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An optimization model for a monopolistic firm serving an environmentally conscious market : use of chemical reaction optimization algorithm.

Choudhary, A. and Suman, R. and Dixit, V. and Tiwari, M. and Fernandes, K. and Chang, P. (2015) 'An optimization model for a monopolistic firm serving an environmentally conscious market : use of chemical reaction optimization algorithm.', International journal of production economics., 164 . pp. 409-420.


This work considers a monopolist firm which faces the following twin challenges of serving an environmentally sensitive market. The first challenge is the demand׳s elasticity to emissions and price. To entice its emission conscious customers and generate higher demand, the firm incrementally invests in cleaner production technologies. It also adopts a voluntary limit on its emissions from transportation. However, such investments and penalty lead to the second challenge of reduced net profit. To address above trade-off, a Non-Linear Programming (NLP) model with a maximization quadratic profit function has been formulated. Recently developed, Chemical Reaction Optimization algorithm, with superior computational performance, has been adopted to solve the NLP. The output of the model provides near optimal monopolistic price, best attainable reduction in manufacturing emissions through proportional investment and makes a choice of suitable mode of transportation for each type of product offered by the firm. Three types of sensitivity analyses were performed by varying contextual parameters: customers׳ emission elasticity, penalty charged per unit emission and investment coefficient. The results, underpin the importance of investments in cleaner technologies and the need of financial aids for profit maximizing firms operating in cleaner markets. This work provides a decision making tool to determine the near optimal degree of each of the above dimension in multiple business fronts.

Item Type:Article
Full text:(AM) Accepted Manuscript
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
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Publisher statement:© 2014 This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Date accepted:29 August 2014
Date deposited:20 October 2017
Date of first online publication:04 November 2014
Date first made open access:No date available

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