Robust pricing and inventory strategies for an omnichannel retailer under carbon tax and cap-and-offset regulations

全渠道 报童模式 偏移量(计算机科学) 碳排放税 利润(经济学) 产业组织 卡鲁什-库恩-塔克条件 微观经济学 供应链 业务 经济 计算机科学 营销 数学优化 温室气体 生物 程序设计语言 数学 生态学
作者
Jianteng Xu,Guangmei Lyu,Qingguo Bai,Qian Luo
出处
期刊:Computers & Industrial Engineering [Elsevier BV]
卷期号:185: 109615-109615 被引量:3
标识
DOI:10.1016/j.cie.2023.109615
摘要

While an increasing number of retailers use the buy-online-and-pick-up-in-store (BOPS) service to improve operational efficiency, it increases the uncertainty between the offline and online channels. Furthermore, the implementation of different carbon regulations creates certain challenges for an omnichannel retailing system because of the tradeoff between the economy and the environment. These factors motivate us to study the joint pricing and inventory decisions of an omnichannel retailer regrading the stochastic demand of BOPS customers with limited information under carbon tax and cap-and-offset regulations. A distributionally robust newsvendor model is developed for the omnichannel retailer under carbon tax regulation to derive the joint decisions on the sales price and inventory. An interesting result indicates that under carbon tax regulation, differential pricing strategy increases the profit of the omnichannel retailer. The distributionally robust newsvendor model under cap-and-offset regulation is solved by the Karush-Kuhn–Tucker (KKT) conditions. The comparison shows that adopting uniform pricing strategy under cap-and-offset regulation brings more profits than adopting it under carbon tax regulation. Finally, a numerical study is conducted to illustrate and complement the theoretical results. The value of addition demand information is also tested. Key findings show that the omnichannel retailer gains higher profit and emits more carbon emissions under cap-and-offset regulation than under carbon tax regulation. Under each type of regulation, the performance of robust solutions is close to that with normal distribution.

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