A hybrid ant colony optimization algorithm for a multi-objective vehicle routing problem with flexible time windows

Zhang, Huizhen; Zhang, Qinwan; Liang, Ma; Zhang, Ziying; Liu, Yun

doi:10.1016/j.ins.2019.03.070

Cited by 168 publications

(63 citation statements)

References 49 publications

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“…The vehicle routing problem with soft time windows (where a penalty is imposed on vehicles for early or late arrivals at customer nodes) [29]- [34] and strict time windows (when vehicles cannot satisfy customer demand outside a defined arrival time window) [35]- [38] have also been addressed in the state-of-the-art. Bae and Moon [36] focused on the vehicle routing problem with strict time windows, where several depots were considered.…”

Section: B the Vehicle Routing Problemmentioning

confidence: 99%

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

Pasha

Dulebenets

Kavoosi³

et al. 2020

IEEE Access

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The "factory-in-a-box" concept involves assembling production modules (i.e., factories) in containers and transporting the containers to different customer locations. Such a concept could be highly effective during emergencies, when there is an urgent demand for products (e.g., the COVID-19 pandemic). The "factory-in-a-box" planning problem can be divided into two sub-problems. The first sub-problem deals with the assignment of raw materials to suppliers, sub-assembly decomposition, assignment of subassembly modules to manufacturers, and assignment of tasks to manufacturers. The second sub-problem focuses on the transport of sub-assembly modules between suppliers and manufacturers by assigning vehicles to locations, deciding the order of visits for suppliers, manufacturers, and customers, and selecting the appropriate routes within the transportation network. This study addresses the second sub-problem, which resembles the vehicle routing problem, by developing an optimization model and solution algorithms in order to optimize the "factory-in-a-box" supply chain. A mixed-integer linear programming model, which aims to minimize the total cost of the "factory-in-a-box" supply chain, is presented in this study. CPLEX is used to solve the model to the global optimality, while four metaheuristic algorithms, including the Evolutionary Algorithm, Variable Neighborhood Search, Tabu Search, and Simulated Annealing, are employed to solve the model for large-scale problem instances. A set of numerical experiments, conducted for a case study of "factory-in-a-box", demonstrate that the Evolutionary Algorithm outperforms the other metaheuristic algorithms developed for the model. Some managerial insights are outlined in the numerical experiments as well. INDEX TERMS Factory-in-a-box, metaheuristics, supply chains, urgent demand, vehicle routing problem.

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Section: B the Vehicle Routing Problemmentioning

confidence: 99%

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

Pasha

Dulebenets

Kavoosi³

et al. 2020

IEEE Access

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“…Because the efficiency of exact algorithms declines greatly in large-scale instances, heuristic algorithms have attracted considerable attention for large-scale instance problems; such algorithms include the ant colony algorithm [13,33], tabu search algorithm [15], genetic algorithm [23] and others. Arnold et al [1] and Triki et al [27] designed heuristic algorithms for their specific large-scale mathematical problems to obtain improved feasible solutions.…”

Section: Related Workmentioning

confidence: 99%

“…Arnold et al [1] and Triki et al [27] designed heuristic algorithms for their specific large-scale mathematical problems to obtain improved feasible solutions. Zhang et al [33] proposed a multi-objective solution strategy based on the ant colony algorithm and three mutation operators to solve the multi-objective vehicle routing problem with flexible time windows. The performance of the proposed approach was evaluated on Solomon benchmark instances, and experimental results show that the suggested approach is comparative to the best known results in the literature.…”

Section: Related Workmentioning

confidence: 99%

A Pointer Neural Network for the Vehicle Routing Problem with Task Priority and Limited Resources

Sheng

Wei

2020

ITC

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The vehicle routing problem with task priority and limited resources (VRPTPLR) is a generalized version of the vehicle routing problem (VRP) with multiple task priorities and insufficient vehicle capacities. The objective of this problem is to maximize the total benefits. Compared to the traditional mathematical analysis methods, the pointer neural network proposed in this paper continuously learns the mapping relationship between input nodes and output decision schemes based on the actual distribution conditions. In addition, a global attention mechanism is adopted in the neural network to improve the convergence rate and results. To verify the effectiveness of the method, we model the VRPTPLR and compare the results with those of a genetic algorithm. The parameter sensitivity of each algorithm is assessed using different datasets. Then, comparison experiments with the two algorithms employing optimal parameter configurations are performed for the validation sets, which are generated at different instance scales. It is found that the solution time of the pointer neural network is much shorter than that of the genetic algorithm and that the proposed method provides better solutions for large-scale instances.

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“…Machine learning (ML) [8] aims at developing automated ways for computers to learn a specific task from a given set of data samples. ML has several applications in image classification [9], NP-hard problems [10] and others. Some of the limitations of ML are the lack of algorithms to select an ML method and to avoid local minimums.…”

Section: Introductionmentioning

confidence: 99%

Parametric Probabilistic Quantum Memory

et al. 2020

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Probabilistic Quantum Memory (PQM) is a data structure that computes the distance from a binary input to all binary patterns stored in superposition on the memory. This data structure allows the development of heuristics to speed up artificial neural networks architecture selection. In this work, we propose an improved parametric version of the PQM to perform pattern classification, and we also present a PQM quantum circuit suitable for Noisy Intermediate Scale Quantum (NISQ) computers. We present a classical evaluation of a parametric PQM network classifier on public benchmark datasets. We also perform experiments to verify the viability of PQM on a 5-qubit quantum computer.

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A hybrid ant colony optimization algorithm for a multi-objective vehicle routing problem with flexible time windows

Cited by 168 publications

References 49 publications

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

A Pointer Neural Network for the Vehicle Routing Problem with Task Priority and Limited Resources

Parametric Probabilistic Quantum Memory

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