Discrete imperialist competitive algorithm for the resource-constrained hybrid flowshop problem with energy consumption
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ORIGINAL ARTICLE
Discrete imperialist competitive algorithm for the resource-constrained hybrid flowshop problem with energy consumption Xin-rui Tao1 · Jun-qing Li1,2 · Ti-hao Huang1 · Peng Duan1 Received: 7 May 2020 / Accepted: 27 August 2020 © The Author(s) 2020
Abstract The resource-constrained hybrid flowshop problem (RCHFS) has been investigated thoroughly in recent years. However, the practical case that considers both resource-constrained and energy consumption still has rare research. To address this issue, a discrete imperialist competitive algorithm (DICA) was proposed to minimize the makespan and energy consumption. In the proposed algorithm, first, each solution was represented by a two-dimensional vector, where one vector represented the scheduling sequence and another one showed the machine assignment. Then, a decoding method considering the resource allocation was designed. Finally, we combined DICA and the simulated annealing algorithm (SA) to improve the performance of the proposed approach. Furthermore, we tested the proposed algorithm based on a randomly generated set of real shop scheduling system instances and compared with the existing heuristic algorithms. The results confirmed that the proposed algorithm can solve the RCHFS with high efficiency. Keywords Hybrid flowshop · Imperialist competitive algorithm · Resource-constrained
Introduction Hybrid flowshop scheduling problem (HFSP) is a generalization of the conventional flowshop scheduling problem. Compared with other types of scheduling problems, the multi-process and multi-stage characteristics of HFSP are deemed more realistic. In a typical manufacturing industry, various dynamic events may occur in the actual production process, such as limited resources and machine breakdown. Therefore, it is necessary to investigate the RCHFS taking this into consideration. As the major source of global warming, manufacturing activities are required to satisfy the regulations on environment protection and energy consumption. In this paper, the resource-constrained hybrid flowshop problem with energy consumption was studied, and this problem has significant practice relevance.
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Jun-qing Li [email protected]
1
School of Computer, Liaocheng University, Liaocheng 252059, China
2
School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China
With regard to the conventional HFSP, many variants and solutions have been discussed by Rubén Ruiz et al. [1]. To solve HFSP, several researchers have applied the exact algorithms, such as the Lagrangian relaxation algorithm [2–4] and the branch and bound algorithm [5–7]. However, with an increase in the scale of the problem, the resolution of accurate algorithms has become limited, and consequently, metaheuristic or heuristic algorithms have been used more widely to solve this problem. For example, the genetic algorithm (GA) has been applied by Behnamian et al. [8] and Dugardin et al. [9]. Then, the simulated annealing algorithm (SA) has been adopted by Elmi and Topaloglu
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