Paper Info
Title
Variable Neighborhood Descent For The Unrelated Parallel Machine Scheduling Problem
Abstract
The NP-hard problem of scheduling jobs on unrelated parallel machines with the objective of minimizing the makespan is addressed. A variable neighborhood descent heuristic hybridized with mathematical programming is proposed. A new constructive heuristic is also introduced to seed the search with a solution residing in a promising region. The strength of the approach lies in the adoption of appropriately chosen optimization criteria that avoid early local optimum entrapment and in the hybridization of the heuristic with mathematical programming for the exploration of the large neighborhood structures. Experimental results on a large set of benchmark problem instances attest to the efficacy of the proposed approach.
Year
DOI
Venue
2012
10.1142/S0218213012400192
INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
Keywords
Field
DocType
Unrelated parallel machine scheduling, hybrid optimization, variable neighborhood decent, mixed-integer programming
Heuristic,Mathematical optimization,Job shop scheduling,Machine scheduling,Local optimum,Scheduling (computing),Computer science,Integer programming,Artificial intelligence,Constructive heuristic,Machine learning
Journal
Volume
Issue
ISSN
21
4
0218-2130
Citations 
PageRank 
References 
0
0.34
15
Authors
2
Name
Order
Citations
PageRank
Christoforos Charalambous1504.35
Krzysztof Fleszar236825.38