揀貨作業是物流中心營運成本的主要組成之一，入庫時儲位指 派的適切性是提升揀貨效率的前提。良好的儲位指派同時也可縮短 入庫時間、提升儲位利用率以及改善庫存管理。本研究之目的為提 出一儲位指派方法以降低入庫作業成本並提升揀貨作業效率。儲位 指派問題可建構為二次指派問題（Quadratic assignment problem, QAP），亦為一 NP-Complete 問題，因此在求解大型問題有其困難 性。因此本研究制定一遺傳演算法來求解儲位指派問題，其目標是 最小化入庫移動距離並將同時揀貨機率高的品項存放至鄰近儲位， 以縮短未來揀貨時之移動距離。由於問題多目標的特性，本研究亦 設計挑選效率解的方式以獲得儲位指派決策。為了驗證本研究方法 之績效，我們以某物流中心作業資料為基礎，模擬在應用本研究方 法與現行作業的情況下，兩者之績效差異。 Picking operation is one of the major operating costs of a distribution center, and the proper assignment of storages to stocks is critical to the improvement of picking efficiency. Appropriate storage assignments can also shorten storing time, improve storage utilization, and facilitate inventory management. The purpose of this study is to propose a storage location assignment method to reduce the cost of storage operations and improve the efficiency of picking operations. The storage assignment problem can be modeled as a quadratic assignment problem（QAP）, which also appertain to an NP-Complete problem, and hence creates difficulties in solving large scale problems. To fill this gap, this study develops a genetic algorithm to solve the problem with the objectives of minimizing the distance of moving and storing the stocks, and maximizing the chance of simultaneous picking of adjacent stocks to reduce the future possible picking movement distance. Considering the multi-objectives characteristics of the problem, this study devises a method to determine the final storage assignment from a set of Pareto solutions. Performance of the proposed approach is evaluated via a computer simulation based on historical orders of the distribution center, by comparing the results from the proposed approach and the current operations of the distribution center respectively.
