Integrated scheduling problem of multi-load AGVs and parallel machines considering the recovery process

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2025-02-03 DOI:10.1016/j.swevo.2025.101861

Xin Fan , Hongyan Sang , Mengxi Tian , Yang Yu , Song Chen

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引用次数: 0

Abstract

In modern manufacturing workshops, parallel machine scheduling and automated guided vehicle (AGV) scheduling are two closely coupled problems. However, the two problems are often solved independently, which reduces the performance of manufacturing system to a large extent. To address this issue, this paper investigates the integrated scheduling problem of multi-load AGV and parallel machine considering the recovery process (MAGVPM-R). Firstly, a mathematical model is established to optimize the completion time. Second, a weight priority integration heuristic (WPIH) and four neighborhood operators are designed based on MAGVPM-R characteristics. Third, a discrete grey wolf optimization (DGWO) algorithm is proposed. Finally, the mathematical model is validated using the GUROBI solver and the performance of DGWO is tested with 100 instances of different scales. The experimental results show that DGWO solves the MAGVPM-R problem better than other competing algorithms.

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.