Vehicle routing with time windows: An overview of exact, heuristic and metaheuristic methods

El-Sherbeny, Nasser A.

doi:10.1016/j.jksus.2010.03.002

Cited by 219 publications

(90 citation statements)

References 36 publications

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“…(10) specifies that, if the vehicle k is travelling from customer i to j, the vehicle cannot arrive at customer j before s kj + s ij . As specified by El-Sherbeny [19], the variable s kj corresponds s to the time the vehicle k starts to service the customer j. If the vehicle k does not service j, s kj is not calculated.…”

Section: Vehicle Routing Problem With Time Windowsmentioning

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: Vehicle Routing Problem With Time Windowsmentioning

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

“…As the problem belongs to NP-hard problems, heuristics are most frequently used to handle a large number of vehicle routing and scheduling variants for DRTs [29][30][31]. Some scholars have tried to develop route-building heuristics by first generating a set of feasible routes, before searching fine-tuned initial solutions [3,4,28,32].…”

Section: Literaturementioning

confidence: 99%

Optimal Design of Demand-Responsive Feeder Transit Services with Passengers’ Multiple Time Windows and Satisfaction

2018

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This paper presents a mixed-integer linear programming model for demand-responsive feeder transit services to assign vehicles located at different depots to pick up passengers at the demand points and transport them to the rail station. The proposed model features passengers' one or several preferred time windows for boarding vehicles at the demand point and their expected ride time. Moreover, passenger satisfaction that was related only to expected ride time is fully accounted for in the model. The objective is to simultaneously minimize the operation costs of total mileage and maximize passenger satisfaction. As the problem is an extension of the nondeterministic polynomial problem with integration of the vehicle route problem, this study further develops an improved bat algorithm to yield meta-optimal solutions for the model in a reasonable amount of time. When this was applied to a case study in Nanjing City, China, the mileage and satisfaction of the proposed model were reduced by 1.4 km and increased by 7.1%, respectively, compared with the traditional model. Sensitivity analyses were also performed to investigate the impact of the number of designed bus routes and weights of objective functions on the model performance. Finally, a comparison of Cplex, standard bat algorithm, and group search optimizer is analyzed to verify the validity of the proposed algorithm.

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“…They encompass branchand-cut (Bard et al 2002), and branch-cut-and-price procedures (Abdallah and Jang 2014), algorithms utilizing many different problem formulations (Kolen et al 1987;Feillet et al 2004;Larsen 2004;Chabrier 2006;Righini and Salani 2006;Baldacci et al 2011), and adopting other problems for the VRPTW (Irnich and Villeneuve 2006). Exact methods were summarized in thorough surveys and reviews (Cordeau et al 2002;Kallehauge 2008;El-Sherbeny 2010;Baldacci et al 2012). Although exact algorithms are continuously being developed, they are still not applicable to large real-life VRPTW instances due to their execution time.…”

Section: Vehicle Routing Problem With Time Windowsmentioning

confidence: 99%

Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows

Nalepa

Błocho²

2015

Soft Comput

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This paper presents an adaptive memetic algorithm to solve the vehicle routing problem with time windows (VRPTW). It is a well-known NP-hard discrete optimization problem with two objectives-to minimize the number of vehicles serving a set of geographically dispersed customers, and to minimize the total distance traveled in the routing plan. Although memetic algorithms have been proven to be extremely efficient in solving the VRPTW, their main drawback is an unclear tuning of their numerous parameters. Here, we introduce the adaptive memetic algorithm (AMA-VRPTW) for minimizing the total travel distance. In AMA-VRPTW, a population of solutions evolves with time. The parameters of the algorithm, including the selection scheme, population size and the number of child solutions generated for each pair of parents, are adjusted dynamically during the search. We propose a new adaptive selection scheme to balance the exploration and exploitation of the solution space. Extensive experimental study performed on the well-known Solomon's and Gehring and Homberger's benchmark sets confirms the efficacy and convergence capabilities of the proposed AMA-VRPTW. We show that it is very competitive compared with other state-of-the-art techniques. Finally, the influence of the proposed adaptive schemes on the AMA-VRPTW behavior and performance is investigated in a thorough sensitivity analysis. This analysis is complemented with

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Vehicle routing with time windows: An overview of exact, heuristic and metaheuristic methods

Cited by 219 publications

References 36 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

Optimal Design of Demand-Responsive Feeder Transit Services with Passengers’ Multiple Time Windows and Satisfaction

Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows

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