A hybrid variable neighborhood search algorithm for the hot rolling batch scheduling problem in compact strip production. (February 2018)

• Record Type: Journal Article
• Authors: Zhang, Biao; Pan, Quan-ke; Gao, Liang; Zhang, Xin-li; Chen, Qing-da
• Is Part Of: Computers & industrial engineering. Volume 116(2018)
• Page Start: 22
• Page End: 36
• Publication Date: 2018-02
• Subjects: Variable neighborhood search -- Fruit fly optimization -- Hot rolling scheduling -- Compact strip production; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2017.12.013
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A mathematical model for the hot rolling batch scheduling in CSP is proposed. A constructive heuristic is proposed for minimizing the number of rolling turns. A hybrid VNS-based algorithm is proposed for minimizing the average thickness change. Abstract: This paper deals with a hot rolling batch scheduling (HRBS) problem arising from the compact strip production (CSP) process, which is one of the most popular production systems in the modern iron-steel industry to produce sheet strips. The HRBS problem aims to determine a sequence of the sheet strips in a predetermined number of rolling turns with the objective of minimizing average thickness change. In this paper, a mathematical model based on a comprehensive investigation is first given. Then a constructive heuristic based on the problem-specific characteristics is presented to generate initial feasible solutions. The heuristic can guarantee a minimum number of rolling turns to accommodate all the ordered sheet strips, but generally performs poorly in the objective of average thickness change. To improve the objective, a hybrid variable neighborhood search algorithm (HVNS) is proposed. In the HVNS, a thickness value permutation is used to encode the solution and four neighborhood structures are well designed. The fruit fly optimization algorithm, is integrated to improve search efficiency. Correspondingly, a neighborhood switching strategy is developed to improve local search ability. Moreover, a restart strategy based on the block swapping operator is used to help the algorithm escape from local optima. To investigate the effectiveness of the solution approach, two sets of instances are tested, including real-world instances and randomly generated instances. The performance of the proposed HVNS is evaluated by comparing with the other existing algorithms and the experimental results demonstrate that the proposed algorithm performs much better.