Author
Talal M. Alkhamis
Bio: Talal M. Alkhamis is an academic researcher from Kuwait University. The author has contributed to research in topics: Simulated annealing & Stochastic optimization. The author has an hindex of 11, co-authored 23 publications receiving 711 citations.
Papers
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TL;DR: Experimental results show that by using current hospital resources, the optimization simulation model generates optimal staffing allocation that would allow 28% increase in patient throughput and an average of 40% reduction in patients' waiting time.
335 citations
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TL;DR: A new iterative method is presented that combines the simulated annealing method and the ranking and selection procedures for solving discrete stochastic optimization problems and is guaranteed to converge almost surely to a global optimal solution.
83 citations
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TL;DR: A modified SA algorithm is presented and it is shown that under suitable conditions on the random error, the modifiedSA algorithm converges in probability to a global optimizer.
55 citations
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TL;DR: Computational results and comparisons demonstrate the efficiency of the simulated annealing (SA) based heuristic in terms of solution quality and computational time for the unconstrained quadratic pseudo-Boolean function.
49 citations
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TL;DR: In this paper, an integrated approach of simulation and optimization is presented to determine the design parameters of stochastically constrained systems where the measure of performance is available only via simulation, and a modified rejection/acceptance criterion is presented for the proposed SA algorithm taking into consideration the stochastic system constraints.
36 citations
Cited by
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TL;DR: In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed.
Abstract: Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this field.
638 citations
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TL;DR: The literature on the unconstrained binary quadratic program is surveyed, providing an overview of the applications and solution methods.
Abstract: In recent years the unconstrained binary quadratic program (UBQP) has grown in importance in the field of combinatorial optimization due to its application potential and its computational challenge. Research on UBQP has generated a wide range of solution techniques for this basic model that encompasses a rich collection of problem types. In this paper we survey the literature on this important model, providing an overview of the applications and solution methods.
340 citations
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TL;DR: Experimental results show that by using current hospital resources, the optimization simulation model generates optimal staffing allocation that would allow 28% increase in patient throughput and an average of 40% reduction in patients' waiting time.
335 citations
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01 Jan 2002
TL;DR: A number of selected topics that were published in the simulated annealing literature during the past decade are reviewed, including generalized convergence results, new performance properties, improved variants, genetic hybrids, and approaches to general mathematical programming models.
Abstract: We review a number of selected topics that were published in the simulated annealing literature during the past decade. The emphasis of the presentation is on theoretical and general results. The presentation of the novel features include generalized convergence results, new performance properties, improved variants, genetic hybrids, and approaches to general mathematical programming models.
310 citations
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TL;DR: Simulation optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation as discussed by the authors, where discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise, etc.
Abstract: Simulation optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation—discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise—various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in SO as compared to algebraic model-based mathematical programming, makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.
284 citations