Simulated annealing for discrete optimization with estimation

Alkhamis, Talal M.; Ahmed, Mohamed; Tuan, Vu Kim

doi:10.1016/s0377-2217(98)00112-x

Cited by 45 publications

(40 citation statements)

References 9 publications

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“…Catoni (1992) derives finite-time estimates for the cooling schedules. Alkhamis et al (1999) extend the basic results for SA algorithms to a stochastic optimization problem where the objective function is evaluated through Monte Carlo simulation. They show that the modified SA algorithm converges in probability to a global optimum under suitable conditions on the random error.…”

Section: Simulated Annealingmentioning

confidence: 95%

Simulation optimization: A comprehensive review on theory and applications

Tekin¹,

Sabuncuoğlu

2004

IIE Transactions

207

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For several decades, simulation has been used as a descriptive tool by the operations research community in the modeling and analysis of a wide variety of complex real systems. With recent developments in simulation optimization and advances in computing technology, it now becomes feasible to use simulation as a prescriptive tool in decision support systems. In this paper, we present a comprehensive survey on techniques for simulation optimization with emphasis given on recent developments. We classify the existing techniques according to problem characteristics such as shape of the response surface (global as compared to local optimization), objective functions (single or multiple objectives) and parameter spaces (discrete or continuous parameters). We discuss the major advantages and possible drawbacks of the different techniques. A comprehensive bibliography and future research directions are also provided in the paper.

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Section: Simulated Annealingmentioning

confidence: 95%

Simulation optimization: A comprehensive review on theory and applications

Tekin¹,

Sabuncuoğlu

2004

IIE Transactions

207

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“…A variety of cooling 'schedules' have been suggested in [41] and [78]. Though simulated annealing was originally meant for optimizing deterministic functions, the framework has been extended to the case of stochastic simulations [2]. The ease of implementing a simulated annealing procedure is high and it remains a popular technique used by several commercial simulation optimization packages.…”

Section: Simulated Annealingmentioning

confidence: 99%

Simulation optimization: a review of algorithms and applications

et al. 2014

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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 simulation optimization as compared to 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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“…These algorithms may be single-solution based (e.g. simulated annealing [17]), population-based (e.g. genetic algorithms [18]) or set-based (e.g.…”

Section: Metaheuristics (Mh)mentioning

confidence: 99%

Hybrid simulation–optimization methods: A taxonomy and discussion

Figueira¹,

Almada-Lobo²

2014

Simulation Modelling Practice and Theory

205

104

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The possibilities of combining simulation and optimization are vast and the appropriate design highly depends on the problem characteristics. Therefore, it is very important to have a good overview of the different approaches. The taxonomies and classifications proposed in the literature do not cover the complete range of methods and overlook some important criteria. We provide a taxonomy that aims at giving an overview of the full spectrum of current simulation-optimization approaches. Our study may guide researchers who want to use one of the existing methods, give insights into the cross-fertilization of the ideas applied in those methods and create a standard for a better communication in the scientific community. Future reviews can use the taxonomy here described to classify both general approaches and methods for specific application fields.

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Simulated annealing for discrete optimization with estimation

Cited by 45 publications

References 9 publications

Simulation optimization: A comprehensive review on theory and applications

Simulation optimization: A comprehensive review on theory and applications

Simulation optimization: a review of algorithms and applications

Hybrid simulation–optimization methods: A taxonomy and discussion

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