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In the area of discrete optimization via simulation (DOvS), optimization over rank values has been of concern in computer science and, more recently, in multi-fidelity simulation optimization. Specifically, Chen et al. (2015) proposes the concept of Ordinal Transformation to translate multi-dimensional discrete optimization problems into single-dimensional problems which are simpler, and the transformed...
E-commerce has become an increasingly relevant business in Southeast Asia. Effective warehouse management in terms of order picking is a key competitive advantage in this industry. Fashion products are particularly difficult to efficiently manage in a warehouse as they have high demand variability, with a short shelf-life and very little replenishment. In this work, after a detailed analysis of demand...
Selecting the optimal subset is highly beneficial to numerous developments in simulation optimization. This paper studies the problem of maximizing the probability of correctly selecting the top- $m$ designs out of $k$ designs under a computing budget constraint. We develop a new procedure which is more efficient and robust than currently existing procedures in the literature. We also provide an...
In simulation-optimization, the accurate evaluation of candidate solutions can be obtained by running a high-fidelity model, which is fully featured but time-consuming. Less expensive and lower fidelity models can be particularly useful in simulation-optimization settings. However, the procedure has to account for the inaccuracy of the low fidelity model. Xu et al. (2015) proposed the MO2TOS, a Multi-fidelity...
In this paper, we develop an efficient computing budget allocation rule to run simulation for a single design whose transient mean performance follows a certain underlying function, which enables us to obtain more accurate estimation of design performance by doing regression. The sequential sampling constraint is imposed so as to fully utilize the information along the simulation replication. We formulate...
How to maximize the probability of correctly selecting the top-m designs out of k designs under a computing budget constraint is crucial in simulation optimization. We develop a new procedure to be more efficient and robust than existing ones. The asymptotic convergence rate of this new procedure achieves higher convergence rate than others in correct selection probability for subset selection problems...
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