Show simple item record

dc.contributor.authorMahootchi, Masoud 21:11:13 (GMT) 21:11:13 (GMT)
dc.description.abstractIn this thesis, modeling and optimization in the field of storage management under stochastic condition will be investigated using two different methodologies: Simulation Optimization Techniques (SOT), which are usually categorized in the area of Reinforcement Learning (RL), and Nonlinear Modeling Techniques (NMT). For the first set of methods, simulation plays a fundamental role in evaluating the control policy: learning techniques are used to deliver sub-optimal policies at the end of a learning process. These iterative methods use the interaction of agents with the stochastic environment through taking actions and observing different states. To converge to the steady-state condition where policies and value functions do not change significantly with the continuation of the learning process, all or most important states must be visited sufficiently. This might be prohibitively time-consuming for large-scale problems. To make these techniques more efficient both in terms of computation time and robust optimal policies, the idea of Opposition-Based Learning (OBL-Type I and Type II) is employed to modify/extend popular RL techniques including Q-Learning, Q(λ), sarsa, and sarsa(λ). Several new algorithms are developed using this idea. It is also illustrated that, function approximation techniques such as neural networks can contribute to the process of learning. The state-of-the-art implementations usually consider the maximization of expected value of accumulated reward. Extending these techniques to consider risk and solving some well-known control problems are important contributions of this thesis. Furthermore, the new nonlinear modeling for reservoir management using indicator functions and randomized policy introduced by Fletcher and Ponnambalam, is extended to stochastic releases in multi-reservoir systems. In this extension, two different approaches for defining the release policies are proposed. In addition, the main restriction of considering the normal distribution for inflow is relaxed by using a beta-equivalent general distribution. A five-reservoir case study from India is used to demonstrate the benefits of these new developments. Using a warehouse management problem as an example, application of the proposed method to other storage management problems is outlined.en
dc.publisherUniversity of Waterlooen
dc.subjectreinforcement learningen
dc.subjectnonlinear modelsen
dc.subjectstorage managementen
dc.subjectreservoir managementen
dc.titleStorage System Management Using Reinforcement Learning Techniques and Nonlinear Modelsen
dc.typeDoctoral Thesisen
dc.subject.programSystem Design Engineeringen Design Engineeringen
uws-etd.degreeDoctor of Philosophyen

Files in this item


This item appears in the following Collection(s)

Show simple item record


University of Waterloo Library
200 University Avenue West
Waterloo, Ontario, Canada N2L 3G1
519 888 4883

All items in UWSpace are protected by copyright, with all rights reserved.

DSpace software

Service outages