臺灣博碩士論文加值系統

區位途程問題（LRP）目前是物流研究中的一個新興領域，其結合了兩個眾所周知的配銷規劃工作：設施區位問題（FLP）和車輛途程問題（VRP）。配銷規劃網絡的主要目標是獲得最小的系統成本，這類似於公司的經濟目標。然而，為了實現地球永續經營的未來，現在公司必須更重視環境保護的問題，例如：員工工作量的產出要與公司物流所造成的汙染排放達到平衡。挑戰點出現於永續問題可能與公司的經濟目標相衝突。因此，本研究努力尋求最佳解決方案。
本研究的文獻探討發現必須在有限工作量下的LRP才可以解決永續經營的問題。因此，本研究考量提出一個多目標函數的LRP來解決這個問題。該模型考慮了三個目標函數：（1）最小化設施、車輛和物流配送相關的總成本，（2）平衡物流配送的工作量及（3）最小化物流配送所產生的二氧化碳排放。該模型的主要目的是計算物流中心的最佳位置、最佳化車輛數量及滿足所有這三個目標函數的配送路線。
為了解決該問題，本研究為多目標途程問題提出一種創新的啟發式演算法，即多目標正弦餘弦演算法（MOSCA）。該演算法採用簡單的離散化方法進行修改，以處理離散空間的尋優問題。然後，採用幾個測試實例以評估MOSCA的性能，並應用於解決印尼Daerah Istimewa Yogyakarta市的實際個案研究。將結果與三種傳統啟發式演算法進行比較，包括非優勢排列遺傳演算法（NSGA-II）、多目標粒子群優化算法（MOPSO）和柏拉圖保存式演化策略演算法（PAES）。實驗結果顯示， MOSCA的性能在五個比較指標上優於其他演算法。

The location-routing problem (LRP) is an emerging area in logistics research which combines two well-known distribution planning tasks: facility location problem (FLP) and vehicle routing problem (VRP). The main goal of planning a distribution network is to obtain a minimum systemwide cost, which resembles companies’ economical objective. However, in order to achieve a more sustainable future, nowadays companies have to start to give attention to the sustainability issue, such as providing workload balance for their employees and producing less emission from their operational activities. The challenge arises because sustainability issue is potentially conflicting with companies’ economical objective. Therefore, an effort has to be made to find the optimal solution.
Our literature review finds only limited works in LRP which have already addressed sustainability issue comprehensively. Thus, this study intends to present a multi-objective sustainable LRP to address this issue. The proposed model considers three objective functions: (1) to minimize the total cost associated with facility, vehicle, and distribution, (2) to balance the workload in distribution activities, and (3) to minimize CO2 emission from transportation activities. The main purposes of this model are to obtain the optimal location of distribution centers, a number of vehicles established, and delivery routes which satisfy all of these three objectives.
In order to solve the model, this study implements a novel metaheuristic for multi-objective routing problem, namely the multi-objective sine-cosine algorithm (MOSCA). The algorithm is modified with a simple discretization technique to deal with discrete search space. Then, the performance of the proposed algorithm is evaluated with several test instances and applied to solve a real world case study in Daerah Istimewa Yogyakarta, Indonesia. The results are compared to three classical metaheuristics, namely non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective particle swarm optimization (MOPSO), and Pareto archived evolution strategy (PAES). The experimental results indicate that, based on five comparison metrics, MOSCA performs better than the other algorithms.

摘 要 i
ABSTRACT ii
ACKNOWLEDGMENT iv
TABLE OF CONTENTS v
LIST OF TABLES vii
LIST OF FIGURES ix
LIST OF APPENDIX x
CHAPTER I - INTRODUCTION 1
1.1 Background and Motivation 1
1.2 Research Objectives 3
1.3 Research Scope and Assumptions 4
1.4 Thesis Organization 4
CHAPTER II - LITERATURE REVIEW 6
2.1 Location-Routing Problem 6
2.2 Multi-Objective Sine-Cosine Algorithm 13
CHAPTER III - METHODOLOGY 17
3.1 Research Framework 17
3.2 Mathematical Model 18
3.2.1 Notations 18
3.2.2 Problem Formulation 20
3.2.3 Formulation of Social Objective 21
3.2.4 Formulation of Environmental Objective 22
3.3 Solution Representation 25
3.4 Discrete Multi-Objective Sine-Cosine Algorithm 26
3.5 Quality Indicators 29
3.5.1 Number of Pareto solutions (NPS) 30
3.5.2 Maximum Spread (MS) 30
3.5.3 Mean-Ideal Distance (MID) 30
3.5.4 Spacing Metrics (SM) 31
3.5.4 CPU Time 31
CHAPTER IV - EXPERIMENTAL RESULTS 32
4.1 Datasets 32
4.2 Parameter Tuning using Taguchi Design 33
4.3 Verification 36
4.3.1 Model Verification 36
4.3.2 Algorithm Verification 37
4.4 Computational Testing 39
4.5 Analysis 39
4.5.1 Number of Pareto solutions (NPS) 39
4.5.2 Maximum Spread (MS) 41
4.5.3 Mean-Ideal Distance (MID) 44
4.5.4 Spacing Metrics (SM) 46
4.5.5 CPU Time 49
4.5.6 Summary 51
4.6 Time Complexity 51
CHAPTER V – CASE STUDY 53
5.1 Profile of Case Study 54
5.2 Computational Testing 55
5.3 Solutions 57
5.4 Managerial Implications 59
5.4.1 Trade-off 59
5.4.2 The Importance of LRP 60
5.4.3 Sensitivity Analysis 61
CHAPTER VI – CONCLUSIONS 64
6.1 Conclusions 64
6.2 Research Limitations 65
6.3 Contributions 65
6.4 Suggestions for Future Research 66
REFERENCES 67

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