應用演算法求解隨機設施規劃問題

摘要傳統上，大部分的設施規劃問題，都是假設在確定性的情況下，進行設施佈置，所以參數都是已知的。而隨機設施規劃最主要的特點是能夠隨著時間的推移，為了更接近實際情況，市場的需求波動受到改變，應對產品需求的不確定性，能夠維持其工作效率的能力。因此本研究針對不確定性進行討論，在文獻資料中，不確定性就包含了風險存在，可能造成設施規劃上的浪費，影響物料搬運成本。由於設施規劃問題是屬於NP-Hard問題，因此本研究使用蝙蝠演算法來找出最佳規劃。在最近啟發式蝙蝠演算法 (Yang, 2010)被證明是更快，更高效的解決測試函數(De Jong's, Schwefel's, etc)相比之前的啟發式演算法。但是，目前沒有研究顯示使用蝙蝠演算法求解的隨機設施規劃問題的不確定性。本研究同時使用模擬退火法用來作為與新提出的啟發式演算法比較。結果表明，蝙蝠演算法比模擬退火法更快，更高效率。因此最後在模擬不同的情境下，使用蝙蝠演算法找出最大限度地減少風險的規劃佈置。關鍵字：隨機設施規劃、不確定性、風險、蝙蝠演算法、模擬退火法

關鍵字

隨機設施規劃、不確定性、風險、蝙蝠演算法、模擬退火法

並列摘要

Abstract Traditional facility layout problem assumes that all of the parameter are known. However in the real world, parameter such as demand of the product would always keep changing; this in result, will affect the production demand. Stochastic facility layout problem have the advantage, such that it can adapt over time subjected to the fluctuations in demand and in results would affect the production demand as well. To create more similar environment to real world scenario, the proposed study would use the stochastic facility layout problem to minimize material handling costs and wastes. Since, facility layout problem is NP-Hard problem we would use meta-heuristic to solve and find the best possible solution. The more recently meta-heuristic, Bat Algorithm (Yang, 2010) is proven to be faster and more efficient on solving benchmark functions (De Jong's, Schwefel's, etc) compared to the previous meta-heuristics. However, no research has been done on implementing bat algorithm to the facility layout problem. the proposed study, proposes a new method on implementing bat algorithm to solve facility layout problem dealing with uncertainty. As a benchmark, Simulated Annealing (SA) algorithm is used as a comparison with the new proposed meta-heuristic. The findings show that Bat Algorithm is proven to be faster and more efficient compare to the SA algorithm. Keyword: Stochastic facility layout problem, Uncertainty, Risk, Bat Algorithm, Simulated Annealing Algorithm

並列關鍵字

Stochastic facility layout problem ； Simulated Annealing Algorithm ； Bat Algorithm ； Uncertainty ； Risk

參考文獻

Barsegar, Jascha. (2011). A Survey of Metaheuristics for Facility Layout Problems.170. UMI Number: 1500314.

Baykasoglu, Adil, Dereli, Turkay, & Sabuncu, Ibrahim. (2006). An ant colony algorithm for solving budget constrained and unconstrained dynamic facility layout problems. Omega, 34(4), 385-396. doi: 10.1016/j.omega.2004.12.001

Braglia *, Marcello, Zanoni, Simone, & Zavanella, Lucio. (2005). Robust versus stable layout design in stochastic environments. Production Planning & Control, 16(1), 71-80. doi: 10.1080/0953728042000267690

Drira, Amine, Pierreval, Henri, & Hajri-Gabouj, Sonia. (2007). Facility layout problems: A survey. Annual Reviews in Control, 31(2), 255-267. doi: 10.1016/j.arcontrol.2007.04.001

Gandomi, Amir H., & Yang, Xin-She. (2014). Chaotic bat algorithm. Journal of Computational Science, 5(2), 224-232. doi: 10.1016/j.jocs.2013.10.002

國際替代計量

應用演算法求解隨機設施規劃問題

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