Scientia Iranica

Scientia Iranica

A hybrid meta-heuristic for balancing and scheduling assembly lines with sequence-independent  Setup times by considering deterioration tasks and learning eff ect

Authors
N. Hamta 1
S.M.T. Fatemi Ghomi 1
R. Tavakkoli-Moghaddam 2
F. Jolai 3
1 Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, 1591634311, Tehran, Iran
2 Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
3 Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Abstract
This paper addresses the Simple Assembly Line Balancing Problem of type II (SALBP-II), with Simultaneous e ects of deterioration and learning in which there are sequence-independent setup times relating to each task. In many real industrial environments, although the actual task processing times are de ned as a function of their starting times due to deterioration e ects, workstations improve continuously as a result of repeating the same activities by worker(s) or machine(s). In this paper, a mathematical model is developed for this novel problem, attempting to minimize the cycle time for a given number of workstations. In addition to the balancing of the assembly line, the developed model presents the execution scheduling of tasks assigned to each workstation. Moreover, a hybrid meta-heuristic method is proposed to solve such an NP-hard problem. This robust and simply structured solution approach uses the tabu search within the Variable Neighbourhood Search (VNS/TS). The computational experiments and comparison with a Di erential Evolution Algorithm (DEA) re ect the high eciency of our proposed algorithm for a number of well-known instances. 
Keywords
Assembly line balancing
Scheduling
Deterioration tasks
Learning e ect
Hybrid meta-heuristic
Sequence-independent setup times
Scientia Iranica
Volume 21, Issue 3
Transactions on Industrial Engineering (E)
June 2014
Pages 963-979

  • Receive Date 05 July 2014
  • Accept Date 27 July 2017