本研究主要針對流程型生產排程問題進行研究,並加入非相關平行機器的考量,其任一工件於各機器上之處理時間依機器而變動,及在既定的績效衡量指標下,決定如何將n項工件分配到m部機器及各機器上的工件處理順序。在實際工廠中欲求之解往往並非全然是單一目標,而是存在相互衝突的多目標,所以本論文為求解最大完工時間(Makespan)和總延遲時間(Total Tardiness Time)之雙目標總和最小化之近似最佳解。 本研究為作業到達時間不一致之雙目標流程型生產排程問題;由於同時考量作業之最大完工時間及總延遲時間,所以求解上相對複雜,因此本研究採用菁英政策(Elitism Strategy)之基因演算法(Genetic Algorithm)作為求解方法,以加強世代演化的效率,並以一案例的生產排程資料為例,將生產排程問題用數學模型建構出來,而後驗證本方法之有效性,接著使用田口實驗設計(Taguchi)找出演算法之最佳參數組合,最後進行演算法的測試及分析。實驗結果證明本研究菁英政策之基因演算法求解效果良好,整體目標值之改善率平均提升約24.51%,並能夠順利協助管理者執行排程相關之作業。
This paper focuses on Flexible Flow Shop production scheduling problem, adding unrelated parallel machines considerations, any of its parts in all of the processing time depends on the machine while the machine changes, and the established performance metrics, deciding how to assign n items to the m machine on the machine processing order. In the actual plants tend to solve the solution is not entirely a single goal, but there is conflicting multi-objective, so this paper is to solve Makespan and Total Tardiness Time with dual objectives of minimizing the sum of approximate optimal solution. In this paper, the arrival time for the job to the twin goals of inconsistent process production scheduling problem; jobs due to simultaneous consideration of Makespan and Total Tardiness Time, so the solution is relatively complex. In this study, Elitism Strategy of the Genetic Algorithm as a solution method to enhance the efficiency of generation of evolution, and in a case of production scheduling information, for example, the production scheduling problems using mathematical models constructed, verifying the effectiveness of the method, using the Taguchi design of Experiments to find the best combination of parameters, last the final testing and analysis algorithms. Experimental results demonstrate that the study of the Genetic Algorithm to solve the Elitism Strategy works well, and the improvement in the overall target average rate increased by approximately 24.51%, so it has practical application value and can successfully help managers run scheduled jobs associated.
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