在當今快速發展的全球經濟中,物流和供應鏈管理的複雜性增加,倉儲(尤其低溫倉儲)的優化成為重要得議題,儘管傳統的優化方法在某些情況下表現良好,但在處理大規模和高度複雜的情況時,常反應出明顯不足。為此,本研究探討以一個基於量子特性的演算法,來應用於低溫倉儲優化。此研究受到了背包問題和護士排班問題的概念所啟發,將背包問題的空間優化概念和護士排班問題的時間管理策略進行結合,這些問題涉及到有效的資源分配和排程。借鑒這些問題的原理,為低溫倉儲的優化提供全新的視角。並設計了一種基於量子啟發式退火的演算法,專門針對低溫倉儲中的空間利用和效率進行優化,以提供一種更可靠及高效的優化方法。
In today's rapidly developing global economy, the complexity of logistics and supply chain management has increased, making the optimization of warehousing, particularly cold storage, a significant challenge. Although traditional optimization methods perform well in certain situations, they often prove inadequate when dealing with large-scale and highly complex scenarios. Therefore, this study explores the application of a quantum-inspired algorithm to optimize cold storage. This study is inspired by the concepts of the Knapsack Problem and the Nurse Scheduling Problem, combining the spatial optimization principles of the Knapsack Problem with the time management strategies of the Nurse Scheduling Problem. These problems involve effective resource allocation and scheduling. By drawing on the principles of these problems, this study provides a novel perspective for the optimization of cold storage. A quantum heuristic annealing-based algorithm is designed specifically to optimize spatial utilization and efficiency in cold storage. This approach aims to provide a more reliable and efficient optimization method, addressing the unique challenges associated with cold storage logistics.