Results 1  10
of
19
Simulation Optimization: Methods And Applications
, 1997
"... Simulation optimization can be defined as the process of finding the best input variable values from among all possibilities without explicitly evaluating each possibility. The objective of simulation optimization is to minimize the resources spent while maximizing the information obtained in a simu ..."
Abstract

Cited by 32 (0 self)
 Add to MetaCart
Simulation optimization can be defined as the process of finding the best input variable values from among all possibilities without explicitly evaluating each possibility. The objective of simulation optimization is to minimize the resources spent while maximizing the information obtained in a simulation experiment. The purpose of this paper is to review the area of simulation optimization. A critical review of the methods employed and applications developed in this relatively new area are presented and notable successes are highlighted. Simulation optimization software tools are discussed. The intended audience is simulation practitioners and theoreticians as well as beginners in the field of simulation.
Statistical Analysis Of Simulation Output
, 1997
"... This paper describes, in general terms, methods to help design the runs for simulation models and interpreting their output. Statistical methods are described for several different purposes, and related problems like comparison, variance reduction, sensitivity estimation, metamodeling, and optimizat ..."
Abstract

Cited by 14 (2 self)
 Add to MetaCart
This paper describes, in general terms, methods to help design the runs for simulation models and interpreting their output. Statistical methods are described for several different purposes, and related problems like comparison, variance reduction, sensitivity estimation, metamodeling, and optimization are mentioned. The main point is to call attention to the challenges and opportunities in using simulation models carefully and effectively.
Global Search Strategies for Simulation Optimization
, 2002
"... Simulation optimization is rapidly becoming a mainstream tool for simulation practitioners, as several simulation packages include addon optimization tools. In this paper we are concentrating on an automated optimization approach that is based on adapting model parameters in order to handle uncerta ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
Simulation optimization is rapidly becoming a mainstream tool for simulation practitioners, as several simulation packages include addon optimization tools. In this paper we are concentrating on an automated optimization approach that is based on adapting model parameters in order to handle uncertainty that arises from stochastic elements of the process under study. We particularly investigate the use of global search methods in this context, as these methods allow the optimization strategy to escape from suboptimal (i.e., local) solutions and, in that sense, they improve the efficiency of the simulation optimization process. The paper compares several global search methods and demonstrates the successful application of the Particle Swarm Optimizer to simulation modeling optimization and design of a steelworks plant, a representative example of the stochastic and unpredictable behavior of a complex discrete event simulation model.
M.J.: Fuzzygenetic decision optimization for optimization of complex stochastic systems
 In: Proceedings 5th Online World Conference on Soft Computing in Industrial Applications. (2000
"... multiobjective optimization on complex stochastic systems. A stochastic simulation model estimates the results of parameter settings for the system. A fuzzy ordinal preference model aggregates these results into a single fitness value for the input parameter set. A genetic algorithm uses this fitne ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
multiobjective optimization on complex stochastic systems. A stochastic simulation model estimates the results of parameter settings for the system. A fuzzy ordinal preference model aggregates these results into a single fitness value for the input parameter set. A genetic algorithm uses this fitness value to search for a population of high performance parameter sets which achieve the modeler’s objectives. In a tactical planning experiment, FuzzyGenetic Decision Optimization enabled a human planner to develop a significantly better plan than he developed without automated assistance. 1
SIMULATION OPTIMIZATION WITH THE LINEAR MOVE AND EXCHANGE MOVE OPTIMIZATION ALGORITHM
"... ...is an algorithm based on a simulated annealing algorithm (SA), a relatively recent algorithm for solving hard combinatorial optimization problems. The LEO algorithm was successfully applied to a facility layout problem, a scheduling problem and a line balancing problem. In this paper we will try ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
...is an algorithm based on a simulated annealing algorithm (SA), a relatively recent algorithm for solving hard combinatorial optimization problems. The LEO algorithm was successfully applied to a facility layout problem, a scheduling problem and a line balancing problem. In this paper we will try to apply the LEO algorithm to the problem of optimizing a manufacturing simulation model, based on a Steelworks Plant. This paper also demonstrates the effectiveness and versatility of this algorithm. We compare the search effort of this algorithm with a Genetic Algorithm (GA) implementation of the same problem.
Problèmes de tournées de bus: une proposition de couplage de méthodes d'optimisation et de simulation
 ACTES DE LA 5ÈME CONFÉRENCE MOSIM
, 2004
"... Dans un contexte économique exigeant, les compagnies de transports publics recherchent des outils d’aide à la décision pour les guider dans l’amélioration de leurs offres de transport en réseau urbain. C’est dans cette optique que nous présentons notre modèle. Cependant, face à un problème d’optimis ..."
Abstract
 Add to MetaCart
(Show Context)
Dans un contexte économique exigeant, les compagnies de transports publics recherchent des outils d’aide à la décision pour les guider dans l’amélioration de leurs offres de transport en réseau urbain. C’est dans cette optique que nous présentons notre modèle. Cependant, face à un problème d’optimisation, deux possibilités principales s’opposent: définir un modèle mathématique et le résoudre avec des techniques avancées d’optimisation, ou alors définir un modèle de simulation et explorer les possibilités pour en dégager une bonne solution. Les deux approches ont leurs défauts: la première trouve souvent une solution optimale, mais sur un modèle trop simplifié; la seconde considère un modèle beaucoup plus complet, mais l’exploration des possibilités est limitée. Nous proposons ici une tentative de couplage de méthodes d’optimisation avec la simulation dans le but de tirer profit des deux approches. Après une présentation du modèle, nous expliquons la méthode de résolution retenue. Nous proposons ensuite d’utiliser la simulation pour affiner les données de ce modèle afin d’obtenir de bonnes solutions pratiques.
Model Enhancement: Improving Theoretical Optimization with Simulation
, 2005
"... When using optimization techniques based on mathematical models, we often need to make important simplifications. The solution thus provided, even if proven to be theoretically one of the best, might not be so good in practice. Simulation can be used to evaluate the actual performance of the solutio ..."
Abstract
 Add to MetaCart
(Show Context)
When using optimization techniques based on mathematical models, we often need to make important simplifications. The solution thus provided, even if proven to be theoretically one of the best, might not be so good in practice. Simulation can be used to evaluate the actual performance of the solution. We propose here a coupling between optimization and simulation that tries to improve the solution provided by a mathematical model. This approach, named "model enhancement" here, still focuses on optimizing the theoretical objective function, contrary to the common optimizationsimulation coupling that focuses on improving the objective function evaluated from simulation. We propose to illustrate this approach on a routing problem, and present numerical results on the quality of the solution and the efficiency of both coupling approaches.
ABSTRACT SIMULATION BASED OPTIMIZATION FOR SUPPLY CHAIN CONFIGURATION DESIGN
"... The design of a supply chain network as an integrated system with several tiers of suppliers is a difficult task. It consists of making strategic decisions on the facility location, stocking location, production policy, production capacity, distribution and transportation modes. This research develo ..."
Abstract
 Add to MetaCart
The design of a supply chain network as an integrated system with several tiers of suppliers is a difficult task. It consists of making strategic decisions on the facility location, stocking location, production policy, production capacity, distribution and transportation modes. This research develops a hybrid optimization approach to address the Supply Chain Configuration Design problem. The new approach combines simulation, mixed integer programming and genetic algorithm. The genetic algorithm provides a mechanism to optimize qualitative and policy variables. The mixed integer programming model reduces computing efforts by manipulating quantitative variables. Finally simulation is used to evaluate performance of each supply chain configuration with nonlinear, complex relationships and under more realistic assumptions. The approach is designed to be robust and could handle the large scale of the real world problems. 1
SINGLE RUN OPTIMIZATION USING THE REVERSESIMULATION METHOD
"... developed to solve system design problems which can not be expressed in explicit analytical or mathematical models. In particular, we explore a new paradigm called the “ReverseSimulation optimization method ” which is quite different from current simulation optimization methods in the literature. T ..."
Abstract
 Add to MetaCart
(Show Context)
developed to solve system design problems which can not be expressed in explicit analytical or mathematical models. In particular, we explore a new paradigm called the “ReverseSimulation optimization method ” which is quite different from current simulation optimization methods in the literature. This paper focuses on the method of online determination of steadystate, which is a very important issue in ReverseSimulation optimization, and the construction of a ReverseSimulation algorithm with expert systems. The proposed algorithm finds the steadystate of a system and an optimal state. The algorithm employs the Lyapunov exponent of Chaos theory to determine both
"The Relationship Between Religious Denomination and Church Size Upon Internal Control Practices"
"... There is an adage in management accounting that goes like this: What gets measured, gets done. Many firms are now reevaluating their performance measurement systems to ensure that performance measures are tied to critical success factors. Kaplan and Norton have proposed a balanced scorecard approac ..."
Abstract
 Add to MetaCart
(Show Context)
There is an adage in management accounting that goes like this: What gets measured, gets done. Many firms are now reevaluating their performance measurement systems to ensure that performance measures are tied to critical success factors. Kaplan and Norton have proposed a balanced scorecard approach to tracking the key elements of a firm s strategy. The balanced scorecard requires management to evaluate performance from four specific perspectives: financial, customer, internal business, and innovation and learning. This paper suggests that business professionals would do well to identify those success factors critical to their own personal and career objectives and apply a balanced scorecard approach to monitoring their personal performance and progress toward achieving their goals.