Pricing policy in green supply chain design: the impact of consumer environmental awareness and green subsidies

Maryam Shoaeinaeini, Kannan Govindan, Donya Rahmani*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


This paper presents a mixed-integer non-linear programming model to design a green closed-loop supply chain comprising hybrid plants, hybrid collection centers, customer zones, secondary markets, and disposal centers. To ensure a smooth reverse flow, our model determines a return rate for each customer zone with respect to the consumers’ environmental awareness and the optimal acquisition price offered for returned products. Considering the environmental awareness levels and optimal green levels, specific cost and price functions for green products are proposed. Further, government subsidy as a financial incentive for manufacturers is considered to make the model more realistic and challenging. The effectiveness of the proposed model is analyzed by an illustrative example generated based on an Iranian straw factory. Beneficial managerial insights are obtained by conducting several sensitivity analyses. Further, the illustrative example and several generated examples in all scales are assessed by Sine Cosine Crow Search Algorithm using an efficient solution representation based on the priority-based encoding. As there is no benchmark available in the related literature to validate the results of Sine Cosine Crow Search Algorithm and to evaluate its performance, Particle Swarm Optimization and Genetic Algorithm are utilized to solve the examples. Finally, the obtained results of different metaheuristic algorithms and the exact solution are compared in terms of the CPU time and the objective function value.

Original languageEnglish
JournalOperational Research
Issue number4
Pages (from-to)3989-4028
Publication statusPublished - Sept 2022


  • Acquisition price
  • Consumer environmental awareness
  • Government subsidy
  • Green supply chain
  • Return rate


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