A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery

Goksal, Fatma Pinar; Karaoğlan, İsmail; Altıparmak, Fulya

doi:10.1016/j.cie.2012.01.005

Cited by 233 publications

(115 citation statements)

References 44 publications

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“…Baños et al [16] introduced a hybrid meta-heuristic for multi-objective vehicle routing problems with time windows. To solve vehicle routing problem with simultaneous pickup and delivery, Goksal et al [17] described a hybrid discrete particle swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Routing Problem with Time Windows Using Multi-Objective Co-Evolutionary Approach

Wu¹,

Dong²,

Li³

2016

Int. j. simul. model.

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This paper introduces a novel multi-objective algorithm (HMPSO) based on discrete particle swarm optimization (PSO) to solve vehicle routing problems with time windows (VRPTW). The presented HMPSO algorithm was combined with an advanced discrete PSO based on set and variable neighbourhood searches to find Pareto optimal routing solutions. These consisted of a complete routing schedule for serving the customers to minimize the two aims of travelling distance and number of vehicles. To increase the discrete PSO efficiency, a novel decoding scheme based on set was designed, and the variable neighbourhood local search was employed to explore new solutions. The experiment results were showed for a set of the Solomon's 56 VRPTW. The HMPSO algorithm was compared with some algorithms published in papers with the computational evaluations clearly supporting the high performance of the proposed HMPSO algorithm against other algorithms, and confirming that the HMPSO is an efficient algorithm because of a reasonable computational time and cost in solve VRPTW.

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Section: Introductionmentioning

confidence: 99%

Vehicle Routing Problem with Time Windows Using Multi-Objective Co-Evolutionary Approach

Wu¹,

Dong²,

Li³

2016

Int. j. simul. model.

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show abstract

“…At the same time, an annealing strategy was used to maintain the diversity of population. The effectiveness of the algorithm was verified by experiments, and the optimal solution was obtained by using the improved ant colony algorithm [27]. Combined with a real-life application, Goel and Gruhn [28] studied the rich VRP.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To minimise the total cost, corresponding constraints were set and this model algorithm was used to solve various problem instances, which further verified the effectiveness of the algorithm [26]. In order to generate an effective solution way to fix the VRPSPD, which was known as an NP-hard problem, Goksal et al [27] proposed an approach which was a solution based on particle swarm optimisation algorithm. At the same time, an annealing strategy was used to maintain the diversity of population.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of an Automated Vehicle Routing Problem in Logistics considering Path Interruption

Zhang

Shi

Chen

et al. 2017

Journal of Advanced Transportation

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The application of automated vehicles in logistics can efficiently reduce the cost of logistics and reduce the potential risks in the last mile. Considering the path restriction in the initial stage of the application of automated vehicles in logistics, the conventional model for a vehicle routing problem (VRP) is modified. Thus, the automated vehicle routing problem with time windows (AVRPTW) model considering path interruption is established. Additionally, an improved particle swarm optimisation (PSO) algorithm is designed to solve this problem. Finally, a case study is undertaken to test the validity of the model and the algorithm. Four automated vehicles are designated to execute all delivery tasks required by 25 stores. Capacities of all of the automated vehicles are almost fully utilised. It is of considerable significance for the promotion of automated vehicles in last-mile situations to develop such research into real problems arising in the initial period.

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“…In the literature, vehicle routing problems can be structured differently according to various constraints, such as capacitated VRP [120][121][122][123][124][125][126], the VRP with heterogeneous fleet of vehicles [127], open VRP [128][129][130][131], VRP with time windows [132][133][134][135][136][137][138], VPR with both pick-up and delivery [139][140][141][142][143][144], location routing problem [145][146][147], period VRP [148][149][150][151], multidepot VRP [152][153][154][155][156], VRP with backhaul [157][158][159], inventory routing problem [160][161][162][163], dynamic VRP [164], or the mixture [165][166]…”

Section: Vrpmentioning

confidence: 99%

A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

Fei

Shen

2017

Scientific Programming

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Over the past decades, optimization in operations management has grown ever more popular not only in the academic literature but also in practice. However, the problems have varied a lot, and few literature reviews have provided an overview of the models and algorithms that are applied to the optimization in operations management. In this paper, we first classify crucial optimization areas of operations management from the process point of view and then analyze the current status and trends of the studies in those areas. The purpose of this study is to give an overview of optimization modelling and resolution approaches, which are applied to operations management.

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A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery

Cited by 233 publications

References 44 publications

Vehicle Routing Problem with Time Windows Using Multi-Objective Co-Evolutionary Approach

Vehicle Routing Problem with Time Windows Using Multi-Objective Co-Evolutionary Approach

Analysis of an Automated Vehicle Routing Problem in Logistics considering Path Interruption

A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

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