Scheduling with multiple performance measures: the one-machine case
Management Science
Analytic evaluation of multi-criteria heuristics
Management Science
A comparison of neighborhood search techniques for multi-objective combinatorial problems
Computers and Operations Research
Multi-objective genetic algorithm and its applications to flowshop scheduling
Computers and Industrial Engineering
Bicriterion scheduling of identical processing time jobs by uniform processors
Computers and Operations Research
A genetic alorithm for multiple objective sequencing problems in mixed model assembly lines
Computers and Operations Research
Parallel machine scheduling with earliness and tardiness penalties
Computers and Operations Research
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Pareto OptimalityGA-Easiness and Deception (Extended Abstract)
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Some Methods for Nonlinear Multi-objective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Computers and Operations Research
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Planning with partial preference models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Generating diverse plans to handle unknown and partially known user preferences
Artificial Intelligence
Hi-index | 0.00 |
The quality of an approximate solution for combinatorial optimization problems with a single objective can be evaluated relatively easily. However, this becomes more difficult when there are multiple objectives. One potential approach to solving multiple criteria combinatorial optimization problems when at least one of the single objective problems is NP-complete, is to use an a posteriori method that approximates the efficient frontier. A common difficulty in this type of approach, however, is evaluating the quality of approximate solutions, since sets of multiple solutions should be evaluated and compared. This necessitates the use of a comparison measure that is robust and accurate. Furthermore, a robust measure plays an important role in metaheuristic optimization for 驴tuning驴 various parameters for evolutionary algorithms, simulated annealing, etc., which are frequently employed for multiple criteria combinatorial optimization problems. In this paper, the performance of a new measure, which we call Integrated Convex Preference (ICP) is compared to that of other measures appearing in the literature through numerical experiments驴specifically, we use two a posteriori solution techniques based on genetic algorithms for a bi-criteria parallel machine scheduling problem and evaluate their performance (in terms of solution quality) using different measures. Experimental results show that the ICP measure evaluates the solution quality of approximations robustly (i.e., similar to visual comparison results) while other alternative measures can misjudge the solution quality. We note that the ICP measure can be applied to other non-scheduling multiple objective combinatorial optimization problems, as well.