Application of a Distributionally Robust Optimization Approach for Single-Period Stochastic Inventory Problems with Different Carbon Policies

Qingguo Bai (), Guihao Wang (), Di Liu () and Zhenning Zhang
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Qingguo Bai: School of Management, Qufu Normal University, Rizhao 276826, P. R. China
Guihao Wang: Institute of Operations Research, Qufu Normal University, Rizhao, 276826, P. R. China
Di Liu: Beijing Institute for Scientific and Engineering, Computing, Beijing University of Technology, Beijing 100124, P. R. China
Zhenning Zhang: Beijing Institute for Scientific and Engineering, Computing, Beijing University of Technology, Beijing 100124, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2023, vol. 40, issue 04, 1-34

Abstract: While single-period stochastic inventory problems have been studied in academia and industry, the impacts of both carbon emissions reduction and partial distribution information have not been investigated. In this paper, we consider carbon emission reduction in a single-period inventory problem where both product price and demand are stochastic. When only partial information on the first and second moments of the two stochastic elements is specified, we use the max–min criterion to construct two optimization models under cap-and-trade and carbon tax policies. For each model, we prove the existence of the optimal robust decisions on production quantity and cleaning equipment investment. By comparing the profit and carbon emission between these two models, we identify certain conditions that improve the economic and environmental performance of the manufacturer. Finally, we conduct a numerical study to test the robustness of the optimal operational decisions under each carbon policy.

Keywords: Distributionally robust optimization; newsvendor; carbon abatement; moments information (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0217595923400018

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