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Optimal Robust Ordering Quantity for a New Product Under Environmental Constraints.

Authors :
Yang, Cheng-Hu
Ma, Xin
Talluri, Srinivas
Ho, William
Source :
IEEE Transactions on Engineering Management; May2019, Vol. 66 Issue 2, p240-251, 12p
Publication Year :
2019

Abstract

Rapid new product introduction is an effective method for firms to capture market share. Considering the growing concerns of environmental threats, firms are under intense pressure to measure their impacts on the environment. Before launching a new product, a firm should make the appropriate decision regarding the order quantity with incomplete information to achieve the goals of both profit maximization and environmental threat minimization simultaneously. In this paper, distribution-free news vendor models with the environmental constraints are proposed to obtain efficient order decisions. The models are formulated based on the absolute minimax regret criterion. Based on the moment bound problem framework, the models are translated into a semi-infinite linear optimization problem. In addition, the closed-form solutions of the robust order quantities and carbon emission volumes are derived. The characteristics of effective order decisions are discussed under three types of carbon emissions regulations (i.e., the carbon capacity policy, carbon tax scheme, and carbon trading mechanism). By analyzing the product characteristics from both economic and environmental aspects, four possible new product introduction strategies are proposed that the firm might employ under different environmental constraints. The characteristics of the optimal order quantity are also investigated under three carbon emissions regulations across four new product introduction strategies. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00189391
Volume :
66
Issue :
2
Database :
Complementary Index
Journal :
IEEE Transactions on Engineering Management
Publication Type :
Academic Journal
Accession number :
135966526
Full Text :
https://doi.org/10.1109/TEM.2018.2819192