臺灣博碩士論文加值系統

在過去缺乏技術的時代，台灣物流市場都是使用人工揀貨方式來進行揀貨作業，揀貨效率極為不理想，難以承受大量訂單。隨著日新月異的工業技術影響，倉儲設施重新規劃及電腦化甚至導入了自動化倉儲系統，對倉庫或物流中心整體效益極為提高。貨物大量進出時，路徑規劃是影響作業的核心因素，隨著日新月異的工業技術，路徑規劃越來越被重視。搬運車出入更加頻繁的情形之下，常衍生出許多的問題，如出入口設置不當、行走動線設計不良等，經常造成貨物堆積於出入口、二次搬運及行走路徑過長。時常在無形中增加了作業時間及成本費用的提升。
本研究應用具有動態自我適應慣性權重調整機制的通用型粒子群演算法規劃載具路徑與出入口規劃。同時，使用Excel VBA撰寫計算揀貨路徑之程式以演算出最短揀貨路線，使揀貨效率達到最佳化。在實驗分析中將本研究與改良式粒子群演算法結合基因演算法行走進行比較，距離優化了4.47％，證明本研究運用通用型粒子群演算法可有效提升倉儲揀貨路徑規劃的效率。

In the past, the traditienal logistic markets in Taiwan used manual picking methods for picking operations. The picking efficiency was extremely unsatisfied and it was difficult to bear a large number of orders. With the influence of ever-changing industrial technology, the introduction of a computerized or ever an automated warehousing system has greatly improved the overall benefits of the logistic center. When a large number of goods arriving and leaving, route planning is the core factor of affecting operations. With the ever-changing industrial technology, route planning has been paid more and more attention. In the case of more frequent arriving and leaving of trucks, problems often arise, such as improperly set up entrances and exits, and poorly designed walking lines. It often causes goods to accumulate and stock at entrances and exits, secondary handling and long walking paths. It often increases the operating time and cost in an intangible way.
In this study, a generalized particle swarm optimization with dynamic self-adaptive inertial weight adjustment mechanism was used to plan vehicle path and entrance and exit planning. This study also uses Excel VBA to write a program to calculate the picking route to calculate the shortest picking route to optimize the picking efficiency. In the experimental analysis, this study is compared with the modified particle swarm optimization combined with the genetic algorithm walking, and the distance is promoted to 4.47%, which proves that the generalized particle swarm optimization used in this study can effectively improve the efficiency of storage picking path planning.

摘要 I
Abstract III
謝誌 V
目錄 VI
圖目錄 IX
表目錄 XI
第一章、緒論 1
1.1研究背景 1
1.2研究動機與目的 2
1.3研究範圍與限制 3
1.4研究架構與流程 4
第二章、文獻探討 7
2.1倉儲佈置與揀貨路徑之探討 7
2.1.1倉儲佈置與出入口設置 7
2.1.2揀貨路徑 10
2.2 聯合訂單批次 11
2.3 啟發式演算法(Meta-Heuristics) 13
2.3.1 蟻群最佳化演算法(Ant Colony Optimization, ACO) 13
2.3.2 基因演算法(Genetic Algorithm, GA) 15
2.3.3 粒子群演算法(Particle Swarm Optimization, PSO) 18
2.3.4 改良型粒子群演算法(Modified Particle Swarm Optimization) 19
2.3.5 通用型粒子群演算法(Generalized Particle Swarm Optimization, GEPSO) 20
2.4 小結 24
第三章、研究方法 25
3.1數學模式 25
3.2研究流程 28
3.3通用型粒子群演算法路徑計算演算步驟 30
3.3.1確認倉儲與訂單資訊 30
3.3.2通用型粒子群演算之建立初始族群 30
3.3.3通用型粒子群演算法計算最短路徑 31
3.3.3.1通用型粒子群演算法方程式模式 31
3.3.3.2參數 32
3.3.3.3通用型粒子群演算法方程式 33
3.3.3.4粒子的速度更新之方程式解析 34
3.3.4更新適應值最佳解 35
3.3.5判斷是否滿足通用型粒子群演算法終止條件 35
3.4小結 35
第四章、研究架構與數據分析 36
4.1問題描述與架構 36
4.2模式建構 40
4.3參數設定 44
4.3.1通用型粒子群演算法需要設置的參數 44
4.3.2改良式粒子群演算法結合基因演算法參數需要設置的參數 45
4.4實驗參數 46
4.5參數設計分析及比較 50
4.6小結 52
第五章、結論與建議 53
5.1結論 53
5.2後續研究建議 54
參考文獻 56
中文文獻 56
英文文獻 58
附錄1 60

中文文獻
石志鑌，「揀貨中心之訂單批量揀取策略研究」，國立中央大學，碩士論文，新竹縣，台灣，(2004)。
宋佩樺，「運用粒子群演算法於道路養護路線排程之研究」，國立屏東科技大學，碩士論文，屏東縣，台灣，(2014)。
李悅翠，「改良式粒子群演算法應用在聯合訂單之最短揀貨路徑問題」，國立屏東科技大學，碩士論文，桃園市，台灣，(2015)。
林豐澤，「演化式計算下篇：基因演算法以及三種應用實例」，智慧科技與應用統計學報第3卷第1期，第29 - 56頁，(2005)。
陳彥州，「應用基因演算法於概念圖動態學習分組之研究」，中原大學，碩士論文，桃園市，台灣，(2004)。
陳君涵、陳俊佑，「揀貨區無人搬運車之派車法則最適化研究」，危機管理學刊第17卷第1期，第55 - 64頁，(2020)。
陳柏瑞，「應用改良型烏鴉搜尋演算法於倉儲揀貨路徑規劃」，國立屏東科技大學，碩士論文，高雄市，台灣，(2020)。
陳君涵、林書愷，「物流中心輕型料架搬運車之最適化規劃研究」，危機管理學刊第18卷第1期，第67- 76頁，(2021)。
傅和彥，「現代物料管理二版」，前程文化事業有限公司出版，(2007)。
黃建興，「應用和弦搜尋演算法求解多橫向通道之批次人工揀貨問題」，元智大學工業工程與管理學系學位論文，碩士論文，(2011)。
黃律惟、林春成、詹立楷、鄭辰仰，「結合基因與蟻群演算法應用於揀貨最短路徑與交期問題」，工業工程學會年會暨學術研討會，第363-370頁，(2012)。
蔡榮發、彭磊義、林明華、陳俐穎，「儲位指派問題之全域最佳化方法」，管理與系統第25卷第1期，第29 - 59頁，(2018)。
英文文獻
Ai, T.J. and V. Kachitvichyanukul., “A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery” Computers & Operations Research, vol.36, pp.1693-1702, (2009).
Caron, F., Marchet, G. and Perego, A., “Layout design in manual picking systems: a simulation approach” Integrated Manufacturing Systems, vol.11, pp.94-104, (2000).
Dorigo , M., “The ant system optimization by a colony of cooperating agents” IEEE Transactions on Systems, vol.26, no.1, pp.1-13, (1996).
Guoqing, Zhang, Tatsuushi Nishi, Sarina D.O. Turner, Keisuke Oga, Xindan Li., “An integrated strategy for a production planning and warehouse layout problem: Modeling and solution approaches” Omega, vol.68, pp.85-94, (2017).
Holland, J. H., “Adaptation in Natural and Artificial Systems,” The University of Michigan Press, (1975).
Hsiao, Y. T., C. L. Chuang. and J. A. Jiang., “A hybrid of ε-constraint and particle swarm optimization for designing of PID controllers,” IEEE International Conference on Systems, vol.4, pp.3063-3070, (2005).
Kulak, O., Y. Sahin., M. E. Taner., “Joint order batching and picker routing in single and multiple-cross-aisle warehouses using cluster-based tabu search algorithms,” Flexible Services and Manufacturing Journal, pp.52-80, (2012).
Kennedy, J. and R. Eberhart., “Particle swarm optimization,” IEEE International Conference on (IEEE)., pp.1942–1948, (1995).
Lian, Yindong and Wei Xie and Langwen Zhang., “A Probabilistic Time-Constrained Based Heuristic Path Planning Algorithm in Warehouse Multi-AGV Systems” IFAC-PapersOnLine, vol.53, pp. 2538-2543, (2020).
Roodbergen, Kees Jan and Renéde Koster “Routing order pickers in a warehouse with a middle aisle” European Journal of Operational Research, vol.133, pp332-43, (2001).
Sedighizadeh, Davoud and Ellips Masehian and Mostafa Sedighizadeh and Hossein Akbaripour., “A new generalized particle swarm optimization algorithm” Mathematics and Computers in Simulation, vol.179, pp. 194-212,(2020).
Tsai, C. Y., J. J. H. Liou., and T. M. Huang., “Using a multiple-GA method to solve the batch picking problem: considering travel distance and order due time” Publishing models and article dates explained, pp.6533-6555, (2008).
Thanushkodi, K. and K. Deeba., “A new improved particle swarm optimization algorithm for multiprocessor job scheduling” International Journal of Computer Science Issues, vol.8, pp.230-235, (2011).
Won, J. and S. Olafsson., “Joint order batching and order picking in warehouse”, pp.1427-1442, (2005).
Xua, Yan and Guofei Guana and Qingwu Songa and Chao Jianga and Lihui Wangb., “Heuristic and random search algorithm in optimization of route planning for Robot’s geomagnetic navigation” Computer Communications, vol.154, pp.12-17, (2020).
Zhang, Y. “Correlated Storage Assignment Strategy to reduce Travel Distance in Order Picking” IFAC-Papers On Line, vol.49, pp.30-35, (2016).