隨著製造業自動化與永續發展目標的推進，工廠內部物流系統的效率與環境永續議題日益受到重視。堆高機作為倉儲與製造現場中最常使用的搬運設備，其配置方式與使用效率對於企業營運成本與碳排放具有關鍵影響。然而，現行多數企業缺乏系統性的堆高機作業區域劃分與資源配置策略，常導致堆高機閒置或過載情形，亦增加不必要的能源消耗與環境衝擊。
本研究以提升堆高機使用效率為目標，提出一套結合聚類方法 (Clustering Algorithm) 與區域負荷平衡方法 (Zone Load Balancing Approach) 之物流系統設計方法。此方法分為兩階段：第一階段針對工廠內部各區域進行 P/D 點（取貨與卸貨點）位置優化；第二階段則提出新的相似係數，結合改良式階層聚類法劃分作業區域，並進行負荷平衡調整與彈性堆高機配置，提升堆高機之作業效率及使用率，並同時避免各區域發生堆高機閒置或過載。
為驗證本方法之可行性與效益，本研究以某紡織產業案例進行實證分析，透過離散事件模擬比較本研究方法與其他傳統聚類法在多種需求情境下之表現。本研究提出之方法相較現況，可將堆高機使用率由 33.9% 提升至 73.3%、平均等待時間降低 51.1%、WIP 數減少 58.6%；同時堆高機台數由 10 台減至 7 台、每月碳排放由 1492.63 kgCO₂e 降至 1159.03 kgCO₂e，改善幅度達 22.4%。驗證本方法具備顯著之效率提升與永續價值，亦可作為企業未來進行堆高機調度與物流規劃之有效參考。

With the advancement of automation in manufacturing and the pursuit of sustainability goals, the efficiency and environmental impact of in‐plant logistics systems have become increasingly important. As the most commonly used material‐handling equipment in warehousing and production environments, the configuration and utilization of forklifts critically affect both operating costs and carbon emissions. However, many enterprises currently lack a systematic strategy for delineating forklift work zones and allocating resources, frequently resulting in forklift idleness or overload and thereby increasing unnecessary energy consumption and environmental burden.
This study aims to improve forklift utilization by proposing a logistics‐system‐design methodology that integrates Clustering Algorithm with a zone load balancing methods. The method is divided into two stages. In the first stage, the locations of pickup and delivery (P/D) points within the factory are optimized. In the second stage, a novel similarity coefficient is introduced and combined with an improved hierarchical clustering algorithm to partition work zones; thereafter, zone load balancing methods and flexible forklift allocation are performed to enhance operational efficiency and utilization rates while preventing forklift idleness or overload in any zone.
To verify the feasibility and benefits of the proposed methodology, an empirical analysis is conducted using a case from the textile industry, employing discrete‐event simulation to compare the performance of our approach against traditional clustering methods under various demand scenarios. The simulation results indicate that, compared to the current configuration, the proposed method increases forklift utilization from 33.9% to 73.3%, reduces average waiting time by 51.1%, and decreases work‐in‐process (WIP) levels by 58.6%. Furthermore, the required number of forklifts decreases from 10 to 7 units, and monthly carbon emissions decline from 1,492.63 kg CO₂e to 1,159.03 kg CO₂e, achieving a 22.4% reduction. These outcomes confirm that the proposed method provides significant efficiency improvements and sustainability benefits, offering a practical reference for enterprises in future forklift dispatching and logistics planning.

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