考量成本與有限空間下之最佳生產系統佈置

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Title:	考量成本與有限空間下之最佳生產系統佈置
Other Titles:	The Optimal Layout of Production System with material-transportaion Cost and Constrained Space Considerations
Authors:	陳得恩 Chen, Te-en
Contributors:	管理科學研究所藍俊雄 Chun-hsiung Lan
Keywords:	生產線佈置;空間離散;二次分配問題 Lingo 9.0;space discrete technique;multi-production lines;QAP
Date:	2006
Issue Date:	2015-08-04 15:44:09 (UTC+8)
Abstract:	本文嘗試建構有限廠房空間下之生產系統模型 (Constrained-Factory-Space Production System Model，簡稱CPS模式)，同時本模式探討重複加工及容許合併工作單元之生產系統最佳佈置問題，並在此限制的情況下求取物料搬運成本最小化。CPS模式之主要目標為二次分配問題(Quadratic Assignment Problem, QAP)，傳統QAP常僅考慮到生產線之一維佈置情形，而有關多生產線及有限空間的問題均鮮少被提及。　　　本研究運用空間離散技術使空間中的無限可佈置位置有限化，進而使有關二維佈置的問題成功地加以求解。本研究所提的CPS模式可將上述實務的問題加以整合探討。CPS模式乃以空間中可能的佈置位置為其決策變數，以求各工作站總物料搬運成本最小化。本CPS模式以Lingo9.0 extended version之語法加以建模，並採用內建之Global Solver為本模式之求解方式。本研究以三條生產線共15個工作站為範例探討其最佳之佈置，並藉由模擬的方式，結論出可佈置空間之最小邊長，乃為決定最佳總搬運成本之關鍵因子。　　This paper proposed a mathematical model called Constrained-Factory-Space Production System (CPS) model to layout the optimal two-dimensional configuration of　multiple production lines under the considerations of constrained factory space, rework process, and the co-sited work The objective of CPS model is regarded as a Quadratic Assignment Problem (QAP). Most QAPs consider the one-dimensional layout of a production line, however the two-dimensional layout of multiple production lines under a constrained factory space is seldom mentioned. The space discrete technique is applied to make the infinite deploying positions become finite, and therefore the two-dimensional layout of a production system can be conducted. Fortunately, the proposed mathematical model, CPS model, can successfully achieve the above-mentioned practical concerns.　　　Besides, the positions to deploy the whole workstations are regarded as the decision variables to achieve the minimal material-transportation cost of the entire production system. In addition, this study applies the syntax of Lingo 9.0 extended version to construct the CPS model, and the built-in Global Solver is selected as its solving technique. The numerical example in this study is going to deploy fifteen workstations for three different production lines, and then determine that the critical factor affecting the optimal total transportation cost is the minimum length of the rectangular deployable area by conducting the simulation.
Appears in Collections:	[Department of Business Administration, Master/Ph.D Program in Management Sciences] Disserations and Theses(Master and Doctoral Program in Management Sciences)

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