Convergence properties of ordinal comparison in the simulation of discrete event dynamic systems
Journal of Optimization Theory and Applications
Universal alignment probabilities and subset selection for ordinal optimization
Journal of Optimization Theory and Applications
An explanation of ordinal optimization: soft computing for hard problems
Information Sciences: an International Journal
Universal alignment probability revisited
Journal of Optimization Theory and Applications
Constraint ordinal optimization
Information Sciences—Applications: An International Journal
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Comparison of selection rules for ordinal optimization
Mathematical and Computer Modelling: An International Journal
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Ordinal Optimization (OO) is a useful simulation-based approach for stochastic optimization problems such as the problems in Discrete Event Dynamic Systems (DEDS). However, OO cannot be applied directly for the problem since many infeasible decisions cannot be excluded from ordinal comparison without extensive computation involving the expectation operation. In this paper, a new approach for solving constrained ordinal optimization (COO) problems is presented. The key idea of our method for constrained OO problems is to estimate the feasibility of decisions and to choose selected subset based on the estimated feasibility. Any crude method such as the one based on rough set theory developed in our previous work can be applied to determine the decision feasibility efficiently. The algorithm for subset selection and the procedure of Blind Picking with Feasibility Model (BPFM) for COO are derived in the paper. The infeasible decisions are excluded by an imperfect feasibility model in the procedure of subset selection. The performance of the new method is evaluated and compared with the regular OO method. Numerical testing with two examples including the planning problem of a practical remanufacturing system shows that to meet the same required alignment probability, BPFM is more efficient than pure Blind Picking in regular OO.