Convergence of an annealing algorithm
Mathematical Programming: Series A and B
Stochastic discrete optimization
SIAM Journal on Control and Optimization
Nelder-Mead simplex modifications for simulation optimization
Management Science
Nested Partitions Method for Global Optimization
Operations Research
Additional Perspectives on Simulation for Optimization
INFORMS Journal on Computing
Simulation Optimization: Integrating Research and Practice
INFORMS Journal on Computing
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Service level management of nonstationary supply chain using direct neural network controller
Expert Systems with Applications: An International Journal
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This paper presents preliminary work done on simulation-based optimization of a stochastic material-dispatching system in a retailer network. The problem we consider is one of determining the optimal number of trucks and quantities to be dispatched in such a system. Theoretical solution models for versions of this problem can be found in the literature. Unlike most theoretical models, we can accommodate many real-life considerations, such as arbitrary distributions of the governing random variables, and all important cost elements, such as inventory-holding costs, stock-out costs, and transportation costs. We have used two techniques, namely, neuro-response surfaces and simulated annealing, for optimizing our system. We have also used a problem-specific heuristic, known as the mean demand heuristic, to provide us with a good starting point for simulated annealing and a benchmark for our other methods. Some computational results are also provided.