在製造相關行業的排程問題，傳統上考量的目標通常是最短完工時間、最短遲交天數及最少機台設置時間，能源成本、對環境造成的影響很少被納入考慮。本研究中，為計算機台能源消耗，將機台運轉分成四種狀態：開/關機狀態、閒置狀態、機台設置狀態及運轉狀態。接著，由台電提供的電價計算公式，例如時間電價與契約容量，將能源消耗量換算成能源成本。訂單拆批的批數、分配到的機台、機台稼動速度以及能源消耗的時間點都會造成兩個目標間的取捨。因此，此篇論文提出數學模型及基於非支配排序III的節能多目標基因演算法應用於拆批與天車搬運問題的彈性零工式生產排程，以最小化最大完工時間及能源成本。之後，以實驗設計取得節能多目標基因演算法的最佳參數設定。接著比較不同多目標演算法的以驗證節能多目標基因演算法的有效性。最後實驗結果證實此篇研究提出的節能多目標基因演算法可以有效的求解拆批與天車搬運問題的彈性零工式生產排程問題。

Traditional scheduling problems in manufacturing systems considers objectives such as makespan, tardiness, and setup time, but seldom take energy cost, and other environmental impacts into account. In the research, machining states including turning on/off machine, idle, setup, and processing state are considered with different energy consumption rate. The energy cost is then computed with the formula provided by Taipower with time-of-use tariff and contract capacity. Decisions such as the number of lots in an order, which machine to assign, what processing speed, when to transport will cause trade-off between the objectives. Therefore, the paper proposed a mathematical model and a non-dominated sorting genetic algorithm III (NSGAIII) based energy-efficient multi-objective genetic algorithm (EEMOGA) to solve flexible job shop scheduling problem (FJSSP) with crane transportation and lot streaming considering makespan and energy cost. The design of experiment (DOE) is used to find optimal setting of the EEMOGA. Then other multi-objective methods are compared, to show the effectiveness of the proposed EEMOGA. The experimental results experiment results prove that the proposed algorithm can solve the problem effectively and efficiently.

摘要……………………………………………………………………………….I
Abstract.…………………………………………………………………………..II
致謝………………………………………………………………………………III
Contents……………………………………………….…………………………IV
List of Figure……………………………………………………………….......VII
List of Table……………………………………………………………………..IX
Chapter 1 Introduction……………………………………………………….1
1.1 Background………………………………………………………………1
1.2 Objectives………………………………………………………………...2
1.3 Research Method…………………………………………………………3
1.4 Organization of Thesis……………………………………………………5
Chapter 2 Literature Review………………………………………………….6
2.1 Energy-efficient flexible job shop scheduling problem………………….6
2.2 Lot streaming…………………………………………………………….11
2.3 Genetic algorithm in multi-objective problems………………………….12
Chapter 3 Problem Definition………………………………………………..14
3.1 Problem Statement………………………………………………………..14
3.2 Assumptions and Notations…….………………………………………..19
3.3 Energy Cost description………………………………………………….27
3.3.1 Energy Consumption of CNC machine………………………………..27
3.3.2 Energy Consumption from Crane Transportation……………………..30
3.3.3 Energy Cost Formulation………………………………………………33
3.4 Problem Formulation…………………………………………………….35
Chapter 4 Methodology………………………………………………………37
4.1 Framework of Energy-Efficient Multi-Objective Genetic Algorithm……37
4.2 Steps of Energy-Efficient Multi-Objective Genetic Algorithm…………..38
4.2.1 Encoding and Decoding…………………………………………………38
4.2.2 Initial population generation…………………………………………….40
4.2.3 Selection…………………………………………………………………44
4.2.4 Crossover………………………………………………………………..44
4.2.5 Mutation…………………………………………………………………49
4.2.6 Local search……………………………………………………………..53
4.2.7 Fitness evaluation………………………………………………………..55
4.2.8 Next population generation………………………………………………55
4.2.9 Pseudocode of proposed EEMOGA……………………………………..56
Chapter 5 Computational Study………………………………………………...60
5.1 Parameter design and evaluation of proposed EEMOGA ………………….60
5.1.1 Performance criteria……………………………………………………….60
5.1.2 Experiment layout……………………………………………………….61
5.1.3 Description of the test data………………………………………………62
5.1.4 Parameter settings………………………………………………………..65
5.1.5 Comparison between Methodologies and result discussion……………..70
5.2 Parameter Analysis………………………………………………………….73
5.2.1 Contract capacity………………………………………………………….73
5.2.2 Crane energy consumption………………………………………………..75
5.2.3 Crane transportation speed………………………………………………..76
5.2.4 Machine threshold…………………………………………………………78
5.2.5 Machine operation speed………………………………………………….79
5.2.6 Energy consumption difference on each machine…………………………81
5.2.7 Operation time difference on each machine……………………………….82
Chapter 6 Conclusion……………………………………………………………84
Reference……………………………………………………………………………..86
Appendix……………………………………………………………………………..90
Appendix A: Part information………………………………………………………..90
Appendix B: Design of Experiment Data Table……………………………………..95

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