Truck and Trailer Routing Problem under fuzzy environment

Torres, Isis; Rosete, Alejandro; Cruz, Carlos; Verdegay, José L.

doi:10.2991/ifsa-eusflat-15.2015.168

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Derigs et al (2013) also developed a two-phase approach, solving the TTRP problem through combing local search with the large neighborhood search (LNS) meta-heuristic. Torres et al (2013Torres et al ( , 2015 considered the TTRP with fuzzy constraints, which incorporated the uncertainty, and developed a fuzzy parametric approach to solve this problem. Parragh and Cordeau (2015) presented a two-phase approach for TTRP.…”

Section: Ec 351mentioning

confidence: 99%

A self-adaptive bat algorithm for the truck and trailer routing problem

Wang

Zhou

Gao

et al. 2018

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Purpose The purpose of this paper is to provide an effective solution method for the truck and trailer routing problem (TTRP) which is one of the important NP-hard combinatorial optimization problems owing to its multiple real-world applications. It is a generalization of the famous vehicle routing problem (VRP), involving a group of geographically scattered customers served by the vehicle fleet including trucks and trailers. Design/methodology/approach The meta-heuristic solution approach based on bat algorithm (BA) in which a local search procedure performed by five different neighborhood structures is developed. Moreover, a self-adaptive (SA) tuning strategy to preserve the swarm diversity is implemented. The effectiveness of the proposed SA-BA is investigated by an experiment conducted on 21 benchmark problems that are well known in the literature. Findings Computational results indicate that the proposed SA-BA algorithm is computationally efficient through comparison with other existing algorithms found from the literature according to solution quality. As for the actual computational time, the SA-BA algorithm outperforms others. However, the scaled computational time of the SA-BA algorithm underperforms the other algorithms. Originality/value In this work the authors show that the proposed SA-BA is effective as a method for the TTRP problem. To the authors’ knowledge, the BA has not been applied previously, as in this work, to solve the TTRP problem.

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Section: Ec 351mentioning

confidence: 99%

A self-adaptive bat algorithm for the truck and trailer routing problem

Wang

Zhou

Gao

et al. 2018

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“…Batsyn and Ponomarenko [10] discussed a sitedependent heterogeneous fleet truck and trailer routing 2 Mathematical Problems in Engineering problem, treating soft and hard time windows separately, based on the sequential greedy insertion algorithm. Torres et al [11] proposed a model based on the soft computing method to solve the TTRP, under which a decision-maker may be permitted to violate some constraints. Some scholars have introduced the organization mode of container transportation to the TTRP problem.…”

Section: Introductionmentioning

confidence: 99%

Container Swap Trailer Transportation Routing Problem Based on Genetic Algorithm

Tao

2018

Mathematical Problems in Engineering

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In swap trailer transportation routing problems, trucks and trailers conduct swap operations at special positions called trailer points. The parallelization of stevedoring and transportation can be achieved by means of these trailer points. This logistics organization mode can be more effective than the others. In this paper, an integer programming model with capacity and timewindow constraints was established. A repairing strategy is embedded in the genetic algorithm (GA) to solve the model. The repairing strategy is executed after the crossover and mutation operation to eliminate the illegal routes. Furthermore, a parameter self-adaptive adjustment policy is designed to improve the convergence. Then numerical experiments are implemented based on the generated datasets; the performance and robustness of the algorithm parameter self-adaptive adjustment policy are discussed. Finally, the results show that the improved algorithm performs better than elementary GA.

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