透過您的圖書館登入
IP:3.145.77.114
  • 學位論文

考量卸載熟練度之整合人車排程計劃

A Study of Integrating Truck and Workforce Scheduling with Workforce Proficiency

指導教授 : 陳建良
本文將於2025/08/02開放下載。若您希望在開放下載時收到通知,可將文章加入收藏

摘要


近年來,隨著跨國生產及跨國貿易的發展,越來越多企業選擇垂直專業化,通過衡量各個地區的優勢條件(如人力成本、自然資源、交通條件等)來決定各個產品的生產地點,從而提高企業的市場競爭優勢。然而供應商數量及產品品類的增長將會提升庫存管理及物流運輸之複雜性。為了解決該問題,許多企業通過建立基於越庫系統之配送中心來構建更成熟、高效、反應迅速的供應鏈體系。配送中心作為貨物運輸網路的樞紐環節,其作業效率對於整個供應鏈上下游有至關重要的影響。配送中心每日需要處理上百輛車進行收發貨的作業。因此,本研究的目標為通過縮短卸貨流程時間以提高物流效率。 本研究為整合車輛安排計劃及考量工人工作效率之人員安排計劃之提升卸載效率問題,目標為最小化整體卸貨時間及延誤時間。本研究還考慮到工人的工作效率會受到其工作熟練度之影響。通過使用不同的演算法來調整車輛卸貨過程中的人員及車輛安排計劃來縮短整體卸貨時間。最後,通過構建穩健演算法模型並比較實驗結果影響結果的敏感因數,為未來研究提供參考。

並列摘要


In recent years, with the development of cross-border production and trade, more and more companies choose a vertical specialization, which determines the location of each plant by measuring the advantages of each region (such as labor costs, natural resources, traffic conditions, etc.). Therefore, companies can increase their competitive advantage in the market. However, the increasing of suppliers and product categories will increase the complexity of inventory and logistics management. To solve this problem, many enterprises build a more mature, efficient, and responsive supply chain system by establishing distribution centers. As the hub of the cargo transportation network, the distribution center's operational efficiency has a vital impact on the suppliers and customers of the entire supply chain. The distribution center needs to handle hundreds of trucks every day. Hence, the goal of this study is to improve logistics efficiency by shortening the trucks’ unloading time. This paper is a study of integrating truck and workforce scheduling with workforce proficiency. The goal is to minimize the total completing time of inbound trucks. This paper also considers that the work proficiency of workers. Use different algorithms to adjust the truck and workforce schedule during the truck unloading process to shorten the unloading time. Finally, computational experiments are designed to illustrate and compare these approaches. The computational results are reported in detail.

參考文獻


Aarts, E., Aarts, E. H., & Lenstra, J. K. (2003). Local search in combinatorial optimization: Princeton University Press.
Amini, A., Tavakkoli-Moghaddam, R., & Omidvar, A. (2014). Cross-docking truck scheduling with the arrival times for inbound trucks and the learning effect for unloading/loading processes. Production & Manufacturing Research, 2(1), 784-804.
Arabani, A. B., Ghomi, S. F., & Zandieh, M. (2010). A multi-criteria cross-docking scheduling with just-in-time approach. The International Journal of Advanced Manufacturing Technology, 49(5-8), 741-756.
Arabani, A. B., Zandieh, M., & Ghomi, S. F. (2011). Multi-objective genetic-based algorithms for a cross-docking scheduling problem. Applied Soft Computing, 11(8), 4954-4970.
Boysen, N., & Fliedner, M. (2010). Cross dock scheduling: Classification, literature review and research agenda. Omega, 38(6), 413-422.

延伸閱讀