Improving local search for the fuzzy job shop using a lower bound

Authors:
Jorge Puente;Camino R. Vela;Alejandro Hernández-Arauzo;Inés González-Rodríguez
Affiliations:
A.I. Centre and Department of Computer Science, University of Oviedo, Spain;A.I. Centre and Department of Computer Science, University of Oviedo, Spain;A.I. Centre and Department of Computer Science, University of Oviedo, Spain;Department of Mathematics, Statistics and Computing, University of Cantabria, Spain
Venue:
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Year:
2009

Citing 9
Cited 0

Job shop scheduling by simulated annealing

Operations Research
Scheduling under Fuzziness

Scheduling under Fuzziness
Complex Scheduling (GOR-Publications)

Complex Scheduling (GOR-Publications)
A Study of Schedule Robustness for Job Shop with Uncertainty

IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Scheduling: Theory, Algorithms, and Systems

Scheduling: Theory, Algorithms, and Systems
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Semantics of Schedules for the Fuzzy Job-Shop Problem

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Jobshop scheduling with imprecise durations: a fuzzy approach

IEEE Transactions on Fuzzy Systems
Expected value of fuzzy variable and fuzzy expected value models

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the fuzzy job shop problem, where uncertain durations are modelled as fuzzy numbers and the objective is to minimise the expected makespan. A recent local search method from the literature has proved to be very competitive when used in combination with a genetic algorithm, but at the expense of a high computational cost. Our aim is to improve its efficiency with an alternative rescheduling algorithm and a makespan lower bound to prune non-improving neighbours. The experimental results illustrate the success of our proposals in reducing both CPU time and number of evaluated neighbours.