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.

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A survey on metaheuristics for stochastic combinatorial optimization

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.

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The unconstrained binary quadratic programming problem: a survey

Simulation optimization for an emergency department healthcare unit in Kuwait

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.

Essays and Surveys in Metaheuristics

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.

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Simulation optimization: a review of algorithms and applications

Simulation is an essential tool for performance evaluation of many practical systems where planners typically want to know how the system will perform under various parameter settings. Since large-scale simulation may require great amount of computer time and storage, appropriate statistical analysis can become quite costly. In this paper, we develop an interpolation technique as an effective tool for estimating system respones to parametric perturbations in simulation. We also analyze the usefulness of the continuous-time Markov chains frame-work to find the likelihood ratio (Radon- Nikodym derivative) for Markovian single server queueing models. We provide numerical experiments that demonstrate how the interpolation technique significantly outperform the likelihood ratio performance extrapolation technique in the context of the Markovian queueing models in transient analysis.

https://www.informs-sim.org/wsc04papers/199.pdf

Sensitivity analysis for transient single server queuing models using an interpolation approach

On Optimizing Maintenance and Production Scheduling of Oil Wells

Computational method for unconstrained optimization functions with noise

In this paper we develop two efficient discrete stochastic search methods based on random walk procedure for maximizing system reliability subjected to imperfect fault coverage where uncovered component failures cause immediate system failure, even in the presence of adequate redundancy. The first search method uses a sequential sampling procedure with fixed boundaries at each iteration. We show that this search process satisfies local balance equations and its equilibrium distribution gives most weight to the optimal solution. We also show that the solution that has been visited most often in the first m iterations converges almost surely to the optimal solution. The second search method uses a sequential sampling procedure with increasing boundaries at each iteration. We show that if the increase occurs slower than a certain rate, this search process will converge to the optimal set with probability 1. We consider the system where reliability cannot be evaluated exactly but must be estimated through Monte Carlo simulation. Copyright © 2008 John Wiley & Sons, Ltd.

Talal M. Alkhamis

Papers

Sensitivity analysis for transient single server queuing models using an interpolation approach

On Optimizing Maintenance and Production Scheduling of Oil Wells

Computational method for unconstrained optimization functions with noise

Random walk search procedures for reliability optimization of systems with fault tolerance