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

A comparison between simulated annealing, genetic algorithm and tabu search methods for the unconstrained quadratic Pseudo-Boolean function

A two-stage stochastic programming model for the parallel machine scheduling problem with machine capacity

This paper develops a variant of simulated annealing (SA) algorithm for solving discrete stochastic optimization problems where the objective function is stochastic and can be evaluated only through Monte Carlo simulations. In the proposed variant of SA, the Metropolis criterion depends on whether the objective function values indicate statistically significant difference at each iteration. The differences between objective function values are considered to be statistically significant based on confidence intervals associated with these values. Unlike the original SA, our method uses a constant temperature. We show that the configuration that has been visited most often in the first m iterations converges almost surely to a global optimizer.

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Simulation-based optimization using simulated annealing with confidence interval

Refinery units maintenance scheduling using integer programming

Scheduling jobs with different processing times and distinct known due dates on parallel machines (which may be idle) so as to minimise the total weighted earliness and tardiness is NP-hard. It occurs frequently in industrial settings with a just-in-time production environment. Here, small instances of this problem are tackled via an exact mixed-integer program, whereas larger instances are approximately solved using hybrid algorithms. The computational investigation discerns what makes a difficult instance, and demonstrates the efficiency of the approach.

Talal M. Alkhamis

Papers

A comparison between simulated annealing, genetic algorithm and tabu search methods for the unconstrained quadratic Pseudo-Boolean function

A two-stage stochastic programming model for the parallel machine scheduling problem with machine capacity

Simulation-based optimization using simulated annealing with confidence interval

Refinery units maintenance scheduling using integer programming

Minimising total weighted earliness and tardiness on parallel machines using a hybrid heuristic