Empirical working time distribution-based line balancing with integrated simulated annealing and dynamic programming

Daniel Leitold, Agnes Vathy-Fogarassy, J. Abonyi

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

According to the Industry 4.0 paradigms, the balancing of stochastic production lines requires easily implementable, flexible and robust tools for task to workstations assignment. An algorithm that calculates the performance indicators of the production line based on the convolution of the empirical density distribution functions of the working times and applies dynamic programming to assign tasks to the workstations is proposed. The sequence of tasks is optimised by an outer simulated annealing loop that operates on the set of interchangeable task-pairs extracted from the precedence graph of the task-ordering constraints. Eight line-balancing problems were studied and the results by Monte Carlo simulations were validated to demonstrate the applicability of the algorithm. The results confirm that our methodology does not just provide optimal solutions, but it is an excellent tool in terms of the sensitivity analysis of stochastic production lines.

Original languageEnglish
JournalCentral European Journal of Operations Research
DOIs
Publication statusAccepted/In press - Jan 1 2018

Fingerprint

Integrated
Production line
Working time
Simulated annealing
Dynamic programming
Line balancing
Monte Carlo simulation
Distribution function
Graph
Methodology
Sensitivity analysis
Performance indicators
Paradigm
Industry
Convolution
Assignment
Optimal solution

Keywords

  • Dynamic programming
  • Line balancing
  • Simulated annealing

ASJC Scopus subject areas

  • Management Science and Operations Research

Cite this

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