Naihui He , M’hammed Sahnoun , David Zhang , Belgacem Bettayeb
{"title":"A hybrid approach using ant colony optimisation for integrated scheduling of production and transportation tasks within flexible manufacturing systems","authors":"Naihui He , M’hammed Sahnoun , David Zhang , Belgacem Bettayeb","doi":"10.1016/j.cor.2025.107059","DOIUrl":null,"url":null,"abstract":"<div><div>This paper studies the integrated scheduling problem in flexible manufacturing systems (FMS), where flexible machines and Automated Guided Vehicles (AGV) shared by production jobs are scheduled simultaneously in an integrated manner. Routing flexibility, a crucial advantage of FMS, enabling a job to be handled via alternative machine combinations, is involved. To address this problem, we propose a novel hybrid approach using Ant Colony Optimisation (ACO), which employs a two-element vector structure to model the ACO decision nodes. Each node represents an operation from a job assigned to a particular machine. During the ACO process, to decide a node for next movement, an ant first assesses potential nodes through a node scheduling procedure with two consecutive steps: firstly, using a heuristic vehicle assignment method, an AGV is designated and scheduled for the operation specified in a node. Following this, based on the established transportation timeline, the operation’s production schedule on the assigned machine is determined. Subsequently, the node selection is guided by the pheromone information on potential paths and the heuristic data of potential nodes derived from their scheduling information. To avoid local optima, multiple heuristic rules are incorporated in the ACO, with one chosen randomly for node selection each time. Numerical tests show that our proposed approach outperforms contemporary metaheuristic approaches in the literature. In addition, its efficiency of handling complex problem instances is also assessed and demonstrated.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107059"},"PeriodicalIF":4.1000,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Operations Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0305054825000875","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper studies the integrated scheduling problem in flexible manufacturing systems (FMS), where flexible machines and Automated Guided Vehicles (AGV) shared by production jobs are scheduled simultaneously in an integrated manner. Routing flexibility, a crucial advantage of FMS, enabling a job to be handled via alternative machine combinations, is involved. To address this problem, we propose a novel hybrid approach using Ant Colony Optimisation (ACO), which employs a two-element vector structure to model the ACO decision nodes. Each node represents an operation from a job assigned to a particular machine. During the ACO process, to decide a node for next movement, an ant first assesses potential nodes through a node scheduling procedure with two consecutive steps: firstly, using a heuristic vehicle assignment method, an AGV is designated and scheduled for the operation specified in a node. Following this, based on the established transportation timeline, the operation’s production schedule on the assigned machine is determined. Subsequently, the node selection is guided by the pheromone information on potential paths and the heuristic data of potential nodes derived from their scheduling information. To avoid local optima, multiple heuristic rules are incorporated in the ACO, with one chosen randomly for node selection each time. Numerical tests show that our proposed approach outperforms contemporary metaheuristic approaches in the literature. In addition, its efficiency of handling complex problem instances is also assessed and demonstrated.
期刊介绍:
Operations research and computers meet in a large number of scientific fields, many of which are of vital current concern to our troubled society. These include, among others, ecology, transportation, safety, reliability, urban planning, economics, inventory control, investment strategy and logistics (including reverse logistics). Computers & Operations Research provides an international forum for the application of computers and operations research techniques to problems in these and related fields.