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Showing 1 to 20 of 72 for “"Simulation Optimization"”.
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Nonparametric metamodeling for simulation optimization
Optimization of simulation model performance requires finding the values of the model's controllable inputs that optimize a chosen model response. Responses are usually stochastic in nature, and the cost of simulation model runs is high. The literature suggests the use of metamodels to synthesize …
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Friction stir welding simulation, optimization and design
… and investigated using multi-physics numerical simulation to predict transient temperature field, residual stress and mechanical performance of welds. The discontinuous cooling method is found to be more effective than conventional active cooling, leading to a lower drop in induced welding …
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Adaptive Sampling Line Search for Simulation Optimization
… concerned with the development of algorithms for simulation optimization (SO), a special case of stochastic optimization where the objective function can only be evaluated through noisy observations from a simulation. Deterministic techniques, when directly applied to simulation optimization …
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Multiobjective Simulation Optimization Using Enhanced Evolutionary Algorithm Approaches
… these critical real-world decisions involve the optimization not only of multiple objectives simultaneously, but also conflicting objectives, where improving one objective may degrade the performance of one or more of the other objectives. Traditional approaches for solving multiobjective …
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Simulation, optimization and instrumentation of agricultural biogas plants
… At first, a methodology for substrate inflow optimization of full-scale biogas plants is developed based on commonly measured process variables and using dynamic simulation models as well as computational intelligence (CI) methods. This methodology which is appliquable to a broad range of …
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Stochastically Constrained Simulation Optimization On Mixed-Integer Spaces
… of the system that are observable only via a simulation model parameterized by a finite number of decision variables. In solving for such a system, one faces the much harder challenge of verifying the feasibility of a potential solution. Toward this, we present cgR-SPLINE, a multistart …
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Vibration assisted machining: Modelling, simulation, optimization, control and applications
… process parameters and conditions through simulations and machining trials for through investigation of vibration assisted machining.
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A knowledge-based simulation optimization system with machine learning
… guide the search strategy selection process in simulation optimization. This system includes a framework for machine learning which enhances the knowledge base and thereby improves the ability of the system to guide optimizations. Response surfaces (i.e., the response of a simulation model to …
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Simulation Optimization Of Operating Room Schedules For Elective Orthopaedic Surgeries
… of surgeries. Through experimentation with three optimization techniques that strategically re-schedule surgeries, two showed promising results being able to reduce the total number of overtime surgeries by 12-15%, equivalent to approximately 1h of total monthly overtime. This approach serves as a …
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Building a knowledge based simulation optimization system with discovery learning
Simulation optimization is a developing research area whereby a set of input conditions is sought that produce a desirable output (or outputs) to a simulation model. Although many approaches to simulation optimization have been developed, the research area is by no means mature. This research makes …
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Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets
… corresponding to a suboptimal system is a convex optimization problem. Thus the optimal allocation may easily be obtained in the context of a "small" number of systems, where the quantifier "small" depends on the available computing resources. A consistent estimator for the optimal allocation and …
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Creating a corporate strategy for utilizing supply chain simulation, optimization and visualization
Computer based supply chain simulation, optimization, and visualization capability have changed significantly in the past 45 years, expanding capability in lockstep with increases in computational power. The increase in accessibility of relatively cheap and powerful hardware has led to the …
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Sampling Controlled Stochastic Recursions: Applications to Simulation Optimization and Stochastic Root Finding
We consider unconstrained Simulation Optimization (SO) problems, that is, optimization problems where the underlying objective function is unknown but can be estimated at any chosen point by repeatedly executing a Monte Carlo (stochastic) simulation. SO, introduced more than six decades ago through …
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Evaluation of water distribution system monitoring using a combined simulation-optimization approach
A simulation-optimization methodology was used to assess monitoring strategies for a drinking water distribution network. Multiple simulation trials of contamination events were used to create input data for an integer optimization problem. A network model, based on the Blacksburg, VA water …
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A Metaheuristic-Based Simulation Optimization Framework For Supply Chain Inventory Management Under Uncertainty
… of supply chains. The widely used traditional optimization procedures usually require an explicit mathematical model formulated based on some assumptions. The validity of such models and approaches for real world applications depend greatly upon whether the assumptions made match closely with …
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Adaptive sampling trust-region methods for derivative-based and derivative-free simulation optimization problems
<p>We consider unconstrained optimization problems where only “stochastic” estimates of the objective function are observable as replicates from a Monte Carlo simulation oracle. In the first study we assume that the function gradients are directly observable through the Monte Carlo simulation. We …
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Sequence-Based Simulation-Optimization Framework With Application to Port Operations at Multimodal Container Terminals
… optimal or near-optimal solutions, whereas simulation models can aid in predicting and studying the behavior of systems over time and monitor performance under stochastic and uncertain circumstances. Given the intensive computational effort that simulation optimization methods impose, …
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ON SOLVING MULTI-OBJECTIVE SIMULATION OPTIMIZATION BY OPTIMAL COMPUTING BUDGET ALLOCATION AND RANDOM SEARCH
… These issues can be addressed by multi-objective simulation optimization, which employs efficient simulation to evaluate solutions' performance and such information is further used to guide the optimization. Despite the advantages of simulation optimization, there exist many challenges to be …
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Simulation-optimization studies: under efficient stimulation strategies, and a novel response surface methodology algorithm
While attempting to solve optimization problems, the lack of an explicit mathematical expression of the problem may preclude the application of the standard methods of optimization which prove valuable in an analytical framework. In such situations, computer simulations are used to obtain the mean …
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