應用演算法求解多目標綠色動態設施規劃問題

Apply Heuristics to Solve Multi-Objective Green Dynamic Facility Layout Problem

廖文璿(Wen-Hsuan Liao)

指導教授 ： 陳育欣

中原大學/電機資訊學院/工業與系統工程研究所/碩士(2015年)

https://doi.org/10.6840/cycu201500560

摘要

在二十一世紀，永續發展已經成為不可忽視的議題，地球持續因溫室氣體增加而導致全球暖化效應越加嚴重，而許多研究報告都表明製造業與其供應鏈正是造成此一現象的元凶之一，故如何降低企業對於地球的傷害也成為當前決策者需要面對的問題。

在以碳足跡為衡量企業的永續經營之際，本研究以降低供應鏈所產生的碳排放為目標，並為了更貼近現實情況，以動態設施規劃(Dynamic Facility Layout)來當作研究問題，同時考量物流成本與部門間鄰近關係等，是為本篇之多目標最佳化問題。

因本研究之三目標三期的動態設施規劃為NP-Hard 問題且資料量龐大， 故以啟發式演算法中的多目標蝙蝠演算法與多目標模擬退火演算法來求解此問題，又因解出的三目標互相之間無法比較優劣，本研究採用柏拉圖最佳解的概念來篩選出非受支配解以供決策者參考。本研究最後分別比較多目標蝙蝠演算法與多目標模擬退火法的求解數量與求解多樣性，結果顯示兩演算法的求解多樣性相差無幾，而多目標蝙蝠演算法的求解數量優於多目標模擬退火演算法。

關鍵字

多目標 ； 動態設施規劃 ； 永續發展 ； 柏拉圖最佳解 ； 多目標蝙蝠演算法 ； 多目標模擬退火演算法

並列摘要

In the twenty-first century, sustainable development has become a topic can’t be ignored. The earth’s effect of global warming due to greenhouse gases(GHG) continue to increase caused seriously increasing. Many studies have shown that it’s caused by the manufacturing industry and supply chain. How to reduce the damage made by enterprise to the Earth has become a problem decision-makers need to face now.

This study apply reducing carbon emissions generated by the supply chain as one objective, and in order to measure the carbon footprint of sustainable management of enterprise. To create more similar environment to real world scenario, this study apply dynamic facility layout as research methodology. At the same time, consider material handling cost between departments and closeness rating of neighboring departments. This study can be defined as a multiple objective optimization problem.

Because of this dynamic facility layout problem with three objectives and three periods and lots of data, this problem is a NP-Hard problem, so this study choose Meta-heuristic to solve it. Multi-objective Bat algorithm and Multi-objective Simulated Annealing algorithm are used in this problem. After running the two algorithm, Pareto optimal solution is used to compare the solution which with three objectives can’t be compared with each other. Finally, this study also compare the solving efficiency and the solution diversity of the two algorithm. The results show that the diversity of two algorithm are almost the same, but for the solving efficiency, Multi-objective Bat algorithm has an edge over Multi-objective Simulated Annealing algorithm.

並列關鍵字

multi-objective ； dynamic facilities planning ； sustainable development ； Pareto optimal solution ； multi-objective Bat algorithm ； multi-objective Simulated Annealing algorithm

參考文獻

Balakrishnan, J., Jacobs, F. R., & Venkataramanan, M. A. (1992). Solutions for the constrained dynamic facility layout problem. European Journal of Operational Research, 57(2), 280-286.

Baykasoglu, A., Dereli, T., & Sabuncu, I. (2006). An ant colony algorithm for solving budget constrained and unconstrained dynamic facility layout problems. Omega, 34(4), 385-396.

Booker, L. B., Goldberg, D. E., & Holland, J. H. (1989). Classifier systems and genetic algorithms. Artificial intelligence, 40(1), 235-282.

Our Common Future

Chen, G. Y.-H., & Lo, J.-C. (2014). Dynamic Facility Layout With Multi-Objectives. Asia-Pacific Journal of Operational Research, 31(04), 1450027.