Subset selection of best simulated systems

Alrefaei, Mahmoud H.; Almomani, Mohammad H.

doi:10.1016/j.jfranklin.2006.02.020

Cited by 16 publications

(4 citation statements)

References 8 publications

(9 reference statements)

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“…As mentioned in Section 1, while most of the R&S algorithms aim choosing the (single) best process [1,9], certain others search for a subset of processes [10,11,3]. Subset selection is essential in evaluating the performance of complex systems using stochastic simulation.…”

Section: Background and Related Workmentioning

confidence: 99%

Revisiting the Algorithm RBSC-SubGen:

Supitayakul,

Furuya,

Yucel

et al. 2023

Information Engineering Express

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This study focuses on the algorithm RBSC-SubGen, which is originally offered for generation of vocabulary decks but can potentially be applied for a variety of problems. We first study the resilience of RBSC-SubGen against various hyper-parameters by testing it under various constraints. Our results indicate that RBSC-SubGen is sensitive to subset size S, followed by desired RBSC coefficient ρ * , permissible disparity ε and, finally, parent set size L. We then offer a simple modification for reducing the computational load of RBSC-SubGen and avoiding saturation. In particular, we offer to carry out an intermediate-level verification of the desired conditions. Additional tests show that the rate of saturation and the number of iterations decrease, whereas the disparity in the RBSC coefficient increases within acceptable limits.

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Section: Background and Related Workmentioning

confidence: 99%

Revisiting the Algorithm RBSC-SubGen:

Supitayakul,

Furuya,

Yucel

et al. 2023

Information Engineering Express

View full text Add to dashboard Cite

show abstract

“…To achieve this target Chen et al [19] proposed the optimal computing budget allocation (OCBA) approach that gives a large number of simulation samples to the designs that have a great effect on identifying the best design, whereas it gives a limited simulation sample for those designs that have little effect on identifying the best one. Most research on the same framework has focused on selecting the single best design, see Almomani and Abdul Rahman [20], Almomani and Abdul Rahman [21], Almomani et al [22], Almomani et al [23], Almomani and Alrefaei [24], Alrefaei and Almomani [25], and there has been no research involving subset selection. Chen et al [4], Chen et al [26] fill this gap by providing an efficient allocation approach for selecting the top m designs, known as (OCBA-m) approach.…”

Section: Computing Budget Allocation For Selecting An Optimal Subsetmentioning

confidence: 99%

Efficient Approach for Selecting the Best Subset of Buffer Profile

Almomani¹

2014

IJOPCSM

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One of the main problems with designing a production line is to find the optimal number of buffers between workstations in order to maximizes the throughput. This problem known as buffer allocation problem. Previous work in this problem focus on selecting a single buffer profile that has the maximum throughput. The objective in this paper would be to selecting from a large number of alternatives, the best subset of buffer profiles where its throughput are at its maximum. The ordinal optimization with optimal computing budget allocation approaches will be used to isolating the best subset of buffer profile, where its throughput is maximum, from the set of all alternatives. Numerical results show that the proposed algorithm finds the best subset of the puffer allocation with high probability and small replications numbers of samples.

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“…The objective of the OO procedure is to isolate a subset of good systems with high probability, then any simulation optimization procedure can be used to locate the optimal solution(s) from the isolated set. Many sequential selection procedures are proposed to select a good system when the number of alternatives is large, see Almomani and Rahman [8] , Alrefaei and Almomani [9] , Almomani and Alrefaei [10] . All these procedures are still focused on selecting a single best system or selecting a subset containing one of the best systems.…”

Section: Introductionmentioning

confidence: 99%