solving VRPTW. An improved hybrid particle swarm optimization (IHPSO) has been presented in this paper for the solution to the VRPTW. Population entropy is introduced to ensure the diversities of the solution. Some post-optimization procedures, such 2-opt, 1-1 opt and 1-0 opt, are adopted to optimize the inner or router route and modify illegal solutions. The advantage of the proposed algorithm is proved by the simulation based on Matlab language. The results shows that IHPSO can be dramatically increase the probability of the finding the optimal solution as well as shorted the searching time. It is a feasible and effective algorithm for VRPTW.

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“…Since the 1990's, some artificial intelligence methods have been suggested, such as the genetic algorithm (GA), Tabu Search, Simulated Annealing and so on [3][4][5][6][7]. Recently, Ant colony optimization and particle swarm optimization has been a popular approach used to solve some VRP problems [8].…”

Section: Introductionmentioning

confidence: 99%

“…The search progress is strictly stochastic. Particle swarm optimization PSO is another evolutionary method that was developed by Kennedy and Elberhart in 1995 [9]. PSO is an example of a meta-heuristic algorithm.…”

Section: Introductionmentioning

confidence: 99%

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A PSO-GA method to solve a partial shipment and scheduling problem

Gang¹

2010

2010 International Conference on Computer Application and System Modeling (ICCASM 2010)

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The partial shipment in an inbound transportation process is considered in this paper, in which a given collection of components from a list must be delivered from several possible providers (origins) to one plant (destination). The partial shipment and scheduling problem can be reduced to a vehicle routing problem (VRP) with a variable size fleet of trucks. Based on actual logistics data and business process, a new optimization objective is defined in this paper. A genetic algorithm (GA) with a particle swarm optimization (PSO) operator is proposed to solve the optimization problem. Finally, a series of numerical quantifications are analyzed to illustrate the advantages of the proposed algorithm.

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A particle swarm optimization algorithm with crossover for vehicle routing problem with time windows

Cited by 13 publications

References 7 publications

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

Research on Improved Hybrid Particle Swarm Optimization for Vehicle Routing Problem with Time Windows

A PSO-GA method to solve a partial shipment and scheduling problem

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