Abstract
The environmental and economic pressures caused by energy consumption have led to an increased consciousness of energy-saving in the manufacturing industry. To this end, this paper focuses on an unrelated parallel machine problem with multiple auxiliary resources, which is a typical configuration in the lithography process of wafer fabrication. By comprehensively considering the jobs with different processing demands, setup times, ready times, resource constraints and energy consumptions, a scheduling model with the objective functions of minimizing the total energy consumption of the system and the total weighted completion time is developed. Based on the mathematical model, a novel modified multi-objective artificial immune algorithm integrated with the non-domination sorting strategy is proposed to solve the problem. Furthermore, to improve the performance of the proposed algorithm, clone operators, neighborhood search operators, and elite preservation operators are applied to the algorithm. The experimental results and analysis validate that the presented algorithm is efficient and effective.
Highlights
Driven by the increasingly severe environmental problems and high energy prices, reducing energy consumption has become a priority for manufacturing enterprises
A population Pt consisting of Npop antibodies is randomly generated, and the range of values of each variable of the antibody is limited according to the known number of the lithography machines (M ) and type of reticle nji, thereby avoiding the generation of the infeasible solutions and reducing the search range of the algorithm
This paper studies the unrelated parallel machine scheduling problem that is integrated with the features of the lithography process in the wafer fabrication systems
Summary
Driven by the increasingly severe environmental problems and high energy prices, reducing energy consumption has become a priority for manufacturing enterprises. The EUPMS problem studied in this paper is based on the traditional unrelated parallel machine scheduling (UPMS) problem. Li et al [18] studied an unrelated parallel machine problem within the background of big data and cloud technology for manufacturing to minimize the total tardiness and energy consumption and proposed ten heuristic algorithms. Based on the above analysis, many efficient heuristic algorithms have been proposed for the UPMS problem; only a few published studies have focused on energy saving strategies. To obtain the solutions with both high quality and efficiency, constrained to the abovementioned conditions, a novel mathematical formulation with the objective function of minimizing both the total weighted completion time and energy consumption is proposed; an innovative modified multi-objective immune clone selection algorithm with an elite strategy is developed.
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