本文旨在探討個案物流公司物流中心供應商進貨配送排程問題之模式特性與求解方法,以供個案公司實務應用之需。透過建構個案公司物流中心供應商進貨配送排程最佳化整數規劃模式(ISSD-IP),採用最佳化演算法及啟發式演算法進行演算分析,探討符合供應商配送需求及物流中心作業限制的最佳月台數量、供應商月台分派、及配送排程表。經分析發現ISSD-IP為作業研究中典型的一維裝箱條碼問題(one dimensional bin packing problem),本文採取歷年研究結果表現較佳之啟發演算法FFD及BFD,以之作為求解方法,並將運算結果與最佳化求解結果及運算時間比較分析,BFD可以得到最快速及最佳的啟發解。實證研究取用個案物流公司北部常溫物流中心2006實際供應商配送資料,進行分析模式建構與分析,分析結果顯示個案公司在物流中心規劃資源、月台數目、供應商等候時間等相關資源之最佳使用將可產生三百一十萬元的節省效果,並可大幅增進與供應商之進貨排程協作效益。分析模式啟發演算法的敏感度分析結果顯示求解品質BFD均優於FFD,但是運算速度則BFD略遜於FFD,但是運算速度均在個案企業可以接受的範圍內。
The purpose of this research is to investigate the optimization issues of the inbound scheduling problem of supplier delivery operation of the distribution center of a major logistics company. The study results will be applied to improve the inbound delivery operations. We formulate inbound scheduling supplier delivery problem as a (0, 1) integer programming model-ISSD-IP to analyze the optimal number of the delivery (receiving) docks, the assignment of docks, and the delivery schedule for suppliers and the logistics company. It is found that ISSD-IP is similar to a well-known classical operations research problem, that is, onedimensional bin-packing problem studied quite extensively by many researchers and had very quick heuristics, namely FFD and BFD, for finding its approximate solutions. We conducted an in-depth ISSD case study of the logistics company and collected four one-week supplier delivery data to build optimization model and conduct relevant computational tests on both optimal and heurist ics algorithms. The computational results showed that BFD performed best in both computational speed and quality of solutions. The analysis had shown that the planning, the number of docks, supplier waiting time and other resources related to the distribution center ISSD of the studied company have potential of 3.1 millions NTD savings. Furthermore, the supplier collaboration can also be enhanced. Sensitivity analysis was conducted on four weeks data. The results showed that BFD was always outperformed FFD in solution quality but slower in computational times.
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