以遺傳演算法為基考量卸貨順序之最適化裝箱法

Abstract

隨著科技日新月異，除了以往實體購物，消費者對網路購物的需求逐漸增加。消費者只需要到電子商務平台搜尋商品，再使用信用卡、超商付款等方式支付，商品便會透過宅配送到指定地點。因為網路購物24小時營業，消費者可以時時刻刻挑選並購買喜歡的商品，而網路購物不需店面，除了打破疆界限制外，省下的租金可以回饋給消費者，提供更實惠的價格。同時，透過網路購物，消費者可以得到更多的商品資訊，除了了解詳細的商品規格，消費者更容易在不同店家之間進行比價。然而，網路購物無法像實體購物可以立即獲得商品，因此如何有效控管出貨及送貨速度，在便利性及時間之間取得平衡，便是值得探討的問題。欲加快物流配送速度，除了對運送路線及車次進行規劃，亦可針對貨物在貨車內的擺放位置進行討論，如此可以減低送貨員在各送貨地點的停留時間，以及確保貨物在運送過程中不會損毀。本研究針對最後一哩運送(Last Mile Delivery)作討論，以基因演算法為基，根據貨物運送順序，以及貨物是否為易碎品等條件，決定貨物在貨車上的擺放位置，使得卸貨置出之物品最小。

With the advance of science and technology, online shopping has become more and more popular. Consumers can find products they want easily on e-commerce platforms and pay by credit card, gift card or other electronic money. After paying, products will be delivered to the destinations. Consumers are able to shop online anytime and anywhere and know more about the specifications. Besides, consumers can easily make a price comparison between different websites. Although products are delivered rapidly, however, consumers still can’t get them immediately. Thus, the way to reduce lead time and delivery time is worth discussing. To speed up delivery, in addition to optimizing route planning, we can also conduct some research on cargo replacement to reduce delivery time and prevent damage caused during shipping. Under these conditions, this study discusses Last Mile Delivery. With the objective of minimizing cargo unloading times, the study aims to find an optimization method for cargo replacement according to the shipping sequence and cargo attributes based on Genetic Algorithm.