Scenarios and policy aggregation in optimization under uncertainty
Mathematics of Operations Research
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Ant Colony Optimization
Simulation optimization: simulation optimization
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
An Ant Colony Optimization Approach to Multi-Objective Supply Chain Model
SSIRI '08 Proceedings of the 2008 Second International Conference on Secure System Integration and Reliability Improvement
Integrated multistage logistics network design by using hybrid evolutionary algorithm
Computers and Industrial Engineering
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
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This paper presents a methodology framework for the design of robust and effective military supply networks integrating various supply chain management dimensions. The proposed network design approach accounts for dynamic market demand, capacity, supply and resource conditions in a time-varying uncertain environment. The framework is based upon a two-level decomposition scheme combining design and user model components. The proposed stochastic multi-stage design model problem consists of determining the number and location of facilities (depots) required to satisfy an anticipated set of customer's demands and customer allocation (mission) to depots over a given time horizon. The user model is exploited to produce scenario-based anticipations to the design model required for network design problem-solving, and to assess network design solutions. The user model component mixes lot-sizing decisions with transportation assets assignments. Simulation is expected to be used to dynamically generate stochastic events supporting the construction of solution at both levels. Preliminary results on a military operational support hubs case study are reported and briefly analyzed for a simplified asset pre-positioning problem.