Multi-objective genetic algorithm for energy-efficient hybrid flow shop scheduling with lot streaming

Hybrid flow shop scheduling problems are encountered in many real-world manufacturing operations such as computer assembly, TFT-LCD module assembly, and solar cell manufacturing. Most research considers the scheduling problem in regard to time requirements and the steps needed to improve production...

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Bibliographic Details
Published in:Annals of operations research 2020-07, Vol.290 (1-2), p.813-836
Main Authors: Chen, Tzu-Li, Cheng, Chen-Yang, Chou, Yi-Han
Format: Article
Language:English
Subjects:
Assembly
Business and Management
Carbon
Combinatorics
Completion time
Electric power
Energy costs
Energy efficiency
Fitness
Genetic algorithms
Integer programming
International organizations
Job shop scheduling
Job shops
Manufacturing
Methods
Multiple objective analysis
Operations research
Operations Research/Decision Theory
Photovoltaic cells
Power consumption
Production management
Production scheduling
S.i. : Some
Scheduling
Scheduling (Management)
Solar cells
Studies
Theory of Computation
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Summary:Hybrid flow shop scheduling problems are encountered in many real-world manufacturing operations such as computer assembly, TFT-LCD module assembly, and solar cell manufacturing. Most research considers the scheduling problem in regard to time requirements and the steps needed to improve production efficiency. However, the increasing amount of carbon emissions worldwide is contributing to the worsening global warming problem. Many countries and international organizations have started to pay attention to this problem, even creating mechanisms to reduce carbon emissions. Furthermore, manufacturing enterprises are showing growing interest in realizing energy savings. Thus, the present research study focuses on reducing energy costs and completion time at the manufacturing-system level. This paper proposed a multi-objective mixed-integer programming for energy-efficient hybrid flow shop scheduling with lot streaming in order to minimize both the production makespan and electric power consumption. Due to a trade-off between these objectives and the computational complexity of the proposed multi-objective mixed-integer program, this study adopts the genetic algorithm (GA) to obtain approximate Pareto solutions more efficiently. In addition, a multi-objective energy efficiency scheduling algorithm is also developed to calculate the fitness values of each chromosome in GA.
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-018-2969-x