Hao Chi, Hong-Yan Sang, Biao Zhang, Peng Duan, Wen-Qiang Zou
{"title":"BDE-Jaya:矩阵制造车间多辆自动导航车调度问题的二元离散增强型 Jaya 算法","authors":"Hao Chi, Hong-Yan Sang, Biao Zhang, Peng Duan, Wen-Qiang Zou","doi":"10.1016/j.swevo.2024.101651","DOIUrl":null,"url":null,"abstract":"<div><p>With the advent of \"Industry 4.0\", more matrix manufacturing workshops have adopted automated guided vehicle (AGV) for material handling. AGV transportation has become a key link in manufacturing production. Traditional AGVs scheduling problem (AGVSP) is studied in depth. However, most research overlooks an important problem, in production with limited resources, the number of AGVs is insufficient. Therefore, the wait time of workstations is longer than expected. The service time of the task is delayed and the cost is increased. To solve above problem, this paper proposes the binary discrete enhanced Jaya (BDE-Jaya) algorithm. The main goal is to minimize transportation cost, including AGV traveling cost, service early penalty, and total tardiness (TTD). A key-task shift method is proposed to reduce TTD and task service early penalty. Two heuristics based on the problem features are designed to generate the initial solution. In the evolutionary stage, three offspring generation methods are used to improve the algorithm exploitation capability and exploration capability. Then, an insertion-based repair method is designed to prevent the exploitation process falling into local optimum. Furthermore, three parameters are proposed to improve the performance of the algorithm. Finally, simulation experiment shows that the proposed BDE-Jaya algorithm has significant advantages compared with other algorithms.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"BDE-Jaya: A binary discrete enhanced Jaya algorithm for multiple automated guided vehicle scheduling problem in matrix manufacturing workshop\",\"authors\":\"Hao Chi, Hong-Yan Sang, Biao Zhang, Peng Duan, Wen-Qiang Zou\",\"doi\":\"10.1016/j.swevo.2024.101651\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>With the advent of \\\"Industry 4.0\\\", more matrix manufacturing workshops have adopted automated guided vehicle (AGV) for material handling. AGV transportation has become a key link in manufacturing production. Traditional AGVs scheduling problem (AGVSP) is studied in depth. However, most research overlooks an important problem, in production with limited resources, the number of AGVs is insufficient. Therefore, the wait time of workstations is longer than expected. The service time of the task is delayed and the cost is increased. To solve above problem, this paper proposes the binary discrete enhanced Jaya (BDE-Jaya) algorithm. The main goal is to minimize transportation cost, including AGV traveling cost, service early penalty, and total tardiness (TTD). A key-task shift method is proposed to reduce TTD and task service early penalty. Two heuristics based on the problem features are designed to generate the initial solution. In the evolutionary stage, three offspring generation methods are used to improve the algorithm exploitation capability and exploration capability. Then, an insertion-based repair method is designed to prevent the exploitation process falling into local optimum. Furthermore, three parameters are proposed to improve the performance of the algorithm. Finally, simulation experiment shows that the proposed BDE-Jaya algorithm has significant advantages compared with other algorithms.</p></div>\",\"PeriodicalId\":48682,\"journal\":{\"name\":\"Swarm and Evolutionary Computation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2024-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Swarm and Evolutionary Computation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210650224001895\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650224001895","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
BDE-Jaya: A binary discrete enhanced Jaya algorithm for multiple automated guided vehicle scheduling problem in matrix manufacturing workshop
With the advent of "Industry 4.0", more matrix manufacturing workshops have adopted automated guided vehicle (AGV) for material handling. AGV transportation has become a key link in manufacturing production. Traditional AGVs scheduling problem (AGVSP) is studied in depth. However, most research overlooks an important problem, in production with limited resources, the number of AGVs is insufficient. Therefore, the wait time of workstations is longer than expected. The service time of the task is delayed and the cost is increased. To solve above problem, this paper proposes the binary discrete enhanced Jaya (BDE-Jaya) algorithm. The main goal is to minimize transportation cost, including AGV traveling cost, service early penalty, and total tardiness (TTD). A key-task shift method is proposed to reduce TTD and task service early penalty. Two heuristics based on the problem features are designed to generate the initial solution. In the evolutionary stage, three offspring generation methods are used to improve the algorithm exploitation capability and exploration capability. Then, an insertion-based repair method is designed to prevent the exploitation process falling into local optimum. Furthermore, three parameters are proposed to improve the performance of the algorithm. Finally, simulation experiment shows that the proposed BDE-Jaya algorithm has significant advantages compared with other algorithms.
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.