Paderborn, Universiẗat, Diss., 2004.

<p> Outbreaks of infectious diseases, such as influenza pandemics, can have significant impacts on interdependent economic sectors, and therefore lead to major economic losses. Based on findings from the 2009 A H1N1 influenza pandemic in the National Capital Region (NCR), this research work presents a risk analysis framework using Input-Output modeling. The present modeling enables to take into consideration the dynamic interdependencies between sectors in an economic system in addition to the inherent characteristics of the economic sectors. The risk of the influenza disaster is captured by two risk metrics. First, there is level of inoperability, which represents the percentage difference between the ideal output and the degraded output. Economic loss measures the dollar value of the degraded output. A primary contribution of this work revolves around the modeling of uncertainties regarding the occurrence of a new perturbation during the recovery of interdependent economic sectors and the resulting consequences due to influenza pandemic. The effect of the new perturbation depends on the nature of the disruption and the probability of its occurrence. The new disruption could lead to either the improvement or the deterioration of the economic sectors during their recovery horizon.</p><p> The level of inoperability of the economic sectors is assessed throughout their recovery horizon from the initial outbreak of the disaster until the after-disaster point of time using a dynamic model. Moreover, the inoperability level values are used to quantify the cumulative economic losses incurred by the sectors during the recovery period. Also, an uncertainty analysis approach is introduced to account for any new perturbation occurring during the recovery horizon of economic sectors. Such uncertainty would serve to assess the potential risk of occurrence of new perturbations and their associated ripple effects. Moreover, a decision-making framework is presented to capture the risk level for the economic sectors and their respective risk metrics. The decision support system is based on a large database of level of inoperability values and economic loss values generated through the simulations of different recovery periods and trajectories.</p>

<p>The strategic importance of corporate diversification on firm?s competitive advantage and superior financial performance has been extensively studied in the strategic management literature. However, in the field of operations management, there is a lack of research on whether and how corporate diversification strategies would impact a firm?s operational performance, and vice versa. To fill this research gap, in this dissertation, we conduct three empirical studies to investigate (1) how product and international diversification strategies influence a firm?s inventory performance; (2) how a firm?s existing resources and target market uncertainty jointly impact product scope expansion; and (3) how a firm?s utilization of resources impacts product scope expansion. The first essay aims to investigate the impacts of two important corporate diversification strategies on firm?s inventory performance: product diversification (including related and unrelated diversification) and international diversification. Based upon a large firm-level data sample collected from Compustat Fundamental Annual and Compustat Segment, we find that firm?s inventory level increases with the degree of related product diversification but decreases with the degree of unrelated product diversification. There is also strong evidence that highly internationally diversified firms tend to hold more inventory. Current research has investigated the importance of product scope to a firm?s operational performance (e.g., inventory efficiency, operational costs, and service levels). However, what drives a firm?s decision to expand its product scope remains under-studied. Recent real options research points to the role of uncertainty in the target market. The second essay adds to the literature by proposing that a firm?s resources endowments and target market uncertainty jointly influence its product scope. The empirical analysis confirms a curvilinear relationship between target market uncertainty and the likelihood of product scope expansion. More importantly, we find that different categories of resources alter the influence of target market uncertainty on product scope expansion differently. Given the fact that firms are striving for improving operational efficiency and reducing operational slack (i.e., resources in excess of what is required to fulfill expected demand), the third essay focuses on the effects of a firm?s capacity, inventory, and supply chain slacks on product scope decision. Our empirical results reveal that firms with more production and inventory slacks are more likely to expand product scopes. Furthermore, our analysis demonstrates that market relatedness positively moderates the effects of production and supply chain slacks on the likelihood of product scope expansion, but negatively moderates the effect of inventory slack on the likelihood of product scope expansion.

No description available.

Technical advances in the past decade have enabled the development of very fast but expensive component placement machines for the production of Printed Circuit Boards (PCBs). However, when these fast machines have to assemble small volumes of many different types of circuit boards, the machine set-up time becomes much more important than the assembly rate of each board. In order to minimize this set up time, we must try to solve the set-up/sequencing decision problem according to either the MCS (Minimizing Component Switches) or MSI (Minimizing Switching Instants) performance criterion, or both. We examine the set-up/sequencing decision problem for a single machine (work cell) which has high mix low volume production schedules. We compare and improve methods which attempt to solve this problem, and we develop our own heuristics for solving this problem when both performance criteria are of great importance. We use lower bounds to determine how good our results are, and we discuss how to adapt methods which we have looked at to different manufacturing environments.

In this thesis, we investigate the problem of minimizing printed circuit board assembly time on high-speed pick-and-place machines. As it is considered unlikely that efficient methods will be found for this problem, heuristic methods which give near optimal solutions are sought. Several such methods currently exist, but there are no comparisons made between the different methods. One of the reasons for this is the differences in the models used. In the thesis, we develop a more general model for the problem which encompasses most other models, and is robust, i.e. it can easily be adapted to different situations. We adapt two of the heuristic methods from the literature to this model, implement and test them and report the resulting assembly times. We also provide improvements to these methods, and improved lower bounding techniques for the problem. Finally, we adapt the best method to a real-world situation, namely the environment at the Mitel Corporation in Ottawa, Canada. We test this method against the sophisticated software tool currently being used at Mitel, with good results.

A good methodology for production scheduling can result in high efficiency in reducing manufacturing costs. SIMEX is an experimental simulation-based expert production scheduler developed by the author for applications in flexible flow shop systems in a dynamic factory environment. This study introduces the general framework of SIMEX. A prototype is developed on an IBM compatible PC in Prolog, MODSIM II, Visual Basic, and Visual C++ to generate feasible and acceptable schedules with a synchronous data exchange facility. In general, primary tasks of SIMEX are to meet due dates of the final products, to increase throughput by reducing the number of setups, and to reduce inventory cost in a flexible flow shop system in real time. SIMEX has also an ability to change its expert system's rule base interactively by means of a user interface. The expert system module of SIMEX allows to use heuristics, and production rules which are the simplifications that help limit the search for possible problem solutions and handling unexpected events. Simulation-based scheduler written in MODSIM II, is another module of SIMEX. It generates the schedules, repeatedly, to analyze and verify proposed design and alternatives. (Abstract shortened by UMI.)

The purpose of the thesis is to explore the design of a planning system applicable to any formal organization. To do this, issues which are related to planning systems of various types (corporate, voluntary, public) are examined in chapter I. In order to take these issues into account, the chapters II and III indicate that the concepts of system and of design should be better understood. This provides tentative guidelines for the design of systems applicable to the management of organizations (chapter IV). These first four chapters make up the first part of the thesis and provide a review of some of the basic concepts which are needed to formulate a planning system of the type envisaged. The second part of the thesis essentially describes the various components of the planning system using the guidelines and concepts provided in the first part. In chapter V, an overview of the system is provided while chapters VI, VII and VIII focus on three aspects of the planning system. Chapter VI, 'Planning Operations,' essentially revises already provided steps of the planning process and chapter VII provides guidelines for 'planning authority and responsibilities' which do not appear to have been extensively discussed in previous literature on planning systems. As a conclusion, chapter VIII presents a series of implications of a general planning system for regional planning theory and practice.

The primary focus of this research is to develop an integrated methodology for Adaptable Evacuation Planning (AEP). In case of regional evacuation caused by a hazardous event, one of main objectives of AEP is the optimal design and analysis of evacuation routes in transportation networks that will minimize total clearance time, traveled distance, and potential congestion on roads to ensure overall safety of the evacuated population. The problem under analysis is complex and challenging due to its multi-objective nature, potential congestion, blocking and queueing along routes. In addition, the hazardous event, which caused the evacuation may evolve and affect the population on egress routes, deplete the capacity of the road network and therefore make the initial assumptions of the evacuation policy invalid. To cope with the complexity of the problem, we consider it as an interaction of events in two overlapping and orthogonal networks. The first network represents a surface wild-fire propagation through a complex landscape. The second is a regional evacuation network for which route assignment optimization models are suggested. The first model utilizes a Delaunay triangulation to represent surface fire spread as movement of the fire event within the network. A data dependent procedure to construct the triangulation and estimate the rate of spread along the edges of the network is discussed. After the Delaunay triangulation is constructed, a two pass shortest path algorithm is incorporated to estimate the minimum travel time paths and fire event arrival times. In the next part of the dissertation, an integer programming (IP) formulation and model for optimal route assignment is presented, which utilizes state dependent queueing models to cope with congestion and time delays on road links. State dependent simulation software is used to evaluate performance measures of the evacuation plan: clearance time, total distance travelled and blocking probabilities. The resulting methodology allows a decision maker to adapt routing policies effectively, in case of change in hazardous event behavior, road infrastructure failure, or traffic incidents. The third model integrates the evacuation model and the fire event model and allows one to reroute the population dynamically. Finally, in the third model demonstration we illustrate proposed methodology with a case study, where regional evacuation for the Western Massachusetts is modeled.

The Multi-Mode Resource Investment Problem (MMRIP), which is a specific class of project scheduling problems, is the topic of this dissertation. A project consists of tasks that follow a specific precedence order, with each task being executed in one of its available modes. Modes for each task are different alternatives for completing a task, and may differ in their resource consumption and duration. The goal in the MMRIP is to select a mode and starting time for each task so that the project due date is met with minimal resource investment. This research is inspired by a real scheduling problem from a large engineering organization, but has applications in many other areas such as service industry and production scheduling. Even with such wide spread applicability, a review of the literature in project scheduling and related areas shows that work in the MMRIP is limited and no practical and effective methods is discovered. The opportunity exists to make both a theoretical and practical contribution in project scheduling research. In this dissertation an effective heuristic for this computationally intractable problem is developed and documented. This enhanced priority rule heuristic simultaneously considers both due date constraints and resource usage to select and schedule tasks in one decision step. This differs from previous priority rule heuristics that require two decision steps. Modifications, including stochastic search and local improvement techniques, are also developed and tested. Computational tests of the new heuristics and comparisons to existing heuristic are conducted. With 600 test instances, the best of the new heuristics performs 15% better than the best existing heuristic. The new heuristic (without stochastic search, or local improvement implemented) performs, on average, 12.6% above optimal in 30 test instances. Computation times with the new heuristic are negligible on a typical personal computer (1999 processor), for a real size problem of 35 tasks. The inclusion of stochastic search techniques takes the computation times up to several minutes.