Optimization of economic lot scheduling problem with backordering and shelf-life considerations using calibrated metaheuristic algorithms

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • University of Malaya

Abstract

This paper addresses the optimization of economic lot scheduling problem, where multiple items are produced on a single machine in a cyclical pattern. It is assumed that each item can be produced more than once in every cycle, each product has a shelf-life restriction, and backordering is permitted. The aim is to determine the optimal production rate, production frequency, cycle time, as well as a feasible manufacturing schedule for the family of items, and to minimize the long-run average costs. Efficient search procedures are presented to obtain the optimum solutions by employing four well-known metaheuristic algorithms,
namely genetic algorithm (GA), particle swarm optimization (PSO), simulated
annealing (SA), and artificial bee colony (ABC). Furthermore, to make the algorithms more effective, Taguchi method is employed to tune various parameters of the proposed algorithms.
The computational performance and statistical optimization results show the effectiveness and superiority of the metaheuristic algorithms over other reported methods in the literature.

Details

Original languageEnglish
Pages (from-to)404-422
Number of pages19
JournalApplied Mathematics and Computation
Volume251
Publication statusPublished - 2015
MoE publication typeA1 Journal article-refereed

    Research areas

  • Economic lot scheduling problem, Genetic algorithm, Particle swarm optimization, Simulated annealing, Artificial bee colony, Taguchi

ID: 16580177