Currently, capacity planning is receiving more emphasis in management of operations in Industrial Engineering because insufficient capacity may lead to deteriorating delivery performance and high work-in-process inventories. On the other hand excess capacity may lead to wastage of resources. Even the most modern and sophisticated capacity planning systems may face a great deal of uncertainty, imprecision and vagueness due to uncertain market demand, set up resources, capacity constraints, pessimistic time standards, and subjective beliefs of managers etc., leading to inferior planning decisions. Under such circumstances fuzzy models which explicitly consider these uncertainties, generate more robust, flexible and efficient planning. The traditional fuzzy logic-based models though are capable of dealing with some complex capacity-planning systems where various uncertain parameters and vagueness are involved, yet they use complex membership functions to calculate the degree of truth that involve complicated, time consuming and tedious mathematical operations. In this thesis, the solution techniques and methods developed are based on possibility theory. These techniques not only eliminate the need of calculation of complex membership functions but also yield crisp answers to fuzzy problems in capacity planning.

Currently, capacity planning is receiving more emphasis in management of operations in Industrial Engineering because insufficient capacity may lead to deteriorating delivery performance and high work-in-process inventories. On the other hand excess capacity may lead to wastage of resources. Even the most modern and sophisticated capacity planning systems may face a great deal of uncertainty, imprecision and vagueness due to uncertain market demand, set up resources, capacity constraints, pessimistic time standards, and subjective beliefs of managers etc., leading to inferior planning decisions. Under such circumstances fuzzy models which explicitly consider these uncertainties, generate more robust, flexible and efficient planning. The traditional fuzzy logic-based models though are capable of dealing with some complex capacity-planning systems where various uncertain parameters and vagueness are involved, yet they use complex membership functions to calculate the degree of truth that involve complicated, time consuming and tedious mathematical operations. In this thesis, the solution techniques and methods developed are based on possibility theory. These techniques not only eliminate the need of calculation of complex membership functions but also yield crisp answers to fuzzy problems in capacity planning.

Thesis (Ph. D.)--University of Notre Dame, 2007. / Thesis directed by Peter van Inwagen for the Department of Philosophy. "April 2007." Includes bibliographical references (leaves 222-226).

Inaug.-diss.--Bonn. / Lebenslauf. "Literatur-verzeichnis": p. 187-190.

The problem of evil arises from the argument that the existence of suffering is incompatible with (or else renders improbable) the existence of an omnipotent, omniscient and omnibenevolent God (and that, since the former surely exists, the latter must not). Philosophers working on the problem, however, rarely make profitable use of the distinction between mental and physical suffering. Accordingly, in this thesis I develop a version of the problem that focuses specifically on the phenomenal experience of physical pain. After providing (in the first chapter) a detailed analysis of (i) both logical and evidential (or probabilistic) formulations of the problem, and (ii) the usefulness of this logical/evidential distinction, I discuss some of the most promising theistic responses to the problem, and conclude that these theistic responses fail. In the second chapter I lay out my argument, and I attempt to show that there is no plausible way for the theist to respond when the problem is formulated in this manner. I conclude the chapter by arguing that my argument demonstrates the incompatibility of theism with both epiphenomenalism and zombies-informed dualism. In the third chapter I begin with a discussion of mental supervenience in order to defend a commonsense modal intuition necessary for the success of my argument. I then proceed to address possible objections, including most notably the effort to cast doubt on the reliability of the inference from conceivability to possibility. Finally, I consider empirical findings that substantiate my argument's most contentious premise. / Master of Arts

A great deal of attention has been paid to Kant's claim in the Critique of Pure Reason that all theoretical attempts to demonstrate the existence of God must necessarily end in failure. What has received considerably less attention is the fact that throughout his pre-Critical period, i.e., the period prior to the 1781 publication of the Critique of Pure Reason, Kant develops a unique argument in support of the possibility of such a demonstration. It is obvious that the Critical Kant can no longer maintain the validity of the argument which he presents in The Only Possible Argument in Support of a Demonstration of the Existence of God (1763); however, it is not obvious exactly how Kant proves that this argument cannot succeed. This thesis is concerned with providing an explanation of the change in Kant's view concerning the possibility of providing a theoretical demonstration of the existence of God. / Master of Arts

This dissertation is an attempt to understand the role of instruments in the process of knowledge production in science. I ask: how can we trust scientific instruments and what do we learn about when we use them? The dissertation has four parts. First, I construct a novel account of “epistemic possibility,” the possibility of knowing, that captures the dependency of knowledge on action, and I introduce the notion of “technological possibility,” which depends on the availability of material and conceptual means to bring about a desired state of affairs. I argue that, under certain circumstances, technological possibility is a condition for epistemic possibility. Second, I ask how instruments become reliable. I argue that when the material capacities and conceptual functions of a scientific instrument correspond, the instrument is a reliable component of the process of knowledge production. I then describe how the instrument design process can result in just such a correspondence. Instrument design produces the material device, a functional concept of the device revised in light of experience, a measure of the closeness of fit between material and function, and practices of trust such as calibration routines. ii Third, I ask what we learn from instruments such as those used for experimentation and simulation. I argue that in experiments, instruments function to inform us about the material capacities of the object of investigation, while in simulations, instruments function to inform us about the conceptual model of the object of investigation. Fourth, I put these philosophical distinctions into historical context through a case study of Monte Carlo simulations run on digital electronic computers in the 1940s-70s. I argue that digital electronic computers made the practice of Monte Carlo simulation technologically possible, but that the new method did not meet existing scientific standards. Consequently, Monte Carlo design practices were revised to address the worries of potential practitioners.

This dissertation is an attempt to understand the role of instruments in the process of knowledge production in science. I ask: how can we trust scientific instruments and what do we learn about when we use them? The dissertation has four parts. First, I construct a novel account of “epistemic possibility,” the possibility of knowing, that captures the dependency of knowledge on action, and I introduce the notion of “technological possibility,” which depends on the availability of material and conceptual means to bring about a desired state of affairs. I argue that, under certain circumstances, technological possibility is a condition for epistemic possibility. Second, I ask how instruments become reliable. I argue that when the material capacities and conceptual functions of a scientific instrument correspond, the instrument is a reliable component of the process of knowledge production. I then describe how the instrument design process can result in just such a correspondence. Instrument design produces the material device, a functional concept of the device revised in light of experience, a measure of the closeness of fit between material and function, and practices of trust such as calibration routines. ii Third, I ask what we learn from instruments such as those used for experimentation and simulation. I argue that in experiments, instruments function to inform us about the material capacities of the object of investigation, while in simulations, instruments function to inform us about the conceptual model of the object of investigation. Fourth, I put these philosophical distinctions into historical context through a case study of Monte Carlo simulations run on digital electronic computers in the 1940s-70s. I argue that digital electronic computers made the practice of Monte Carlo simulation technologically possible, but that the new method did not meet existing scientific standards. Consequently, Monte Carlo design practices were revised to address the worries of potential practitioners.

This project sets as its goal the development of an Intelligent Design paradigm that makes falsifiable predictions. According to Karl Popper, such falsifiability is a key component of scientific theories. To accomplish this, two hypothetical historical narratives are first outlined based on guided processes and the design points they predict. A biochemical approach to characterizing organisms then defines a protein's global functional limits as determining the set of amino acids that allow it to successfully perform its functions in any situation. The local functional limits restrict this potential substitution set to only those proteins viable within an individual genetic background. Proteins are referred to as the first-order of specified complexity because a protein's gene is the fundamental unit of inheritance. Other orders of specified complexity are described culminating in the organism level, which is the fundamental unit of selection. Each phylogenetic tree within the two intelligent design scenarios is founded by an original group or archetype. The descendants of this archetype are known as the archetype's genus. Speciation events within the genus are brought about by a slow process called co-adapted drift that creates distinct species through functional incompatibilities. A theory of natural selection is developed that attempts to characterize the relationship between the gene and the organism. Natural selection in this sense is described as a preservation mechanism that selects against deleterious phenotypes instead of selecting for beneficial ones. Finally, a practical methodology is developed that begins by determining the history of a gene in a given species by the symmetrical causal relationships of the alleles and the species allelic distribution. The original alleles in this species and their local functional limits are then compared with those of analogous genes in similar species to determine if these species were functionally compatible at that time. The two Intelligent Design paradigms predict patterns of incompatibilities, or design points, where guided actions were involved. This is a falsifiable prediction that raises the status of these paradigms in a Popperian sense.

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