隨著全球電子商務的蓬勃發展，倉儲管理在現代企業運營中扮演著越來越重要的角色。特別是在科技產業中，如何有效管理大量且種類繁多的庫存，並快速響應訂單需求，已成為企業競爭力的關鍵指標之一。本研究以W公司為例，探討了在複雜的倉儲環境中，如何最佳化多人協作的揀貨路徑規劃問題，同時考慮工作負載均衡和時間窗限制。
傳統的倉儲管理面臨諸多挑戰，如庫存空間有限、物料先進先出(FIFO)原則、多儲位揀貨等問題，這些因素都增加了揀貨作業的複雜性。為了提高作業效率，本研究將揀貨人員路徑規劃問題視為一種特殊的車輛途程問題(Vehicle Routing Problem, VRP)，並提出了一個創新的數學規劃模式來求解此問題。
本研究考慮了多項實際因素，包括物料體積、搬運時間、儲位間距離、點到點行走時間等。在上述設定下，本研究一共開發了一個整數規劃模式可得精確最佳解、三種高效的貪婪類演算法:(1)「基本貪婪演算法」以工單交期排序做多人協同合作的揀貨分配與路線規劃;(2)「分群貪婪演算法」將同交期的所有工單視為一群，再依基本貪婪演算法行做分配揀貨排序與路線規劃;(3)「隨機貪婪演算法」則以分群貪婪演算法為基礎，但將同交期的揀貨以隨機排序，以產生更多樣類似品質的貪婪解；以及兩種基因類演算法;(4)「基本基因演算法」將物料被揀順序視為基因，而染色體為所有物料的揀選序列，利用基因演算法原理收斂出優秀的物料揀選序列，以此產生揀貨路徑;(5)「分群基因演算法」則結合交期分群和基因演算法的優點，按交期依序分群，然後使用基本基因演算法但限縮其突變與交配需在同一交期分群中處理。大量數值測試結果顯示，整數規劃僅能求解小規模問題，實務上以貪婪類演算法求解速率最快但無法精進，而基因類演算法則可在合理時限內改善精進其求解品質，為本研究屆推薦的最佳實務作法。

This study addresses the challenges in optimizing multi-worker collaborative order picking routes, focusing on load balancing and time window constraints. Using Company W as a case study, it develops a mathematical model with integer programming to optimize picking routes and worker assignments. Due to the problem’s complexity, three greedy algorithms (Basic, Random, Clustered) and two genetic algorithms (Basic, Clustered) are proposed for large-scale instances. Historical order data from Company W tests these algorithms, showing genetic algorithms outperform greedy ones in solution quality. The Basic Genetic Algorithm excels in minimizing maximum worker time difference and total picking time, while the Clustered Genetic Algorithm achieves the shortest total picking time by reducing unnecessary distant assignments. In large-scale scenarios, the Basic Genetic Algorithm performs best in minimizing total picking time and worker time difference, with the Clustered Genetic Algorithm ranking second due to its efficient parallel operations within due date clusters.
The study underscores the potential for enhancing order picking efficiency and optimizing human resource allocation in warehouse management through these methods. Future research should explore multi-level storage locations, improve algorithm efficiency, incorporate both inbound and outbound processes, and address material jurisdiction constraints.

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