考量時間及容量限制下之B2C電子商務配送模型研究

A study of B2C E-Commerce delivery model with time and capacity limit

洪星宇(Hsing-Yu Hung)

指導教授 ： 蔣明晃

國立臺灣大學/管理學院/商學研究所/碩士(2022年)

https://doi.org/10.6342/NTU202201228

摘要

現今科技的進步加上網路的普及，使得人們的消費習慣跟過去相比有非常大的不同，從以往傳統的實體店面購物轉變為藉由網路查看商品資訊、下單訂購的購物模式，雖然前年爆發的新冠疫情影響了電子商務市場的成長幅度，但B2C電子商務整體市場仍維持穩定的成長，在商品資訊較為透明的電子商務市場中，如何在時間、容量的限制下降低整體物流成本藉此增加自身的競爭優勢，便是企業未來需要考量的重大課題。

本研究之目的為建構一考量運送時間及車輛容量限制下，最小化車輛配送成本和車輛數量之模型，幫助企業規劃運送路線及決定所需車輛數，讓企業能藉由模型建立一套具競爭力的決策系統。

在過去車輛旅途問題大多使用傳統演算法作為求解工具，但許多傳統演算法不僅求解耗時且其結果品質較差。本研究以帶菁英策略之非支配排序基因演算法(NSGA II)對最短配送路線、最小車輛數，兩項目標進行求解，來有效減少業者車輛配送相關的總成本。

最後以國內知名B2C電商業者之訂單資料為研究對象，導入改良之非支配排序基因演算法，對車輛數及配送路線進行求解並對其結果進行分析，幫助B2C電商制定最佳的競爭策略。

關鍵字

B2C電子商務物流 ； 車輛途程問題 ； 基因演算法 ； 非支配排序基因演算法 ； 配送模型

並列摘要

Because of the technological advances and the popularity of the Internet. People's consumption habits have changed a lot different from those of the past, from the traditional physical shopping to the online shopping. Although the outbreak of covid-19 in the previous year affected the growth rate of the e-commerce market, the overall B2C e-commerce market continued to show steady growth. In the e-commerce market where product information is more transparent, how to increase their competitive advantage by reducing overall logistics costs under the constraints of time and capacity is a significant issue that enterprises need to consider in the future.

The purpose of this study is to establish a model for minimizing distribution costs and vehicle numbers under the constraints of delivery time and vehicle capacity. This model can help companies to build a competitive decision-making system by planning their delivery routes and determining the number of vehicles.

In the past, most traditional algorithms were used to solve Vehicle Routing Problems. However, many traditional algorithms are not only time-consuming to solve the problem but also have poor quality results. In this study, a Non-dominated Sorting Genetic Algorithm II (NSGA II) is used to solve the two objectives of shortest delivery route and minimum number of vehicles to reduce the total cost.

Finally, we use the well-known B2C electricity supplier order data and NSGA II to solve the problem. Help B2C e-commerce companies to develop the best competitive strategy by analyzing the result.

並列關鍵字

B2C E-commerce Logistic ； Vehicle Routing Problem ； Genetic Algorithm ； NSGA II ； Distribution Model

參考文獻

林志隆 (2004). "台灣B2C電子商務經營與成功模式分析." 臺灣大學國際企業研究所學位論文。

李莉 (2008). "基於遺傳演算法的多目標尋優策略的應用研究." 江南大學碩士學位論文。

徐慧英, 趙健民, 張泳, 朱信忠 (2010). "改進NSGA II算法在車輛路徑多目標優化問題的應用. " 計算機工程與科學. 32(10):117-121。

張易晟 (2014). "B2C 電子商務下都會區配送模型之研究." 臺灣大學工業工程學研究所學位論文。

Gommans, Marcel, Krish S. Krishnan, and Katrin B. Scheffold. (2001). "From brand loyalty to e-loyalty:A conceptual framework." Journal of Economic and Social research 3.1: 43-58.