貨架空間規劃問題(Shelf-space allocation problem, SSAP)為零售商營運上最重要的課題之一。本研究以Yang and Chen(1999)提出的SSAP模型為基礎,應用近年來較新穎的教與學(Teaching-and-learning based optimization, TLBO)演算法進行解題,並與Yang啟發式演算法、Yang改良啟發式演算法、GA演算法以及GA-VNS演算法進行解題品質之比較分析,以驗證其求解之表現。緊接,本研究結合TLBO與變動鄰域搜尋法(Variable neighborhood search, VNS)強化 TLBO的求解品質。經實驗分析結果得知,所提出的TLBO-VNS演算法除所需設定控制參數少外,其求解品質顯著地優於現有其他演算法。
Shelf-space allocation problem (SSAP) is one of the most important issues in retail operations management. In this paper, we use the SSAP model proposed by Yang and Cheng (1999) as the foundation and apply a relatively newer algorithm, Teaching-Learning-Based Optimization (TLBO), to solve the problem. We also compare the solution quality of this method with Yang’s heuristic algorithm, Yang’s improved heuristic algorithm, Genetic Algorithm (GA) and Genetic Algorithm-Variable Neighborhood Search (GA-VNS). Further, we integrate TLBO and VNS methods to enhance the solution quality of TLBO. The experimental results indicate that in addition to use of fewer control parameters, the proposed TLBO-VNS algorithm is also superior to other algorithms in solution quality.