Skip to main content
Article
Optimization of economic lot scheduling problem with backordering and shelf-life considerations using calibrated metaheuristic algorithms
Applied Mathematics and Computation (2015)
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. (C) 2014 Elsevier Inc. All rights reserved. Link to Full-Text Articles : http://www.sciencedirect.com/science/article/pii/S0096300314015586

Keywords
  • Economic lot scheduling problem,
  • Genetic algorithm,
  • Particle swarm optimization,
  • Simulated annealing,
  • Artificial bee colony,
  • Taguchi method (design of experiments)
Publication Date
January 15, 2015
Publisher Statement
Times Cited: 0 0
Citation Information
"Optimization of economic lot scheduling problem with backordering and shelf-life considerations using calibrated metaheuristic algorithms" Applied Mathematics and Computation Vol. 251 (2015)
Available at: http://works.bepress.com/facultyofengineering_universityofmalaya/142/