Abstract
Batch processing machine (BPM) scheduling problem is a NP hard problem for it includes machine allocation, job grouping, and batch scheduling. In this paper, to address the BPM scheduling problem with unrelated parallel machine, a multiobjective algorithm based on multipopulation coevolution is proposed to minimize the total energy consumption and the completion time simultaneously. Firstly, the mixed integer programming model of the problem is established, and four heuristic decoding rules are proposed. Secondly, to improve the diversity and convergence of the algorithm, the population is divided into two populations: each of the populations evolves independently by using different decoding rules, and the two populations will communicate through a common external archive set every certain number of generations. Thirdly, an initialization strategy and a variable neighborhood search algorithm (VNS) are proposed to improve the overall performance of the algorithm. Finally, in order to evaluate the proposed algorithm, a large number of comparative experiments with the state-of-the-art multiobjective algorithms are carried out, and the experimental results proved the effectiveness of the proposed algorithm.
Highlights
Manufacturing industry reflects a country’s productivity level, and production scheduling is a key link to determine enterprises to achieve their production objectives. erefore, the research on production scheduling has always been a hot issue for enterprises and scholars
(3) Research on multiobjective production scheduling problem, such as Zhou et al [6], addressed a Batch processing machine (BPM) scheduling problem with the aim to minimize the makespan and the total electricity cost; Zhu-Min [7] addressed the scheduling problem of hybrid flow-shop (HFS) with the aim to minimize delay penalties, proceeding time, setup cost, and holding cost; Lu et al [8] addressed the scheduling problem of HFS to minimize the noise pollution, makespan and energy consumption simultaneously, etc. erefore, it is of great significance to study the multiobjective energy-efficient scheduling problem under complex production conditions
Batch processing machine scheduling problem (BPMSP) is a NP hard problem; it has a wide range of applications, such as foundry industry [9], logistics freight [10], and electronics manufacturing facilities [11]. e character of batch processing machine scheduling problem (BPMSP) is that it takes a batch as a unit to process multiple jobs at the same time, and the processing time of a batch is determined by the job with the longest
Summary
Manufacturing industry reflects a country’s productivity level, and production scheduling is a key link to determine enterprises to achieve their production objectives. erefore, the research on production scheduling has always been a hot issue for enterprises and scholars. Erefore, it is of great significance to study the multiobjective energy-efficient scheduling problem under complex production conditions. In UPBPMSP, for each job, we should decide the processing machine, with which jobs to form a batch and the process sequence of the batches All these affect the production efficiency and the total processing energy consumption of enterprises at the same time. We studied the UPBPMSP with the aim to minimize the energy consumption and the maximum completion time (makespan) simultaneously, as they are the main goals an enterprise pursues. (1) Aiming at minimizing the total energy consumption (TEC) and the makespan, a multiobjective differential evolution algorithm based on multipopulation coevolution (MODE/MPC) is proposed (2) To ensure the distribution and diversity of solutions, the algorithm adopts two-population coevolution: each population adopts different coding and decoding methods to search for excellent solutions from different perspectives.
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