A modified variable neighborhood search hybridized with genetic algorithm for vehicle routing problems with cross-docking

Baniamerian, Ali; Bashiri, Mahdi; Zabihi, Fahime

doi:10.1016/j.endm.2018.03.019

Cited by 12 publications

(5 citation statements)

References 5 publications

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“…(Larioui et al, 2020) extended the one-to-one correspondence between suppliers and customers by allowing customers to order from more than one supplier and compared the results within a few solution methods. A hybrid method was applied to a variant of VRPCD that maximizes the total profit of the CD system in the study of Baniamerian et al (2018a). Baniamerian et al (2018b) considered the customer satisfaction factor for VRPCD with time windows.…”

Section: Review Of Literaturementioning

confidence: 99%

An Optimization Model for the Hard Time Windows Vehicle Routing Problem with Moving Shipments at the Cross Dock Center

Gnanapragasam,

Daundasekera

2023

OUSL Jnl

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Cross-Docking (CD) technique was initiated in the 1930s to make a cost-effective supply chain. Vehicle Routing Problem (VRP) is one of the widely discussed optimization problems. The research on integration of VRP with CD (VRPCD) was initiated at the beginning of 2000s. Moving Shipments (MS) from receiving doors to shipping doors is an activity inside a Cross-Dock Centre (CDC). This study mainly considers MS as an additional aspect in the literature of VRPCD. In this study, not only loading or unloading shipments at all the nodes including CDC and homogenous fleets of vehicles within pickup or delivery process are considered, but also aspects of heterogeneous fleets of vehicles between pickup and delivery processes are considered. Furthermore, Time Windows (TW) characteristics are also considered here. A mixed integer nonlinear programming model is developed to obtain the optimal solutions to hard time windows vehicle routing problem with moving shipments at the cross-dock centre (TW-VRPCD-MS). The compatibility of the proposed model is tested using sixteen randomly generated small scale instances. Since the average computational time is reasonably less for the tested instances, it can be concluded that this proposed model can be used for last time planning for similar small-scale problems. Further analysis revealed that the convergence rate to reach the optimal solution rises exponentially with the scale of the problem. Therefore, this study recommends in applying heuristic or metaheuristic techniques to solve large scale instances of TW-VRPCD-MS to obtain a near optimal solution in a reasonable computational time.

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Section: Review Of Literaturementioning

confidence: 99%

An Optimization Model for the Hard Time Windows Vehicle Routing Problem with Moving Shipments at the Cross Dock Center

Gnanapragasam,

Daundasekera

2023

OUSL Jnl

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“…Experiments were carried out on different instances and found good performance of the algorithm. A novel hybrid algorithm by combining the GA and modified VNS (MVNS) for the VRP with cross-docking (VRPCD) is proposed in [22]. To prove the usefulness of the hybrid algorithm, a comparative study is carried out on some problem instances.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Genetic Algorithms for the Asymmetric Distance-Constrained Vehicle Routing Problem

Ahmed

Hameed

Mutar

2022

Mathematical Problems in Engineering

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We aim to suggest a simple genetic algorithm (GA) and other four hybrid GAs (HGAs) for solving the asymmetric distance-constrained vehicle routing problem (ADVRP), a variant of vehicle routing problem (VRP). The VRP is a difficult NP-hard optimization problem that has numerous real-life applications. The VRP aims to find an optimal tour that has least total distance (or cost) to provide service to n customers (or nodes or cities) utilizing m vehicles so that every vehicle starts journey from and ends journey at a depot (headquarters) and visits every customer only once. The problem has many variations, and we consider the ADVRP for this study, where distance traveled by every vehicle must not exceed a predefined maximum distance. The proposed GA uses random initial population followed by sequential constructive crossover and swap mutation. The HGAs enhance the initial solution using 2-opt search method and incorporate a local search technique along with an immigration procedure to obtain effective solution to the ADVRP. Experiments have been conducted among the suggested GAs by solving several restricted and unrestricted ADVRP instances on asymmetric TSPLIB utilizing several vehicles. Our experiments claim that the suggested HGAs using local search methods are very effective. Finally, we reported a comparative study between our best HGA and a state-of-the-art algorithm on asymmetric capacitated VRP and found that our algorithm is better than the state-of-the-art algorithm for the instances.

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“…In these circumstances, the main aim of this study is to propose a trustworthy algorithm that can be used to efficiently solve the SALBPs. Given the promising performance of GAs in dealing with a variety of combinatorial optimization problems (Baniamerian et al , 2018), an improved GA (IGA) is proposed in this study. The IGA benefits from a GT mechanism improving its diversification and a VNS enhancing its intensification.…”

Section: Literature Reviewmentioning

confidence: 99%

An improved genetic algorithm with variable neighborhood search to solve the assembly line balancing problem

Fathi

Nourmohammadi

et al. 2019

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Purpose This study aims to propose an efficient optimization algorithm to solve the assembly line balancing problem (ALBP). The ALBP arises in high-volume, lean production systems when decision-makers aim to design an efficient assembly line while satisfying a set of constraints. Design/methodology/approach An improved genetic algorithm (IGA) is proposed in this study to deal with ALBP to optimize the number of stations and the workload smoothness. Findings To evaluate the performance of the IGA, it is used to solve a set of well-known benchmark problems and a real-life problem faced by an automobile manufacturer. The solutions obtained are compared against two existing algorithms in the literature and the basic genetic algorithm. The comparisons show the high efficiency and effectiveness of the IGA in dealing with ALBPs. Originality/value The proposed IGA benefits from a novel generation transfer mechanism that improves the diversification capability of the algorithm by allowing population transfer between different generations. In addition, an effective variable neighborhood search is used in the IGA to enhance its local search capability.

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A modified variable neighborhood search hybridized with genetic algorithm for vehicle routing problems with cross-docking

Cited by 12 publications

References 5 publications

An Optimization Model for the Hard Time Windows Vehicle Routing Problem with Moving Shipments at the Cross Dock Center

An Optimization Model for the Hard Time Windows Vehicle Routing Problem with Moving Shipments at the Cross Dock Center

Hybrid Genetic Algorithms for the Asymmetric Distance-Constrained Vehicle Routing Problem

An improved genetic algorithm with variable neighborhood search to solve the assembly line balancing problem

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