An efficient stochastic hybrid heuristic for flowshop scheduling

Authors:
Dipak Laha;Uday K. Chakraborty
Affiliations:
Department of Mechanical Engineering, Jadavpur University, Kolkata 700032, India;Department of Math and Computer Science, University of Missouri, St. Louis, MO 63121, USA
Venue:
Engineering Applications of Artificial Intelligence
Year:
2007

Citing 1
Cited 5

The application of the simulated annealing algorithm to the solution of the n/m/Cmax flowshop problem

Computers and Operations Research

Minimizing total flow time in permutation flow shop scheduling with improved simulated annealing

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Floating-point to integer mapping schemes in differential evolution for permutation flow shop scheduling

International Journal of Bio-Inspired Computation
Solving a periodic single-track train timetabling problem by an efficient hybrid algorithm

Engineering Applications of Artificial Intelligence
An opposition-based differential evolution algorithm for permutation flow shop scheduling based on diversity measure

Advances in Engineering Software
A simulated annealing heuristic for minimizing makespan in parallel machine scheduling

SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing

Quantified Score

Hi-index	0.01

Visualization

Abstract

The paper addresses the problem of flowshop scheduling in order to minimize the makespan objective. Three probabilistic hybrid heuristics are presented for solving permutation flowshop scheduling problem. The proposed methodology combines elements from both constructive heuristic search and a stochastic improvement technique. The stochastic method used in this paper is simulated annealing (SA). Experiments have been run on a large number of randomly generated test problems of varying jobs and machine sizes. Our approach is shown to outperform best-known existing heuristics, including the classical NEH technique (OMEGA, 1983) and the SA based on (OMEGA, 1989) of Osman and Potts . Statistical tests of significance are performed to substantiate the claims of improvement.