A hybrid heuristic to solve the parallel machines job-shop scheduling problem |
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| Authors: | Andrea Rossi Elena Boschi |
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| Affiliation: | 1. School of computer, Liaocheng University, Liaochheng, 252059, P. R.China;2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore;3. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, P. R. China;4. Singapore Institute of Manufacturing Technology, Nanyang Drive 638075, Singapore;1. Chair of Business Administration, Production & Supply Chain Management, Augsburg University, D-86135 Augsburg, Germany;2. Department of MIS, OM & DSc, University of Dayton, Dayton, OH, USA;1. Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK;2. Warwick Business School, University of Warwick, Coventry CV4 7AL, UK |
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| Abstract: | This paper presents an advanced software system for solving the flexible manufacturing systems (FMS) scheduling in a job-shop environment with routing flexibility, where the assignment of operations to identical parallel machines has to be managed, in addition to the traditional sequencing problem. Two of the most promising heuristics from nature for a wide class of combinatorial optimization problems, genetic algorithms (GA) and ant colony optimization (ACO), share data structures and co-evolve in parallel in order to improve the performance of the constituent algorithms. A modular approach is also adopted in order to obtain an easy scalable parallel evolutionary-ant colony framework. The performance of the proposed framework on properly designed benchmark problems is compared with effective GA and ACO approaches taken as algorithm components. |
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