| Summary: | 碩士 === 輔仁大學 === 企業管理學系管理學碩士班 === 103 === As the importance of industry grows, more and more energy has been consumed. Industrial energy consumption grows from 200 quadrillion British thermal unit (Btu) in 2010 to 307 quadrillion Btu in 2040. Energy is a very important basis for industrial production, one of the costs resulted from energy consumption. If we can use production scheduling to reduce energy consumption cost, it will reduce the cost of production and make more competitive in the industry.
A lot of researches discussing reduced energy consumption focuses on machine efficiency or process redesign. To optimize the machine operation time can also save the energy, and these researches have received great interests in recent years. This study considers three different states of machines, to solve the problem of minimizing energy consumption costs under time-of-use tariff with non-delay in flexible flow shop. We proposed a two stage solving procedure. Stage one is using genetic algorithm to get job sequence. Stage two is to adjust part of job’s process start time to avoid on-peak period for minimizing energy consumption costs.
We compare 4 different genetic algorithm procedure as follows: minimizing Cmax genetic algorithm; minimizing energy genetic algorithm; adjust minimizing Cmax genetic algorithm and adjust minimizing energy genetic algorithm in the simulation test. The result shows that adjust minimizing Cmax genetic algorithm comparing to other genetic algorithm procedure, the energy consumption costs ratio of minimizing Cmax genetic algorithm: minimizing energy genetic algorithm: adjust minimizing Cmax genetic algorithm: adjust minimizing energy genetic algorithm is 1:0.78:0.95:0.74. In addition, adjust minimizing energy genetic algorithm has good ability for solving energy consumption cost problem in this study.
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