Two meta-heuristic algorithms for the dual-resource constrained flexible job-shop scheduling problem

Authors

1 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C.,‎ Tehran, Iran

3 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

    Systems where both machines and workers are treated as constraints are termed dual- resource constrained (DRC) systems. In the last few decades, DRC scheduling has attracted much attention from researchers. This paper addresses the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) to minimize makespan. This problem is NP-hard and mainly includes three sub-problems: (1) assigning each operation to a machine out of a set of compatible machines, (2) determining a worker among a set of skilled workers for operating each operation on the selected machine, and (3) sequencing the operations on the machines considering workers in order to optimize the performance measure. This paper presents two meta-heuristic algorithms, namely simulated annealing (SA) and vibration damping optimization (VDO), to solve the DRCFJSP. The proposed algorithms make use of various neighborhood structures to search in the solution space. The Taguchi experimental design method as an optimization technique is employed to tune different parameters and operators of the presented algorithms. Numerical experiments with randomly generated test problems are used to evaluate performance of the developed algorithms. A lower bound is used to obtain the minimum value of makespan for the test problems. The computational study confirms the proper quality of results of the proposed algorithms. 

Keywords

Scientia Iranica
Volume 22, Issue 3 - Serial Number 3
Transactions on Industrial Engineering (E)
June 2015
Pages 1242-1257

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History

  • Receive Date: 31 August 2014
  • Revise Date: 21 December 2024
  • Accept Date: 27 July 2017

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