A four-echelon supply chain considering economic, social and regions satisfaction goals

Document Type : Research Paper

Authors

1 Assistant professor, Department of Industrial Engineering, Faculty of Technology and Engineering, University of Qom, Head of ICT Center, University of Qom

2 MSc student, Department of Industrial engineering, university of Qom, Qom, Iran

3 MSc, Department of Industrial engineering, qom university of technology, Qom, Iran

Abstract

This study develops a new multi-objective programming model to design a four-echelon pharmaceutical supply chain (PSC) network for several perishable products over multiple time periods. Supply chain consists of four echelons, including suppliers, manufacturers, distribution centers, and retailers. This model proposes an integrated decision-making approach for the location of facilities (pharmaceutical production and distribution sites) and their most suitable allocation to each other for a reliable transportation of products between echelons. It also determines the optimal amount of production and transportation among facilities and the required number of labours. A varying level of technological expertise is required for the establishment of production and distribution systems. The problem aims to reduce costs and unemployment and pharmaceutical supply gap between regions and to increase their satisfaction rate with an emphasis on the importance of providing a large supply of pharmaceutical products. Given the fact that the problem is a NP-hard one and accurate methods are inefficient, a genetic algorithm-based meta-heuristic is developed for problem-solving and its performance is analyzed on a wide range of single- and two-objective problem instances. The results show that an increase in the satisfaction rate of regions and a reduction in its gap between regions as two objectives are of great importance in pharmaceutical supply chain. Moreover, a reduction in unemployment gap between regions improves the level of employment, and it provides a right balance between social responsibilities. The developed algorithm also provides an optimal solution for large-sized single- and two-objective problems in a short time period.

Keywords

References

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Journal of Industrial Engineering Research in Production Systems
Volume 7, Issue 15
March 2020
Pages 199-217

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