本研究以隨機權重多目標基因演算法為基礎設計應用於零散式揀貨倉庫系的啟發式儲貨指派方法。由於儲貨指派問題屬於非決定性多項式集合難題，無法找到最佳解，因此發展出許多啟發式演算法來尋找最佳近似解。 然而，很少有研究同時考慮多目標。本文提出的啟發式隨機權重多目標遺傳演算法以如何減少作業過程中因為缺貨而發生的緊急補貨作業及減少因各個揀貨區中工作量不平衡所導致生產線停滯問題這兩者為目標。在多目標基因演算法加上隨機權重係數可以使結果分散在多維目標空間中，以找出最優近似解，另外菁英保留策略使表現優良的染色體得以保存在菁英群體中，透過將菁英解加入每一個世代，使每一代染色體的水平提高，保留表現優良的基因。 最後，通過數據實驗建立模擬情境，將演算法的結果與隨機儲位指派方法和先到先服務儲位指派方法進行比較，其結果顯示本文所提出的方法優於比較對象。 This paper develops a storage assignment policy base on random weight multi-objective genetic algorithm for storage assignment problem (SAP) in a pick-and-pass warehouse system. Since SAP is an NP-hard problem, many heuristic algorithms have been proposed to find approximation solutions to the SAP. However, few research considered about simultaneously solving the multi-objective solution in SAP. The proposed heuristic random weight multi-objective genetic algorithm considered the workload balance between picking lines and emergency replenishment during picking operation. The random weight coefficient in the proposed algorithm can distribute the possible solution results in multi-dimensional objective space to help obtain the optimal solutions. Besides, the elite preserve strategy of our proposed genetic algorithm keeps the solutions with better performance in the elite solution group, further improving the quality and competitiveness of each solution generation. Finally, using data simulation, our proposed algorithm is compared with random and first-come-first-served assignment policies. The results from the simulation show that the proposed algorithm outperforms the ones with random and first-come-first-served assignment policies.
