Abstract The framing into demand driven operations is because of the operations research modelling approach. The modelling approach requires continuous regressors and an independent response factor. The demand as an operating factor is considered as independent response factor in relation to the continuous regressors. The method validation is made along several longitudinal case studies to cover local, global and international industrial operations. The examined operational scope is from continuous operations to one-off production. Concerning scheduling, these examined demand driven, industrial operations are considered as open and dynamic, flow shop or job-shop operations. The examined managerial scope is from local work management to global industrial operations management. The theoretical framework of this study is based on operations management, productivity and controllability engineering. The strategical target is to improve productivity. The operational target setting is based on linear goal programming, streamlined demand driven material flow and specified operating factors according to this study, Forrester effect diagnostics and replenishment models. The engineering of strategical target into exact operational schedule as a task target is hard to accomplish, because of the combinatorial dynamic job-shop problem. The purpose of this study is to simplify this managerial task. These study operating factors are the heart in constructing a Decision Support System for the examined operations, alongside the method’s product flow diagnostics. This operations management method consists of the operating factors, specified in this study and these specified factors’ use in constructing a Decision Support System, by engineering current operations management system. The construct consist two parts. Firstly, the exact operational target alignment along this method diagnostics and secondly, the control mechanism according to this operational linear target. The expected managerial benefit is in productivity improvement. The practical benefits are in savings in logistics costs and improvement in customer service, due to shorten lead time and exacting delivery.

Recently an iterative method was formulated employing a new splitting strategy for the solution of tridiagonal systems of difference equations. The method was successful in solving the systems of equations arising from one dimensional initial boundary value problems, and a theoretical analysis for proving the convergence of the method for systems whose constituent matrices are positive definite was presented by Evans and Sahimi [22]. The method was known as the Alternating Group Explicit (AGE) method and is referred to as AGE-1D. The explicit nature of the method meant that its implementation on parallel machines can be very promising. The method was also extended to solve systems arising from two and three dimensional initial-boundary value problems, but the AGE-2D and AGE-3D algorithms proved to be too demanding in computational cost which largely reduces the advantages of its parallel nature. In this thesis, further theoretical analyses and experimental studies are pursued to establish the convergence and suitability of the AGE-1D method to a wider class of systems arising from univariate and multivariate differential equations with symmetric and non symmetric difference operators. Also the possibility of a Chebyshev acceleration of the AGE-1D algorithm is considered. For two and three dimensional problems it is proposed to couple the use of the AGE-1D algorithm with an ADI scheme or an ADI iterative method in what is called the Explicit Alternating Direction (EAD) method. It is then shown through experimental results that the EAD method retains the parallel features of the AGE method and moreover leads to savings of up to 83 % in the computational cost for solving some of the model problems. The thesis also includes applications of the AGE-1D algorithm and the EAD method to solve some problems of fluid dynamics such as the linearized Shallow Water equations, and the Navier Stokes' equations for the flow in an idealized one dimensional Planetary Boundary Layer. The thesis terminates with conclusions and suggestions for further work together with a comprehensive bibliography and an appendix containing some selected programs.

<p> Model-Based Systems Engineering and Logistics Engineering are emerging disciplines that offer a synergy for integrating the proactive modeling of prototype R&amp;D acquisition and industrial base sustainment support into a framework that characterizes the most influential phases of the Department of Defense ground vehicle and robotics equipment life cycle. This research enhances situational awareness of upstream factors that drive the capability and capacity constraints to leveraging new technology for sustainment risk mitigation. These capability and capacity constraints include sub-optimal supply chain coordination and limited collaboration between government R&amp;D centers. This research also demonstrates how a new business model called the Defense Mobility Enterprise solves these problems, while offering an incubator for Model-Based Systems Engineering experimentation and continuous productivity improvement. Through the successful application of SysML, the modeling language of systems engineering, this research concludes with multi-model orchestration, using the momentum of commercial-off-the-shelf tools, providing a strategic lens with which to specify, analyze, design, and verify Department of Defense ground vehicle and robotics technology transition opportunities.</p><p>

<p> Classification is a ubiquitous decision activity. Regardless of whether it is predicting the future, e.g., a weather forecast, determining an existing state, e.g., a medical diagnosis, or some other activity, classifier outputs drive future actions. Because of their importance, classifier research and development is an active field.</p><p> Regardless of whether one is a classifier developer or an end user, evaluating and comparing classifier output quality is important. Intuitively, classifier evaluation may seem simple, however, it is not. There is a plethora of classifier summary statistics and new summary statistics seem to surface regularly. Summary statistic users appear not to be satisfied with the existing summary statistics. For end users, many existing summary statistics do not provide actionable information. This dissertation addresses the end user's quandary. </p><p> The work consists of four parts: 1. Considering eight summary statistics with regard to their purpose (what questions do they quantitatively answer) and efficacy (as defined by measurement theory). 2. Characterizing the classification problem from the end user's perspective and identifying four axioms for end user efficacious classifier evaluation summary statistics. 3. Applying the axia and measurement theory to evaluate eight summary statistics and create two compliant (end user efficacious) summary statistics. 4. Using the compliant summary statistics to show the actionable information they generate.</p><p> By applying the recommendations in this dissertation, both end users and researchers benefit. Researchers have summary statistic selection and classifier evaluation protocols that generate the most usable information. End users can also generate information that facilitates tool selection and optimal deployment, if classifier test reports provide the necessary information. </p><p>

In many segments of the chemical industry, emphasis is nowadays placed upon meeting customer needs. This imposes new requirements in terms of various product quality specifications as well as due dates for deliveries, and provides an important incentive for the development of comprehensive procedures for the design and scheduling of multiproduct batch processes. Previous studies fail to address many of the specific issues involved in batch processing, such as distinct due dates, inventory costs, changeover costs, the choice between existing and new equipment, and the many potential alternatives in the synthesis of batch networks. In this thesis, flexible procedures based on the simulated algorithm have been developed to provide a global treatment of scheduling, design, synthesis, and retrofit, with special emphasis on the issues most relevant to batch processes. The flexibility of simulated annealing allows the optimization of complete economic objectives including capital, operating, labor, inventory, and changeover costs as well as penalties for early and late production. The simulated annealing algorithm has been adapted to handle the complex problem structures that arise in design and synthesis. The choice of economic objectives, the effect of uncertainties on the scheduling of flowshops, and the planning of multiplants have been investigated. Optimal scheduling and planning have been incorporated in a design and synthesis procedure to generate processes that are both economical, and capable of meeting a fluctuating demand. This procedure has been extended to retrofit design, to allow the choice between existing and new equipment, and develop a unified treatment of planning, retrofit, and design. Finally, the choice of operating conditions together with design, synthesis, and scheduling has been examined and applied to the batch production of chain growth polymers. The robustness of the optimization procedures has been checked by solving a variety of previously published examples. Significant extensions of these problems have also been developed to explore new issues and the interactions between the different decisions levels. These procedures allow a comprehensive and flexible treatment of a wide class of problems in batch design and scheduling.

In multiagent systems, computational agents search for and make contracts on behalf of the real world parties that they represent. This dissertation analyses negotiations among agents that try to maximize payoff without concern of the global good. Such a self-interested agent will choose the best negotiation strategy for itself. Accordingly, the interaction protocols need to be designed normatively so that the desired local strategies are best for the agents--and thus the agents will use them--then certain desirable social outcomes follow. The normative approach allows the agents to be constructed by separate designers and/or to represent different parties. Game theory also takes a normative approach, but full rationality of the agents is usually assumed. This dissertation focuses on situations where computational limitations restrict each agent's rationality: in combinatorial negotiation domains computational complexity precludes enumerating and evaluating all possible outcomes. The dissertation contributes to: automated contracting, coalition formation, and contract execution. The contract net framework is extended to work among self-interested, computationally limited agents. The original contract net lacked a formal model for making bidding and awarding decisions, while in this work these decisions are based on marginal approximations of cost calculations. Agents pay each other for handling tasks. An iterative scheme for anytime task reallocation is presented. Next it is proven that a leveled commitment contracting protocol enables contracts that are impossible via classical full commitment contracts. Three new contract types are presented: clustering, swaps and multiagent contracts. These can be combined into a new type, CSM-contract, which is provably necessary and sufficient for reaching a globally optimal task allocation. Next, contracting implications of limited computation are discussed, including the necessity of local deliberation scheduling, and tradeoffs between computational complexity and monetary risk when an agent can participate in multiple simultaneous negotiations. Finally, issues in distributed asynchronous implementation are discussed. A normative theory of coalitions among self-interested, computationally limited agents is developed. It states which agents should form coalitions and which coalition structures are stable. These analytical prescriptions depend on the performance profiles of the agents' problem solving algorithms and the unit cost of computation. The prescriptions differ significantly from those for fully rational agents. The developed theory includes a formal application independent domain classification for bounded rational agents, and relates it precisely to two traditional domain classifications of fully rational agents. Experimental results are presented. Unenforced exchange methods are particularly desirable among computational agents because litigation is difficult. A method for carrying out exchanges without enforcement is presented. It is based on splitting the exchange into chunks that are delivered one at a time. Two chunking algorithms are developed, as well as a nontrivial sound and complete quadratic chunk sequencing algorithm. Optimal stable strategies for carrying out the exchange are derived. The role of real-time is also analyzed, and deadline methods are developed that do not themselves require enforcement. All of these analyses are carried out for isolated exchanges as well as for exchanges where reputation effects prevail. Finally, it is argued that the unenforced exchange method hinders unfair renegotiation. The developed methods in all three subareas are domain independent. The possibility of scaling to large problem instances was shown experimentally on an ${\cal N}P$-complete distributed vehicle routing problem. The large-scale problem instance was collected from five real-world dispatch centers. (Abstract shortened by UMI.)

Research on the area of queueing networks has been extensive over the last decades. This is largely due to their ability to model many complex systems which are receiving growing attention such as flexible manufacturing systems, assembly lines, facility planning problems, computer and communication networks, transportation systems and so on. The focus of prior research has been on queueing networks with unrestricted storage or buffer capacities and queueing networks with restricted buffer capacities where external arrivals and departures are allowed, that is, open finite queueing networks. In contrast, the field of closed finite queueing networks, where no arrival to or departures from the system are allowed, has been relatively neglected due in part to their more difficult mathematical tractability. This dissertation represents a contribution to narrow this gap by concentrating on the important field of closed finite queueing networks and their optimization problems. First, an efficient numerical approximation is developed to evaluate the performance measures of this type of network where blocking can occur after service. Secondly, the optimal resource allocation problem is addressed by combining mostly elements of queueing theory and nonlinear optimization. The proposed approximation method is based on expanding and decomposing the closed network to account for the blocking phenomenon for which an adapted version of the Expansion Method is used in conjunction with a especially developed Equalization Phase and the well known Mean Value Analysis. This approximation is applicable to network topologies with tandem nodes or combinations of split and merge sequences that have exponential service times and one-server stations. The resulting numerical evaluations are computational efficient and render excellent results as compared to simulation results under a variety of testing conditions. This method is then embedded in an optimization scheme to study the resource allocation problem with the objective of optimizing a nonlinear cost function that integrates system throughput, cycle time, and the number of buffer spaces in the network. The flexibility of this objective function provides for a potentially great number of applications. The emphasis here is however on manufacturing and communication systems. The optimization procedure solves for the suboptimal buffer allocation at each node or station, and for the suboptimal number of customers or entities circulating in the closed queueing network. The solution to the buffer allocation problem is achieved via Powell's nonlinear unconstrained optimization, where necessary tests are provided to ensure deadlock-free solutions. Then, building upon this scheme, a search with backward and forward sweeps is applied to find the best setting for the number of customers. This problem is highly complex, since no known closed form expression exists for the objective function and because the problem is nonlinear and integral in nature. Discussions on the applicability, convergence, and computational analysis of the procedure are presented, as well as comparisons against pertinent simulation results.

In this dissertation, I develop a new theoretical framework for the modeling, pricing analysis, and computation of solutions to electric power supply chains with power generators, suppliers, transmission service providers, and the inclusion of consumer demands. In particular, I advocate the application of finite-dimensional variational inequality theory, projected dynamical systems theory, game theory, network theory, and other tools that have been recently proposed for the modeling and analysis of supply chain networks (cf. Nagurney (2006)) to electric power markets. This dissertation contributes to the extant literature on the modeling, analysis, and solution of supply chain networks, including global supply chains, in general, and electric power supply chains, in particular, in the following ways. It develops a theoretical framework for modeling, pricing analysis, and computation of electric power flows/transactions in electric power systems using the rationale for supply chain analysis. The models developed include both static and dynamic ones. The dissertation also adds a new dimension to the methodology of the theory of projected dynamical systems by proving that, irrespective of the speeds of adjustment, the equilibrium of the system remains the same. Finally, I include alternative fuel suppliers, along with their behavior into the supply chain modeling and analysis framework. This dissertation has strong practical implications. In an era in which technology and globalization, coupled with increasing risk and uncertainty, complicate electricity demand and supply within and between nations, the successful management of electric power systems and pricing become increasingly pressing topics with relevance not only for economic prosperity but also national security. This dissertation addresses such related topics by providing models, pricing tools, and algorithms for decentralized electric power supply chains. This dissertation is based heavily on the following coauthored papers: Nagurney, Cruz, and Matsypura (2003), Nagurney and Matsypura (2004, 2005, 2006), Matsypura and Nagurney (2005), Matsypura, Nagurney, and Liu (2006).

Several models in econometrics and finance have been proven to be computationally intractable due to their complexity. In this dissertation, we propose an evolutionary-genetic-algorithm for solving these types of problems. We extend the models so that less restrictive assumptions are required and we cope with the increased complexity by using a modified version of the evolutionary algorithm proposed for the simpler cases. More specifically, we study closer the estimation of switching regression models as introduced by Quandt (1958). The applicability of the proposed algorithms is examined through disequilibrium models; models that provide supply and demand functions for markets, when the price is not adjusted so that the quantity supplied equals the quantity demanded. We focus on the computational aspect of the deterministic switching regression models and we suggest a self-evolving genetic algorithm for solving these types of problems. As an illustration, we present results from Monte Carlo simulations and thereafter we apply the algorithm to the disequilibrium model proposed for the gasoline market during the “energy crisis”. We further extend the “general model” for markets in disequilibrium by incorporating dynamic relationships, and we examine the applicability of the proposed genetic algorithm in this more complex and realistic problem. Subsequently, the proposed genetic algorithm for the markets in disequilibrium is applied to financial models, where the structure and computational complexity are comparable with those of the switching regression models. As example, we apply the algorithm to minimizing portfolio tracking error with respect to a pre-specified index. The proposed genetic algorithm possesses unique characteristics that maximize the fitness of the algorithm itself for each individual problem. This is achieved through a Self-Evolving process that teaches the genetic algorithm what internal parameters improve the algorithm's fitness.

The primary focus of this research is on the distributed allocation of dynamically arriving interdependent tasks to the agents of a heterogeneous multi-agent system in an uncertain environment. This dissertation consists of three parts. First, we develop a centralized task allocation model which explicitly considers the communication between the agents in coordinated problem solving. The tasks enter the system with certain payment and specific processing requirements. The agents are grouped into different types based on their processing capabilities. A task can only be processed by an appropriate agent. Processing of the tasks incurs certain operational cost on the multi-agent system resulting from processing and communication costs. The performance of an agent system is defined as the discounted sum of rewards over an infinite time horizon. We formulate the task assignment problem as a Markov decision problem and show that a stationary policy exists. An action elimination procedure is proposed that decreases the action space for each state. Moreover, a heuristic policy is proposed based on certain structural properties and is shown to perform close to 1% of the policy obtained from computational methods. The second part of the dissertation studies different distributed task allocation models and shows that distributed task allocation may be preferable over centralized task allocation despite their lower performance for the agent system. Each of these decision methods are evaluated based on the computational costs incurred in the decision making and the information exchange cost between the agents. The task allocation methods are classified into different scopes such as system level, group level, and individual level. For each level of scope, we consider both off-line and on-line decision procedures. The composite performance of each model is computed in order to evaluate cost effectiveness of a decision method. We show that centralized methods may not be preferred due to excessive decision costs involved. We also investigate the performance of multi-agent systems under partial information about other agents in the system. The third and final part of the dissertation investigates the effect of organizational structures on the performance of multi-agent systems. We study different organizational structures resulting from coalition formation between individual agents in the multi-agent systems. The coalitions are formed between agents to benefit from the increased state information. (Abstract shortened by UMI.)