Properties of synthetic optimization problems

Reilly, Charles H.

doi:10.1109/wsc.1998.745042

Cited by 8 publications

(10 citation statements)

References 13 publications

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“…Hill (1998) addresses the increasing popularity of heuristics to solve discrete optimization problems. Due to the increased popularity of heuristics, there is an inevitable need of a standardized test for empirical evaluations as described by Hill (1998) and Reilly (1998).…”

Section: Discussionmentioning

confidence: 99%

A survey of recent advances in discrete input parameter discrete-event simulation optimization

et al. 2004

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Discrete-event simulation optimization is a problem of significant interest to practitioners interested in extracting useful information about an actual (or yet to be designed) system that can be modeled using discrete-event simulation. This paper presents a survey of the literature on discrete-event simulation optimization published in recent years (1988 to the present), with a particular focus on discrete input parameter optimization. The discrete input parameter case differentiates techniques appropriate for small and for large numbers of feasible input parameter values. Examples of applications that illustrate these methods are also discussed.

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Section: Discussionmentioning

confidence: 99%

A survey of recent advances in discrete input parameter discrete-event simulation optimization

et al. 2004

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“…In particular, we suggest finding some feasible solution to the two-dimensional knapsack problem with a quick running heuristic and use the proportion of active decision variables (X j =1) as Pr(X=1). This approach is motivated by Reilly (1998). Reilly (1998) shows how to estimate the proportion of feasible solutions for the 0-1 knapsack problem and considers two-dimensional knapsack problems when the coefficients of the constraints are correlated.…”

Section: A New Heuristic For Random Population Generationmentioning

confidence: 99%

“…This approach is motivated by Reilly (1998). Reilly (1998) shows how to estimate the proportion of feasible solutions for the 0-1 knapsack problem and considers two-dimensional knapsack problems when the coefficients of the constraints are correlated. The key points of his effort are based on 0-1 knapsack problems (equation (1) …”

Section: A New Heuristic For Random Population Generationmentioning

confidence: 99%

“….5 and is used by Reilly (1998)), t is the ratio used to establish the right-hand side values of a sample problem (the constraint slackness measure), and µ A and σ A are the mean and standard deviation of the distribution defined for the constraint coefficient vector, A. The probability of randomly generating a feasible solution is…”

Section: Proportion Of Feasible Solutions For Knapsack Problemsmentioning

confidence: 99%

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A Monte-Carlo study of genetic algorithm initial population generation methods

Hill

1999

Proceedings of the 31st Conference on Winter Simulation Simulation---a Bridge to the Future - WSC '99

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We briefly describe genetic algorithms (GAs) and focus attention on initial population generation methods for twodimensional knapsack problems.Based on work describing the probability a random solution vector is feasible for 0-1 knapsack problems, we propose a simple heuristic for randomly generating good initial populations for genetic algorithm applications to two-dimensional knapsack problems.We report on an experiment comparing a current population generation technique with our proposed approach and find our proposed approach does a very good job of generating good initial populations.

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“…The inclination to simplify the input model (via use of classical distributions, statistical independence, and time homogeneity) is equally pervasive. Warnings against such simplification are easily found: Bratley, Fox, and Schrage (1987), Kelton et al (1990), Johnson (1987), and Wilson (1997); warnings are implicit in sensitivity analyses such as Gross and Juttijudata (1997), Gross and Masi (1998), and Reilly (1998).…”

Section: The Logical and Input Modelsmentioning

confidence: 99%