E-Commerce Delivery Demand Modeling Framework for An Agent-Based Simulation Platform

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Tác giả: André Alho, Moshe Ben-Akiva, ZhiYuan Chua, Yusuke Hara, Md Sami Hasnine, Peiyu Jing, Takanori Sakai, Ravi Seshadri

Ngôn ngữ: eng

Ký hiệu phân loại: 511.8 Mathematical models (Mathematical simulation)

Thông tin xuất bản: 2020

Mô tả vật lý:

Bộ sưu tập: Metadata

ID: 165463

The e-commerce delivery demand has grown rapidly in the past two decades and such trend has accelerated tremendously due to the ongoing coronavirus pandemic. Given the situation, the need for predicting e-commerce delivery demand and evaluating relevant logistics solutions is increasing. However, the existing simulation models for e-commerce delivery demand are still limited and do not consider the delivery options and their attributes that shoppers face on e-commerce order placements. We propose a novel modeling framework which jointly predicts the average total value of e-commerce purchase, the purchase amount per transaction, and delivery option choices. The proposed framework can simulate the changes in e-commerce delivery demand attributable to the changes in delivery options. We assume the model parameters based on various sources of relevant information and conduct a demonstrative sensitivity analysis. Furthermore, we have applied the model to the simulation for the Auto-Innovative Prototype city. While the calibration of the model using real-world survey data is required, the result of the analysis highlights the applicability of the proposed framework.
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