A Hybrid Genetic Algorithm for Multidepot and Periodic Vehicle Routing Problems

Vidal, Thibaut; Crainic, Teodor Gabriel; Gendreau, Michel; Lahrichi, Nadia; Rei, Walter

doi:10.1287/opre.1120.1048

Cited by 539 publications

(504 citation statements)

References 68 publications

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“…Current widely used objective function-based individual evolution may shrink the search space and lead to premature convergence. In this study, we revise the biased fitness function proposed by Vidal et al [46]. In this method, the objective value of a solution and its contribution to population diversity are jointly considered.…”

Section: Individual Evolution and Selectionmentioning

confidence: 99%

Location-Routing Problem with Simultaneous Home Delivery and Customer’s Pickup for City Distribution of Online Shopping Purchases

Zhou

Wang

et al. 2016

Sustainability

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Abstract:With the increasing interest in online shopping, the Last Mile delivery is regarded as one of the most expensive and pollutive-and yet the least efficient-stages of the e-commerce supply chain. To address this challenge, a novel location-routing problem with simultaneous home delivery and customer's pickup is proposed. This problem aims to build a more effective Last Mile distribution system by providing two kinds of service options when delivering packages to customers. To solve this specific problem, a hybrid evolution search algorithm by combining genetic algorithm (GA) and local search (LS) is presented. In this approach, a diverse population generation algorithm along with a two-phase solution initialization heuristic is first proposed to give high quality initial population. Then, advantaged solution representation, individual evaluation, crossover and mutation operations are designed to enhance the evolution and search efficiency. Computational experiments based on a large family of instances are conducted, and the results obtained indicate the validity of the proposed model and method.

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Section: Individual Evolution and Selectionmentioning

confidence: 99%

Location-Routing Problem with Simultaneous Home Delivery and Customer’s Pickup for City Distribution of Online Shopping Purchases

Zhou

Wang

et al. 2016

Sustainability

View full text Add to dashboard Cite

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“…For this reason, we decided to treat infeasibility indirectly and to use a hierarchical penalized cost function to allow the local search to enter the infeasible region. Similar approaches for the control exploration of feasible and infeasible regions are widely used, and as reported in Vidal et al (2012), they are found to enhance the performance of the search process. Finally, it is worth to note that the proposed mechanisms for selecting solution components of varying size generalize those of earlier AMP heuristics applied for the deterministic CVRP in Tarantilis and Kiranoudis (2002) and Tarantilis (2005), while they provide more consistent performance.…”

Section: Article Submitted Tomentioning

confidence: 89%

An Adaptive Memory Programming Framework for the Robust Capacitated Vehicle Routing Problem

Gounaris

Repoussis

Tarantilis

et al. 2016

Transportation Science

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We present an Adaptive Memory Programming (AMP) metaheuristic to address the Robust Capacitated Vehicle Routing Problem under demand uncertainty. Contrary to its deterministic counterpart, the robust formulation allows for uncertain customer demands, and the objective is to determine a minimum cost delivery plan that is feasible for all demand realizations within a prespecified uncertainty set. A crucial step in our heuristic is to verify the robust feasibility of a candidate route. For generic uncertainty sets, this step requires the solution of a convex optimization problem, which becomes computationally prohibitive for large instances. We present two classes of uncertainty sets for which route feasibility can be established much more efficiently. While we discuss our implementation in the context of the AMP framework, our techniques readily extend to other metaheuristics. Computational studies on standard literature benchmarks with up to 483 customers and 38 vehicles demonstrate that the proposed approach is able to quickly provide high quality solutions. In the process, we obtain new best solutions for a total of 123 benchmark instances.

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“…In [20], the authors propose an approach based on metaheuristic method, which combines a hybrid genetic algorithm for research and adaptive control for diversification to solve the multi-depot and periodic vehicle routing problems. In the work of [18], the authors present two metaheuristics for the resolution of the Multi depot Vehicle Routing Problem (MDVRP) which are based on a local research and a hybrid genetic algorithm.…”

Section: Genetic Algorithm For the Vehicle Routing Problem And The Pimentioning

confidence: 99%

A Comparative Study of the PSO and GA for the m-MDPDPTW

Alaïa

Dridi

Borne³

et al. 2018

INT J COMPUT COMMUN

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Abstract:The m-MDPDPTW is the multi-vehicles, multi-depots pick-up and delivery problem with time windows. It is an optimization vehicles routing problem which must meet requests for transport between suppliers and customers for the purpose of satisfying precedence, capacity and time constraints. This problem is a very important class of operational research, which is part of the category of NP-hard problems. Its resolution therefore requires the use of evolutionary algorithms such as Genetic Algorithms (GA) or Particle Swarm Optimization (PSO). We present, in this sense, a comparative study between two approaches based respectively on the GA and the PSO for the optimization of m-MDPDPTW. We propose, in this paper, a literature review of the Vehicle Routing Problem (VRP) and the Pick-up and Delivery Problem with Time Windows (PDPTW), present our approaches, whose objective is to give a satisfying solution to the m-MDPDPTW minimizing the total distance travelled. The performance of both approaches is evaluated using various sets instances from [10] PDPTW benchmark data problems. From our study, in the case of m-MDPDPTW problem, the proposed GA reached to better results compared with the PSO algorithm and can be considered the most appropriate model to solve our m-MDPDPTW problem.

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A Hybrid Genetic Algorithm for Multidepot and Periodic Vehicle Routing Problems

Cited by 539 publications

References 68 publications

Location-Routing Problem with Simultaneous Home Delivery and Customer’s Pickup for City Distribution of Online Shopping Purchases

Location-Routing Problem with Simultaneous Home Delivery and Customer’s Pickup for City Distribution of Online Shopping Purchases

An Adaptive Memory Programming Framework for the Robust Capacitated Vehicle Routing Problem

A Comparative Study of the PSO and GA for the m-MDPDPTW

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