4.7 Article

Optimal retailer investments in green operations and preservation technology for deteriorating items

期刊

JOURNAL OF CLEANER PRODUCTION
卷 140, 期 -, 页码 1514-1527

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2016.09.229

关键词

Green retailing; Preservation technology investment; Reference price; Optimal control; Simulated annealing algorithm

资金

  1. National Research Foundation of Korea (NRF) - Ministry of Science, ICT & Future Planning [2015R1A2A1A15053948]
  2. National Research Foundation of Korea [2015R1A2A1A15053948] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

This paper examines the impact of dynamic retailer investments in green operations while considering the reference price effect. A replenishment problem based on joint pricing, dynamic investment in green operations, preservation technology, and optimal replenishment times for deteriorating items is considered as a way to maximize retailer profit. In this problem, the demand rate depends on the sales and reference prices as well as green concern level. Optimal control theory is employed to obtain a dynamic investment rate, and a simulated annealing algorithm is used to find the solution of the nonlinear constraint optimization problem, which determines the price, preservation technology investment, and replenishment cycle time. In addition, computational simulations and sensitivity analyses are carried out to offer managerial insights. The findings suggest that continuous investment in green operations and preservation technology can significantly improve the retailer's financial performance. The consumer reference price is an important influence on the retailer's decision to invest in green operations. Furthermore, results show that the higher price sensitivity of the market always discourages the retailer from investing in green operations. The retailer needs to invest more in green operations for products with relatively high unit value. (C) 2016 Elsevier Ltd. All rights reserved.

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