Modeling and scheduling no-idle hybrid flow shop problems

Journal of Optimization in Industrial Engineering

مقاله 7، دوره 10، شماره 21، خرداد 2017، صفحه 59-66   

نوع مقاله: Original Manuscript

شناسه دیجیتال (DOI): 10.22094/joie.2016.261

نویسندگان

Mehdi Yazdani^{* 1}؛ Bahman Naderi²

¹Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran

²Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

چکیده

Although several papers have studied no-idle scheduling problems, they all focus on flow shops, assuming one processor at each working stage. But, companies commonly extend to hybrid flow shops by duplicating machines in parallel in stages. This paper considers the problem of scheduling no-idle hybrid flow shops. A mixed integer linear programming model is first developed to mathematically formulate the problem. Using commercial software, the model can solve small instances to optimality. Then, two metaheuristics based on variable neighborhood search and genetic algorithms are developed to solve larger instances. Using numerical experiments, the performance of the model and algorithms are evaluated.

Although several papers have studied no-idle scheduling problems, they all focus on flow shops, assuming one processor at each working stage. But, companies commonly extend to hybrid flow shops by duplicating machines in parallel in stages. This paper considers the problem of scheduling no-idle hybrid flow shops. A mixed integer linear programming model is first developed to mathematically formulate the problem. Using commercial software, the model can solve small instances to optimality. Then, two metaheuristics based on variable neighborhood search and genetic algorithms are developed to solve larger instances. Using numerical experiments, the performance of the model and algorithms are evaluated.

کلیدواژه‌ها

Scheduling؛ No-idle hybrid flow shops؛ Mixed integer linear programming؛ variable neighborhood search؛ Genetic Algorithm