A simple and effective evolutionary algorithm for the capacitated location–routing problem

Lopes, Rui Borges; Ferreira, Carlos; Santos, Beatriz Sousa

doi:10.1016/j.cor.2016.01.006

Cited by 76 publications

(41 citation statements)

References 29 publications

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“…Our algorithm obtains similar results with SALRP and performs much better than GRASP, MAPM, LRGTS and GRASP+ELS. As CPU times depend on many factors (such as computers and programming languages used) [43], the proposed method seems faster than SALRP, ALNS and GRASP+ILP.…”

Section: Tests Based On Benchmark Instancementioning

confidence: 99%

“…Moreover, considering the most recent and effective methods in the literature, the performance comparison is provided in Table 3 based on the CPU and Best Gap/BKR. The compared algorithms are GRASP in Prins et al [49], MAPM in Prins et al [39], LRGTS in Prins et al [50], GRASP+ELS in Duhamel et al [36], SALRP in Vincent et al [35], ALNS in reference [51], MACO in [32], GRASP+ILP in Contardo et al [52], GVTNS in Escobar et al [38] and Hybrid GA in Lopes et al [43]. Among those compared algorithms, GRASP+ILP is on average the most effective on this set of benchmark instances followed by ALNS, GVTNS, MACO and SALRP.…”

Section: Tests Based On Benchmark Instancementioning

confidence: 99%

“…In this method, GA is used to locate the facilities in the first level and the VRP in the second level is solved by expanding neighborhood search (ENS). Most recently, Lopes et al [43] proposed a simple and effective hybrid GA with LS procedure that works as a mutation operator.…”

Section: Introductionmentioning

confidence: 99%

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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: Tests Based On Benchmark Instancementioning

confidence: 99%

Section: Tests Based On Benchmark Instancementioning

confidence: 99%

See 1 more Smart Citation

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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show abstract

“…Genetic algorithm is hybridised with local search procedure. Similar approach has recently been applied by Lopes et al [18] that also combines genetic algorithm with local search.…”

Section: Solution Methodsmentioning

confidence: 98%

Genetic Algorithm for the Continuous Location-Routing Problem

Rybickova¹,

Mockova²,

Teichmann³

2019

NNW

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This paper focuses on the continuous location-routing problem that comprises of the location of multiple depots from a given region and determining the routes of vehicles assigned to these depots. The objective of the problem is to design the delivery system of depots and routes so that the total cost is minimal. The standard location-routing problem considers a finite number of possible locations. The continuous location-routing problem allows location to infinite number of locations in a given region and makes the problem much more complex. We present a genetic algorithm that tackles both location and routing subproblems simultaneously.

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“…A good recent review on it can be found in Prodhon and Prins (2014), but several efforts have been published by Lopes et al (2016), Laporte (1988), Gao et al (2016), Zhalechian et al (2016), Min et al (1998). For detailed information about classification for the LRP; Laporte (1988) is the first researcher who classifies the LRP models.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving a bi-objective mathematical model for location-routing problem with time windows in multi-echelon reverse logistics using metaheuristic procedure

Ghezavati

Beigi

2016

J Ind Eng Int

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During the last decade, the stringent pressures from environmental and social requirements have spurred an interest in designing a reverse logistics (RL) network. The success of a logistics system may depend on the decisions of the facilities locations and vehicle routings. The location-routing problem (LRP) simultaneously locates the facilities and designs the travel routes for vehicles among established facilities and existing demand points. In this paper, the location-routing problem with time window (LRPTW) and homogeneous fleet type and designing a multi-echelon, and capacitated reverse logistics network, are considered which may arise in many real-life situations in logistics management. Our proposed RL network consists of hybrid collection/inspection centers, recovery centers and disposal centers. Here, we present a new bi-objective mathematical programming (BOMP) for LRPTW in reverse logistic. Since this type of problem is NP-hard, the non-dominated sorting genetic algorithm II (NSGA-II) is proposed to obtain the Pareto frontier for the given problem. Several numerical examples are presented to illustrate the effectiveness of the proposed model and algorithm. Also, the present work is an effort to effectively implement the e-constraint method in GAMS software for producing the Pareto-optimal solutions in a BOMP. The results of the proposed algorithm have been compared with the e-constraint method. The computational results show that the e-constraint method is able to solve small-size instances to optimality within reasonable computing times, and for medium-to-large-sized problems, the proposed NSGA-II works better than the e-constraint.

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A simple and effective evolutionary algorithm for the capacitated location–routing problem

Cited by 76 publications

References 29 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

Genetic Algorithm for the Continuous Location-Routing Problem

Solving a bi-objective mathematical model for location-routing problem with time windows in multi-echelon reverse logistics using metaheuristic procedure

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