Mohammad Almomani | The Hashemite University (original) (raw)

Papers by Mohammad Almomani

AIMS Mathematics

In the present article, we consider the stochastic inventory model aiming at selecting a subset o... more In the present article, we consider the stochastic inventory model aiming at selecting a subset of best policies with minimum total cost. When the computational budget is limited, then the optimal computing budget allocation (OCBA) is used for allocating the available computational budget to the different policies in order to correctly select the best policies. To measure the quality of the selection, we use the expected opportunity cost (EOC) approach that measures the total difference means between the actual best policies and the selected policies. The proposed algorithm OCBA based on EOC is implemented on a stochastic inventory example. The results show that the EOC approaches zero as the total number of computational budgets increases and that using the EOC measure gives better results than using the probability of correct selection (PCS). Moreover, the numerical results indicate that the proposed algorithm is robust.

International Journal of Electrical and Computer Engineering, 2021

Selecting a subset of the best solutions among large-scale problems is an important area of resea... more Selecting a subset of the best solutions among large-scale problems is an important area of research. When the alternative solutions are stochastic in nature, then it puts more burden on the problem. The objective of this paper is to select a set that is likely to contain the actual best solutions with high probability. If the selected set contains all the best solutions, then the selection is denoted as correct selection. We are interested in maximizing the probability of this selection; P(CS). In many cases, the available computation budget for simulating the solution set in order to maximize P(CS) is limited. Therefore, instead of distributing these computational efforts equally likely among the alternatives, the optimal computing budget allocation (OCBA) procedure came to put more effort on the solutions that have more impact on the selected set. In this paper, we derive formulas of how to distribute the available budget asymptotically to find the approximation of P(CS). We then...

International Journal of Performability Engineering, 2019

Simulation Modelling Practice and Theory, 2017

Abstract Selecting a set that contains the best simulated systems is an important area of researc... more Abstract Selecting a set that contains the best simulated systems is an important area of research. When the number of alternative systems is large, then it becomes impossible to simulate all alternatives, so one needs to relax the problem in order to find a good enough simulated system rather than simulating each alternative. One way for solving this problem is to use two-stage sequential procedure. In the first stage the ordinal optimization is used to select a subset that overlaps with the actual best systems with high probability. Then in the second stage an optimization procedure can be applied on the smaller set to select the best alternatives in it. In this paper, we consider the optimal computing budget allocation (OCBA) in the second stage that distribute available computational budget on the alternative systems in order to get a correct selection with high probability. We also discuss the effect of the simulation parameters on the performance of the procedure by implementing the procedure on three different examples. The numerical results indeed indicate that the choice of these parameters affect its performance.

Asia-Pacific Journal of Operational Research, 2016

In this paper, we consider the problem of selecting the top [Formula: see text] systems when the ... more In this paper, we consider the problem of selecting the top [Formula: see text] systems when the number of alternative systems is very large. We propose a sequential procedure that consists of two stages to solve this problem. The procedure is a combination of the ordinal optimization (OO) technique and optimal computing budget allocation (OCBA) method. In the first stage, the OO is used to select a subset that overlaps with the set of actual best [Formula: see text] systems with high probability. Then in the second stage the optimal computing budget is used to select the top [Formula: see text] systems from the selected subset. The proposed procedure is tested on two numerical examples. The numerical tests show that the proposed procedure is able to select a subset of best systems with high probability and short simulation time.

International Journal of the Physical Sciences, 2012

In this paper, we dealt with one problem in designing a production line, which is the problem of ... more In this paper, we dealt with one problem in designing a production line, which is the problem of buffer allocation. A selection approach was developed and tested for selecting the best design for a huge number of alternatives set. The proposed selection approach is a combination between cardinal and ordinal optimization. The algorithm involves four procedures; ordinal optimization, optimal computing budget allocation, subset selection and indifference-zone. The purpose of this paper is to use the proposed selection approach to find the optimal allocation of buffers that maximizes the mean production rate (throughput) in short, unbalanced and reliable production lines. Numerical results are presented to demonstrate the efficiency of the selection algorithm in finding the best buffer profile where its mean production rate is at its maximum.

Journal of the Franklin Institute, 2007

In this paper, we consider the problem of selecting a subset of k systems that is contained in th... more In this paper, we consider the problem of selecting a subset of k systems that is contained in the set of the best s simulated systems when the number of alternative systems is huge. We propose a sequential method that uses the ordinal optimization to select a subset G randomly from ...

Communications in Statistics - Simulation and Computation, 2012

In this article, we present the problem of selecting a good stochastic system with high probabili... more In this article, we present the problem of selecting a good stochastic system with high probability and minimum total simulation cost when the number of alternatives is very large. We propose a sequential approach that starts with the Ordinal Optimization procedure to ...

One of the main problems with designing a production line is to find the optimal number of buffer... more 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.

International Journal of Mathematics and Computation, 2010

Statistical selection approaches are used to identify the best simulated design from a finite set... more Statistical selection approaches are used to identify the best simulated design from a finite set of alternatives. For each alternative, stochastic simulation is used to deduce the value of performance measures. Since we used simulation to get the estimates value of performance measures, there is a potential for incorrect selection. There are two measures of selection quality; the Probability of Correct Selection and the Expected Opportunity Cost of potentially incorrect selection. In this paper, we present a combined approach for selecting a good stochastic design with high probability when the number of elements in the feasible solution set is huge. In the first stage, Ordinal Optimization approach is used to select randomly a subset that intersects with the set of the actual best k% design with high probability from the search space. The next step we used Optimal Computing Budget Allocation method to allocate the available simulation samples in a way that maximize the Probability...

We consider the effect of the initial sample sizes on the performance of Four-Stage selection app... more We consider the effect of the initial sample sizes on the performance of Four-Stage selection approach that is used in selecting a good enough simulated system, when the number of alternatives is very large. We implement Four-Stage approach on M\M\1 queuing system under some parameter settings, with a different choice of the initial sample sizes to explore the impacts on the performance of this approach. The results show that the choice of the initial sample size does affect the performance of Four-Stage selection approach.

International journal of pure and applied mathematics, 2018

The selection of the best stochastic systems from a set of a finite but very large alternative sy... more The selection of the best stochastic systems from a set of a finite but very large alternative systems is considered in this paper. Suppose we have limited computational budget to be distributed among the alternatives in order to correctly select the best alternative.Instead of distributing these computational efforts equally likely among the alternatives, the optimal computing budget allocation (OCBA) procedure puts more effort on the promising alternatives. In this article, we propose an algorithm that combines three procedures including the Ordinal Optimization (OO), the OCBA, and the ranking and selection (R&S) to select a good enough solution. The algorithm was tested on a generic example under various parameter settings and the numerical results indicates that the algorithm behaves well under various parameter sitting. AMS Subject Classification: 62F07, 90C06, 49K30

World Academy of Science, Engineering and Technology, International Journal of Mathematical and Computational Sciences, 2016

International journal of pure and applied mathematics, 2011

The article studies the effect of the initial simulation sample size on the performance of Three-... more The article studies the effect of the initial simulation sample size on the performance of Three-Stage selection approach. The aim of the study was to see the effects of the initial simulation sample size on the selection approach in selecting the best simulated designs when the number of alternatives system is large. In Three-Stage selection approach, the ordinal optimization technique is used in the first stage to select a subset that overlap with the set of the best m% designs with high probability, and then subset selection procedure is used to get a smaller subset that contains the best among the subset that is selected by the first stage. Finally, the indifference-zone procedure is used to select the best design among the survivors in the second stage. In fulfilling the aim of the study, the Three-Stage approach was implemented on M/M/1 queuing system under some parameter settings, with a different choice of the initial simulation sample sizes. The results show that the choice...

Electronic Journal of Applied Statistical Analysis, 2018

Based on the competing risks grouped data, Bayesian estimation approach is considered for the par... more Based on the competing risks grouped data, Bayesian estimation approach is considered for the parameters of the Weibull distribution and the related speciﬁc hazard and survival functions. The estimation procedures are carried out under the square error loss (SELF) and linear exponential loss (LINEX) functions. High posterior (HPD) credible intervals for the speciﬁed parameters are also obtained. The derived estimators are in explicit closed forms. Their properties and performance are illustrated through an application to real lifetime’s data and an extended simulation study. Overall results indicate that, the Bayesian estimators are dominated other estimators obtained by other methods and are recommended when continuous life testing is not available. تور استانبول تور استانبول تور آنتالیا تور آنتالیا تور پوکت ساخت اپلیکیشن صندلی اداری آگهی رایگان آموزش وردپرس آموزش وردپرس

World Academy of Science, Engineering and Technology, International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 2011

Statistical selection procedures are used to select the best simulated system from a finite set o... more Statistical selection procedures are used to select the best simulated system from a finite set of alternatives. In this paper, we present a procedure that can be used to select the best system when the number of alternatives is large. The proposed procedure consists a combination between Ranking and Selection, and Ordinal Optimization procedures. In order to improve the performance of Ordinal Optimization, Optimal Computing Budget Allocation technique is used to determine the best simulation lengths for all simulation systems and to reduce the total computation time. We also argue the effect of increment in simulation samples for the combined procedure. The results of numerical illustration show clearly the effect of increment in simulation samples on the proposed combination of selection procedure. Keywords—Indifference-Zone, Optimal Computing Budget Allocation, Ordinal Optimization, Ranking and Selection, Subset Selection.

In this paper, we consider the effect of the initial sample size on the performance of a sequenti... more In this paper, we consider the effect of the initial sample size on the performance of a sequential approach that used in selecting a good enough simulated system, when the number of alternatives is very large. We implement a sequential approach on M/M/1 queuing system under some parameter settings, with a different choice of the initial sample sizes to explore the impacts on the performance of this approach. The results show that the choice of the initial sample size does affect the performance of our selection approach. Keywords—Ranking and Selection, Ordinal Optimization, Optimal Computing Budget Allocation, Subset Selection, Indifference-Zone, Initial Sample Size.

International Journal of Electrical and Computer Engineering (IJECE), 2021

Statistical selection approaches are used to select the best stochastic system from a flnite set ... more Statistical selection approaches are used to select the best stochastic system from a flnite set of alternatives. The best system will be the system with minimum or maximum performance measure. We consider the problem of selecting the best system when the number of alternative systems is huge. Three-Stage and Four-Stage selection approaches are proposed to solve this problem. The main strategy in these two selection approaches involves a combination method of cardinal and ordinal optimization. Ordinal optimization procedure is used to reduce the number of systems in the search space such that to be appropriate for cardinal optimization procedures. Three-Stage selection approach consists three procedures; Ordinal Optimization, Subset Selection and Indifierence-Zone. While, Four-Stage selection approach consists four procedures; Ordinal Optimization, Optimal Computing Budget Allocation, Subset Selection and Indifierence-Zone. In this paper, we compare the performance between the two s...