Multi-echelon supply chain network modelling and optimization via simulation and metaheuristic algorithms

Authors

1 Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

2 Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Abstract

An important problem in todays industries is the cost issue, due to the high level of competition in the global market. This fact obliges organizations to focus on improvement of their production-distribution routes, in order to nd the best. The Supply Chain Network (SCN) is one of the, so-called, production-distribution models that has many layers and/or echelons. In this paper, a new SCN, which is more compatible with real world problems is presented, and then, two novel hybrid algorithms have been developed to solve the model. Each hybrid algorithm integrates the simulation technique with two metaheuristic algorithms, including the Genetic Algorithm (GA) and the Simulated Annealing Algorithm (SAA), namely, HSIM-META. The output of the simulation model is inserted as the initial population in tuned-parameter metaheuristic algorithms to nd near optimum solutions, which is in fact a new approach in the literature. To analyze the performance of the proposed algorithms, di erent numerical examples are presented. The computational results of the proposed HSIM-META, including hybrid simulation-GA (HSIM-GA) and hybrid simulation-SAA (HSIM-SAA), are compared to the GA and the SAA. Computational results show that the proposed HSIM-META has suitable accuracy and speed for use in real world applications.

Keywords

Scientia Iranica
Volume 23, Issue 1 - Serial Number 1
Transactions on Industrial Engineering (E)
February 2016
Pages 330-347

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History

  • Receive Date: 02 March 2016
  • Revise Date: 21 December 2024
  • Accept Date: 27 July 2017

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