The thesis focuses on a model that seeks to address patient scheduling step of the surgical scheduling process to determine the number of surgeries to perform in a given day. Specifically, provided a master schedule that provides a cyclic breakdown of total OR availability into specific daily allocations to each surgical specialty, we look to provide a scheduling policy for all surgeries that minimizes a combination of the lead time between patient request and surgery date, overtime in the ORs and congestion in the wards. We cast the problem of generating optimal control strategies into the framework of Markov Decision Process (MDP). The Approximate Dynamic Programming (ADP) approach has been employed to solving the model which would otherwise be intractable due to the size of the state space. We assess performance of resulting policy and quality of the driven policy through simulation and we provide our policy insights and conclusions.

<p>Background and problem:</p><p>The industry of today is changing and many firms chose to internationalize due to the increased price competition. It is often cheaper to produce in a low cost country and sometimes it can be strategic to produce there in order to gain market shares.</p><p>Purpose:</p><p>The purpose of this thesis is to study the decision proc-ess when manufacturing firms choose to locate production abroad.</p><p>Theoretical framework:</p><p>Decisions have different grade of rationality and complexity. To make a decision to establish abroad is a complicated decision, which often involves many people, requires time and information in order to avoid uncertainty. The people involved in the process need to be able to both gather and use the information in order to do as good decision as possible. Many factors affect the decision concerning foreign establishment, the most occurring are low costs, better market structure and the growth potential in the area of interest.</p><p>Empirical findings:</p><p>ABA Group, Balton AB, ITAB Shop Concept and Stilexo Industry AB are the four firms in which the decision process is investigated. They have all experienced increasing competition in the more globalized market, which has forced them to establish abroad in order to survive.</p><p>Analysis and final discussion:</p><p>For all firms investigated, the decision to establish abroad was influenced by availability of information and time, the people involved and their ability to use the information. All these factors are affected by uncertainties, from which the firms not completely can protect themselves. To do as good decision as possible the firms need to be careful and consider all factors that influence the outcome.</p>

In a standard tennis match each player has an unlimited opportunity to challenge an umpire’s call, but if three incorrect challenges are made in a set he is not allowed to challenge anymore in that set. If the set goes into a tie break the limit on incorrect challenges increases by one. These limited incorrect challenges are not carried over from one set to another. So this is kind of a limited resource available to the player and if he knows how to use this resource in a best possible way, there is a scope for increasing his overall chances of winning a match. With the motive of gaining insight on when to challenge a call, we have modeled a single game in a tennis match as a Markov decision process. We have also studied the impact of variables like player’s probability of winning a point, the player’s perception of the challengability of a call and proportion of challengable calls on the decision making process. / text

This dissertation is concerned with the role of regret aversion in decision making. Specifically, it examines how regret aversion influences decision process, choice, and post-decisional behaviors and feelings. Chapter 1 provides an overview of the past empirical findings and theorizing on regret aversion. Chapter 2 examines whether regret aversion leads to a stronger or weaker preference for so-called reason-based choices (cf., Shafir, Simonson, &amp; Tversky, 1993), or options that are more easily justifiable. Specifically, four experiments test whether four well-known reason-based choice effects are amplified or attenuated when regret is made salient. These effects are the asymmetric dominance effect, the compromise effect, the select/reject effect, and the most important attribute effect. Chapter 3 reports on five experiments that examine whether regret aversion leads decision makers to engage in more careful decision processing as suggested by Janis and Mann (1977). It extends the study of regret aversion from choice behavior to decision processing. Chapter 4 studies the effects of regret aversion in repeated decisions. Specifically, it examines experimentally how decision makers handle the trade off between seeking feedback on foregone options that may facilitate learning and better decision making in future decisions, and avoiding feedback on foregone options as such feedback may cause feelings of regret. Chapter 5 summarizes the contributions of this dissertation and concludes.

A new promising approach for wireless network optimization is from a cross-layer perspective. This thesis focuses on exploiting channel state information (CSI) from the physical layer for optimal transmission scheduling at the medium access control (MAC) layer. The first part of the thesis considers exploiting CSI via a distributed channel-aware MAC protocol. The MAC protocol is analysed using a centralized design approach and a non-cooperative game theoretic approach. Structural results are obtained and provably convergent stochastic approximation algorithms that can estimate the optimal transmission policies are proposed. Especially, in the game theoretic MAC formulation, it is proved that the best response transmission policies are threshold in the channel state and there exists a Nash equilibrium at which every user deploys a threshold transmission policy. This threshold result leads to a particularly efficient stochastic-approximation-based adaptive learning algorithm and a simple distributed implementation of the MAC protocol. Simulations show that the channel-aware MAC protocols result in system throughputs that increase with the number of users. The thesis also considers opportunistic transmission scheduling from the perspective of a single user using Markov Decision Process (MDP) approaches. Both channel state information and channel memory are exploited for opportunistic transmission. First, a finite horizon MDP transmission scheduling problem is considered. The finite horizon formulation is suitable for short-term delay constraints. It is proved for the finite horizon opportunistic transmission scheduling problem that the optimal transmission policy is threshold in the buffer occupancy state and the transmission time. This two-dimensional threshold structure substantially reduces the computational complexity required to compute and implement the optimal policy. Second, the opportunistic transmission scheduling problem is formulated as an infinite horizon average cost MDP with a constraint on the average waiting cost. An advantage of the infinite horizon formulation is that the optimal policy is stationary. Using the Lagrange dynamic programming theory and the supermodularity method, it is proved that the stationary optimal transmission scheduling policy is a randomized mixture of two policies that are threshold in the buffer occupancy state. A stochastic approximation algorithm and a Q-learning based algorithm that can adaptively estimate the optimal transmission scheduling policies are then proposed.

All of us make more or less important decisions during our entire lives, in private and professional arenas. Some decisions have consequences for an individual or organization in the short term, others have long lasting consequences. This thesis concerns studies of decision processes and structures involved indecision-making with medium to long-term consequences for an organization or individual. Study I and II focus decision-making theory and judgments in procurement. Study III concerns real-life, individual career decision-making. Study I used a laboratory context for an investigation of willingness to pay (WP) for the creation of a procurement offer. Study II investigated organizational decision processes and structures of procurement of large projects in a nuclear power plant organization. Study III investigated the decision process used to make a choice between two professional training programs leading to psychotherapist certification. Study I found, that participants used a multiplicative combination of probability and profit when judging WP for the creation of a bid. Scales of subjective probability had smaller ranges than objective probability. In this context, participants were more sensitive to variation in monetary value than to probability. In Study, II it was possible to describe the procurement process in a framework of information search and decision theory. A Multi Attribute Utility Theory-inspired model was used by the staff, in the evaluations of procurement alternatives. Both compensatory (e.g. negative aspects can be compensated by positive aspects) and non-compensatory (particular “pass” levels of attributes have to be exceeded for acceptance of a choice alternative) decision rules were used. In study III it was found that a development and extension of Differentiation and Consolidation theory described individual reasons pro and con alternatives before and after the choice of a professional training program. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 1: Submitted. </p>

This thesis examines a series of problems with the goal of better understanding the fundamental dilemma of whether to invest effort in obtaining information that may lead to better opportunities in the future versus exploiting immediately available opportunities. In particular this work investigates how this dilemma is affected by competition in an evolutionary setting. To achieve this requires both the use of evolutionary game theory, and Markov decision procesess or stochastic dynamic programming. This thesis grows directly out of earlier work on the Social Learning Strategies Tournament. Although I cast the problem in the biological setting of optimal foraging theory, where it fills an obvious gap, this fundamental dilemma should also be of some interest to economists, operations researchers, as well as those working in ecology, evolution and behaviour. / Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2012-01-31 19:55:25.11

Increasing legislative and societal pressures are forcing manufacturers to become environmentally-conscious and take responsibility for the fate of their goods after they have been used by consumers. As a result, some manufacturers operate hybrid systems which produce new goods and recover used goods. Product recovery describes the process by which used products are returned to their manufacturers or sent to a specialised facility for recovery, before being sold on the original or a secondary market. The quality of the returned goods is a significant issue in product recovery systems as it can affect both the type of recovery and costs associated with it. Quality in product recovery systems has not been adequately studied, with many authors either ignoring the possibility of receiving lower quality returns, or assuming they are disposed of rather than recovered. However, such assumptions ignore the possibility that the firm might be able to salvage value from lower quality returns by using them for parts or materials. This thesis presents four models that investigate the importance of considering the quality of returns in the management of inventory in a product recovery system, by examining the cost-effectiveness of recovering both high quality and low quality returns. The first model is a deterministic lot-sizing model of a product recovery system. It was found that performing both high and low quality recovery reduced the sensitivity of the optimal cost to operational restrictions on the choice of decision variables. The second model is a discrete-time, periodic-review model formulated as a Markov decision process (MDP) and introduces uncertainty in demand, returns, and the quality of the returns. It was found that performing both types of recovery can lead to cost savings and better customer service for firms through an increased fill rate. The third model addresses those industries where produced and recovered goods cannot be sold on the same market due to customers’ perceptions and environmental legalisation. Using an MDP formulation, the model examines a product recovery system in which produced and recovered goods are sold on separate markets. The profitability of offering two-way substitution between these markets was investigated. It was found that offering substitution can allow firms to increase both their profits and fill rates. The fourth model examines the issue of separate markets and substitution in the continuous time domain using a semi-Markov decision process. The continuous nature of the model allows more detailed examination of the substitution decision. It was found that offering substitution can allow firms to increase their profit and in some cases also increase their fill rate. In some cases, production is performed less frequently when downward substitution can be offered, and recovery is performed less often when upward substitution can be offered. The findings of this thesis could be used to help a firm that is currently recovering high quality returns assess the cost-effectiveness of also recovering lower quality returns. Recovering low-quality items, rather than disposing of them, may allow a firm to increase the amount it recycles. The findings highlight the importance of considering the quality of returns when managing a product recovery system as they show that economic gains can be achieved by reusing rather than refusing low quality returns.

The thesis focuses on a model that seeks to address patient scheduling step of the surgical scheduling process to determine the number of surgeries to perform in a given day. Specifically, provided a master schedule that provides a cyclic breakdown of total OR availability into specific daily allocations to each surgical specialty, we look to provide a scheduling policy for all surgeries that minimizes a combination of the lead time between patient request and surgery date, overtime in the ORs and congestion in the wards. We cast the problem of generating optimal control strategies into the framework of Markov Decision Process (MDP). The Approximate Dynamic Programming (ADP) approach has been employed to solving the model which would otherwise be intractable due to the size of the state space. We assess performance of resulting policy and quality of the driven policy through simulation and we provide our policy insights and conclusions.

A new promising approach for wireless network optimization is from a cross-layer perspective. This thesis focuses on exploiting channel state information (CSI) from the physical layer for optimal transmission scheduling at the medium access control (MAC) layer. The first part of the thesis considers exploiting CSI via a distributed channel-aware MAC protocol. The MAC protocol is analysed using a centralized design approach and a non-cooperative game theoretic approach. Structural results are obtained and provably convergent stochastic approximation algorithms that can estimate the optimal transmission policies are proposed. Especially, in the game theoretic MAC formulation, it is proved that the best response transmission policies are threshold in the channel state and there exists a Nash equilibrium at which every user deploys a threshold transmission policy. This threshold result leads to a particularly efficient stochastic-approximation-based adaptive learning algorithm and a simple distributed implementation of the MAC protocol. Simulations show that the channel-aware MAC protocols result in system throughputs that increase with the number of users. The thesis also considers opportunistic transmission scheduling from the perspective of a single user using Markov Decision Process (MDP) approaches. Both channel state information and channel memory are exploited for opportunistic transmission. First, a finite horizon MDP transmission scheduling problem is considered. The finite horizon formulation is suitable for short-term delay constraints. It is proved for the finite horizon opportunistic transmission scheduling problem that the optimal transmission policy is threshold in the buffer occupancy state and the transmission time. This two-dimensional threshold structure substantially reduces the computational complexity required to compute and implement the optimal policy. Second, the opportunistic transmission scheduling problem is formulated as an infinite horizon average cost MDP with a constraint on the average waiting cost. An advantage of the infinite horizon formulation is that the optimal policy is stationary. Using the Lagrange dynamic programming theory and the supermodularity method, it is proved that the stationary optimal transmission scheduling policy is a randomized mixture of two policies that are threshold in the buffer occupancy state. A stochastic approximation algorithm and a Q-learning based algorithm that can adaptively estimate the optimal transmission scheduling policies are then proposed.