Pareto simulated annealing and colonial competitive algorithm to solve an offline scheduling problem with rejection

Abstract The present paper considers the problem of scheduling a set of jobs where some jobs may be rejected. The objective function consists of minimizing two criteria simultaneously: the sum of the weighted completion times of the accepted jobs and the sum of the rejection costs. Although the char...

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Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part B, Journal of engineering manufacture Journal of engineering manufacture, 2010-07, Vol.224 (7), p.1119-1131
Main Authors: Jolai, F, Sangari, M S, Babaie, M
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Computer simulation
Costs
Criteria
Decision theory. Utility theory
Exact sciences and technology
Game theory
Heuristic methods
Mathematical models
Mathematical programming
Mechanical engineering
Mechanical engineers
Multiple objective analysis
Operational research and scientific management
Operational research. Management science
Optimization
Pareto optimality
Pareto optimum
Production scheduling
Rejection
Scheduling
Scheduling, sequencing
Simulated annealing
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Summary:Abstract The present paper considers the problem of scheduling a set of jobs where some jobs may be rejected. The objective function consists of minimizing two criteria simultaneously: the sum of the weighted completion times of the accepted jobs and the sum of the rejection costs. Although the characteristics of this problem have been discussed in the literature, no solution algorithm has yet been proposed. Herein, solution algorithms are developed using two meta-heuristic methods: Pareto simulated annealing (PSA), a multiple-objective optimization approach; and colonial competitive algorithm (CCA), a novel method which is adopted for the first time in a discrete multiple-objective optimization problem. Computational testing illustrates the practicality of both algorithms to find a good estimation of the Pareto optimal set. The quality of the proposed algorithms are evaluated and compared by some available performance measures and a new measure introduced in the paper. The comparative results show that CCA offers better estimation of the Pareto optimal set than PSA.
ISSN:0954-4054
2041-2975
DOI:10.1243/09544054JEM1746