Stochastic discrete optimization
SIAM Journal on Control and Optimization
Accelerating the convergence of the stochastic ruler method for discrete stochastic optimization
Proceedings of the 29th conference on Winter simulation
Iterative ranking-and-selection for large-scale optimization
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Accelerating the convergence of random search methods for discrete stochastic optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the 32nd conference on Winter simulation
Proceedings of the 33nd conference on Winter simulation
Stochastic Comparison Algorithm for Discrete Optimization with Estimation
SIAM Journal on Optimization
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
Nested Partitions Method for Global Optimization
Operations Research
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A Multiple Attribute Utility Theory Approach to Ranking and Selection
Management Science
Simulation-based optimization using simulated annealing with confidence interval
WSC '04 Proceedings of the 36th conference on Winter simulation
Optimal computing budget allocation for multi-objective simulation models
WSC '04 Proceedings of the 36th conference on Winter simulation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
A multi-objective selection procedure of determining a Pareto set
Computers and Operations Research
A new perspective on feasibility determination
Proceedings of the 40th Conference on Winter Simulation
Multi-objective COMPASS for discrete optimization via simulation
Proceedings of the Winter Simulation Conference
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In this paper, we consider a multi-objective simulation optimization problem with three features: huge solution space, high uncertainty in performance measures, and multi-objective problem which requires a set of nondominated solutions. Our main purpose is to study how to integrate statistical selection with search mechanism to address the above difficulties, and to present a general solution framework for solving such problems. Here due to the multi-objective nature, statistical selection is done by the multi-objective computing budget allocation (MOCBA) procedure. For illustration, MOCBA is integrated with two meta-heuristics: multi-objective evolutionary algorithm (MOEA) and nested partitions (NP) to identify the nondominated solutions for two inventory management case study problems. Results show that, the integrated solution framework has improved both search efficiency and simulation efficiency. Moreover, it is capable of identifying a set of non-dominated solutions with high confidence.