期刊
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
卷 54, 期 22, 页码 6891-6911出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2016.1193246
关键词
scheduling; assembly flowshop; genetic algorithms; variable neighbourhood search; opposition-based learning
资金
- National Natural Science Foundation of China [71231007, 71131004, 71301124, 51305376]
- Research Fund for the Doctoral Program of Higher Education of China [20130141120071]
To enhance the overall performance of supply chains, coordination among production and distribution stages has recently received an increasing interest. This paper considers the coordinated scheduling of production and transportation in a two-stage assembly flowshop environment. In this problem, product components are first produced and assembled in a two-stage assembly flowshop, and then completed final products are delivered to a customer in batches. Considering the NP-hard nature of this scheduling problem, two fast heuristics (SPT-based heuristic and LPT-based heuristic) and a new hybrid meta-heuristic (HGA-OVNS) are presented to minimise the weighted sum of average arrival time at the customer and total delivery cost. To guide the search process to more promising areas, the proposed HGA-OVNS integrates genetic algorithm with variable neighbourhood search (VNS) to generate the offspring individuals. Furthermore, to enhance the effectiveness of VNS, the opposition-based learning (OBL) is applied to establish some novel opposite neighbourhood structures. The proposed algorithms are validated on a set of randomly generated instances, and the computation results indicate the superiority of HGA-OVNS in quality of solutions.
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