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2018年 第5卷 第4期

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《工程管理前沿（英文）》 >> 2018年 第5卷 第4期 doi: 10.15302/J-FEM-2018042

Minimization of total energy consumption in an m-machine flow shop with an exponential time-dependent learning effect

1. Department of Industrial Engineering & Management, Peking University, Beijing 100871, China
2. Department of Industrial & Systems Engineering, University of Wisconsin-Madison, WI, USA
3. Department of Industrial Engineering & Management, Peking University, Beijing 100871, China; Department of Industrial & Systems Engineering, University of Wisconsin-Madison, WI, USA

录用日期： 2018-11-08 发布日期： 2018-11-29
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This study investigates an energy-aware flow shop scheduling problem with a time-dependent learning effect. The relationship between the traditional and the proposed scheduling problem is shown and objective is to determine a job sequence in which the total energy consumption is minimized. To provide an efficient solution framework, composite lower bounds are proposed to be used in a solution approach with the name of Bounds-based Nested Partition (BBNP). A worst-case analysis on shortest process time heuristic is conducted for theoretical measurement. Computational experiments are performed on randomly generated test instances to evaluate the proposed algorithms. Results show that BBNP has better performance than conventional heuristics and provides considerable computational advantage.

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