隨著科技的不斷創新與發展加上網路的盛行，傳統購物模式開始轉變，大眾的購物模式不再僅限於至實體商店採購，現今顧客擁有另一個線上通路選擇，許多零售企業已將線上平台此一線上通路加入其銷售方式之一。2020年Covid-19的爆發，更加速民眾生活型態及購物模式的轉變，透過網路下單購買生鮮食品的需求日益提高，當配送產品儲存環境的限制出現了巨大的變化以及面臨大量的訂購量，若沒有規劃良好的配送策略，將會導致產品的損失以及增加非機遇性的額外成本，而影響公司的信譽，此時，線上及線下整合物流之配送變得更加重要，因此本研究提出全通路零售冷鏈產品之車輛途程問題(Vehicle Routing Problem for cold chain products in omni-channel retailing，VRPCCOC)。
全通路零售冷鏈產品之車輛途程問題解決供應商的冷鏈產品配送問題，在問題中有一個配銷中心及數個零售商和顧客，並利用同質車輛進行配送。本研究目標為最小化總成本，其中包括運輸成本及品質成本，在考慮時間窗和產品需求的情況下，規劃出最佳的車輛行駛路徑。我們為本研究之問題開發一個混整數的數學規劃模型，並利用BARON求解小型題庫，同時利用自適應大鄰域搜尋法 (Adaptive Large Neighborhood Search，ALNS)求解大型題庫並分析其效率，實驗結果顯示本研究所提出之ALNS演算法在求解VRPCCOC問題上有很好且穩定的表現，最後，我們探討不同的參數及不同庫存水準對於路徑成本的影響。

With the constant innovation and development of technology and the prevalence of the internet, traditional shopping patterns have begun to change and the public is no longer limited to purchasing from physical stores. The demand for fresh food products through online orders is increasing. However, when there is a significant change in the constraints of the storage environment for distribution products and a large volume of orders, failing to plan a good distribution strategy can lead to product loss and increase non-opportunistic additional costs, which can affect the company's reputation. Therefore, the integration of online and offline logistics for delivery has become even more important. Therefore, this study proposes a new variant of the Vehicle Routing Problem (VRP) called the VRP for cold chain products in omni-channel retailing (VRPCCOC).
VRPCCOC addresses the delivery problem of a fresh food supplier. In this problem, there is a distribution center, a set of retailers, and a set of customers. The distribution is performed using a fleet of homogeneous vehicles. The objective is to find an optimal routing plan that minimizes the total cost, consisting of transportation cost and quality loss cost. This problem is formulated as a mixed integer nonlinear programming model (MINLP) and small instances are solved using the BARON solver. Additionally, an adaptive large neighborhood search (ALNS) metaheuristic is proposed to solve large instances. Computational experiments are conducted to analyze the efficiency of ALNS. The experimental results indicate that the proposed ALNS is stable and performs well in solving VRPCCOC. Finally, the impact of various parameters and inventory levels on the optimal routes is explored.

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