New development of optimal computing budget allocation for discrete event simulation
Proceedings of the 29th conference on Winter simulation
A ranking and selection project: experiences from a university-industry collaboration
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Simulation optimization: a review, new developments, and applications
WSC '05 Proceedings of the 37th conference on Winter simulation
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Minimum price is not the only objective that companies pursue when sourcing their materials. Selecting the best supplier entails looking for the best quality as well as the most reliable delivery. This work suggests a Multi-Criteria objective function that linearly aggregates a number of Taguchi loss functions, which represent the criteria of price, quality, and delivery. We initially recommend a framework to represent the market and then generate test data to represent the different market scenarios. We introduce randomness into this framework in order to achieve a highly realistic assumption. This study then employs the Optimal Computation Budget Allocation (OCBA) algorithm to choose the best supplier. OCBA solutions are benchmarked against the deterministic solution to check OCBA's ability to find the optimal solution. OCBA solutions are also compared to an Equal Allocation (EA) algorithm to verify their effectiveness in terms of minimizing the costs of sampling.