An approach to a consistent qualification of decision procedures in probabilistic information processing

Pattern recognition seems to be a rather unique field of interwoven logical inference and decision theory applications. The existence of hundreds of theoretical and real pattern recognition devices forms an ideal basis for research on the structures of various approaches and their comparison. The task of pattern recognition is to select a hypotheses out of a set (e.g.: figures 0, …, 9) on the basis of given data (e.g. the black and white points of a digitized picture). There exists an ideal classifier to solve this problem as the theorem of Bayes provides a logically perfect connection between the input data and the result. But as the so called Bayes-machine proves completely unpractical for real purposesit is “approximated” by more or less complex “real” decision procedures.Thus the theorem of Bayes provides a starting point for the application of statistical considerations and information theory to the analysis of the structures of real decision procedures. The results allow a rather consistent and simple comparison of most decision procedures and provide a tool to estimate the performance of a given procedure in a given environment. The results apply not only to pattern recognition but also to many other fields such as imminence analysis and medical diagnosis.     

Integrated decision support for aviation safety inspectors

This paper presents a systems viewpoint for developing an advanced decision support system for aircraft safety inspectors. Research results from a Federal Aviation Administration (FAA) sponsored project to use neural network and expert systems technology to analyze aircraft maintenance databases are summarized. One of the main objectives of this research is to define more refined “alert” indicators for national comparison purposes that can signal potential problem areas by aircraft type for safety inspector consideration.

Integration aspects are addressed on two levels: (1) integration of the various technical components of the decision support system, and (2) integration of the decision support system with individual behavior, management systems and organizational structure, as well as corporate culture across both formal and informal dimensions. The paper summarizes the creation of strategic “inspection profiles” for aging aircraft and reliability curve fitting for structural components both based upon using neural network technology. Also, the potential use of a model-based expert system to facilitate field inspection diagnostics is presented. Finally, a framework for developing an intelligent decision system to support aircraft safety inspections is proposed that links expert systems, neural networks, as well as a paradigm of the decision making process typically used in unstructured situations.     

Multiple criteria decision making combined with finance: A categorized bibliographic study

This paper provides a categorized bibliography on the application of the techniques of multiple criteria decision making (MCDM) to problems and issues in finance. A total of 265 references have been compiled and classified according to the methodological approaches of goal programming, multiple objective programming, the analytic hierarchy process, etc., and to the application areas of capital budgeting, working capital management, portfolio analysis, etc. The bibliography provides an overview of the literature on “MCDM combined with finance,” shows how contributions to the area have come from all over the world, facilitates access to the entirety of this heretofore fragmented literature, and underscores the often multiple criterion nature of many problems in finance.     

Fuzzy probability system: fuzzy-probability space (1)

The present paper discusses “The major objective of the theory of probability”, and hence provides a fuzzy probability system, which links both Von Mises's probability system and Kolmogorov's probability system to be a new one. Such a probability system has a more original theoretical starting point, and appears to deal with such uncertainty as has subjectivity and fuzziness. In addition, this probability system has some softness too. This paper attempts to induct the subject and the subjective factor into mathematics.     

Self-consistency of decision rules for group decision making

The author treats, in this paper, a group of decision makers, where each of them already has preference on a given set of alternatives but the group as a whole does not have a decision rule to make their group decision, yet. Then, the author examines which decision rules are appropriate. As a criterion of “appropriateness” the author proposes the concepts of self-consistency and universal self-consistency of decision rules. Examining the existence of universally self-consistent decision rules in two cases: (1) decision situations with three decision makers and two alternatives, and (2) those with three decision makers and three alternatives, the author has found that all decision rules are universally self-consistent in the case (1), whereas all universally self-consistent decision rules have one and just one vetoer in the essential cases in (2). The result in the case (2) implies incompatibility of universal self-consistency with symmetry. An example of applications of the concept of self-consistency to a bankruptcy problem is also provided in this paper, where compatibility of self-consistency with symmetry in a particular decision situation is shown.     

A simple decision model of counterproliferation

We develop a simple decision model of counterproliferation involving a status quo “incumbent” and a nuclear “entrant”. The problem is examined as a one-stage interaction in two phases: nuclear development and deployment. We examine the conditions that will influence the decision to move pre-emptively against a proliferator's nuclear program. Particular attention is given to the role of uncertainty in determining the expected costs of action at different points in the entrant's weapon's development and deployment cycle. The model permits us to determine the optimal time to act given varying levels of information concerning entrant behavior. In conclusion, we examine the tradeoffs between the expected costs of action and the costs of intelligence.     

On deformation of associative algebras and graph homology

Deformation theory of associative algebras and in particular of Poisson algebras is reviewed. The role of an “almost contraction” leading to a canonical solution of the corresponding Maurer–Cartan equation is noted. This role is reminiscent of the Homotopical Perturbation Lemma, with the infinitesimal deformation cocycle as “initiator.”Applied to star-products, we show how Moyal's formula can be obtained using such an almost contraction and conjecture that the “merger operation” provides a canonical solution at least in the case of linear Poisson structures.     

A study of high-level managerial decision processes, with implications for MCDM research

We study six real-world major strategic decisions and discuss the role that analytic Multiple Criteria Decision Making (MCDM) models could play in helping decision makers structure and solve such problems. We have interviewed successful and well-educated managers who had access to quantitative decision models, but did not use them as part of their decision process. Our approach is a clinical one that takes a close look at the decision processes. We believe that the normative MCDM framework is oversimplified and does not always fit well with complex, real-world organizational decision processes. This may be one reason why decision tools are not used more widely for solving high-level decision problems. We believe that it would be worthwhile to revise some of the MCDM mainstream postulates and practices to make existing models and tools more suitable for practical purposes. The MCDM mainstream research has until today focused on the choice among alternatives. One should realize that MCDM models could also be used in creating alternatives, in assessing the importance of criteria, in providing the decision makers with “post-commitment support”, and as part of a devil's advocate approach.     

Selecting preferred solutions in the minimax approach to dynamic programming problems under flexible constraints

Dynamic Programming is a powerful approach to the optimization of sequential or multistage decision processes, e.g., in planning or in system control. In this paper, we consider both theoretical and algorithmic issues in sequential decision processes under flexible constraints. Such processes must attain a given goal within some tolerance. Tolerances or preferences also apply to the values the decision variables may take or on the action chosen at each step. Such problems boil down to maximin optimization. Unfortunately, this approach suffers from the so-called “drowning effect” (lack of discrimination) and the optimality principle of dynamic programming is not always verified. In this context, we introduce a general framework for refined minimax optimization procedures in order to compare and select preferred alternatives. This framework encompasses already introduced methods such as LexiMin and DiscriMin, but it allows their extension to the comparison of vectors of unequal lengths. We show that these refined comparisons restore compatibility with the optimality principle, and that classical algorithms can be adapted to compute such preferred solutions, by exploiting existing results on idempotent semirings.     

Treasury Management Model with Foreign Exchange Exposure

In this paper we formulate a model for foreign exchange exposure management and (international) cash management taking into consideration random fluctuations of exchange rates. A vector error correction model (VECM) is used to predict the random behaviour of the forward as well as spot rates connecting dollar and sterling. A two-stage stochastic programming (TWOSP) decision model is formulated using these random parameter values. This model computes currency hedging strategies, which provide rolling decisions of how much forward contracts should be bought and how much should be liquidated.The model decisions are investigated through ex post simulation and backtesting in which value at risk (VaR) for alternative decisions are computed. The investigation (a) shows that there is a considerable improvement to “spot only” strategy, (b) provides insight into how these decisions are made and (c) also validates the performance of this model.     

Mathematical modeling of a steam generator for sensor fault detection

A dynamic model for a nuclear power plant steam generator (vertical, preheated, U-tube recirculation-type) is formulated as a sixth-order nonlinear system. The model integrates nodal mass and energy balances for the primary water, the U-tube metal and the secondary water and steam. The downcomer flow is determined by a static balance of momentum. The mathematical system is solved using transient input data from the Philippsburg 2 (FRG) nuclear power plant. The results of the calculation are compared with actual measured values. The proposed model provides a low-cost tool for the automatic control and simulation of the steam generating process. The “parity-space” algorithm is used to demonstrate the applicability of the mathematical model for sensor fault detection and identification purposes. This technique provides a powerful means of generating temporal analytic redundancy between sensor signals. It demonstrates good detection rates of sensor errors using relatively few steps of scanning time and allows the reconfiguration of faulty signals.     

Production/Clearing Models Under Continuous and Sporadic Reviews

We consider production/clearing models where random demand for a product is generated by customers (e.g., retailers) who arrive according to a compound Poisson process. The product is produced uniformly and continuously and added to the buffer to meet future demands. Allowing to operate the system without a clearing policy may result in high inventory holding costs. Thus, in order to minimize the average cost for the system we introduce two different clearing policies (continuous and sporadic review) and consider two different issuing policies (“all-or-some” and “all-or-none”) giving rise to four distinct production/clearing models. We use tools from level crossing theory and establish integral equations representing the stationary distribution of the buffer’s content level. We solve the integral equations to obtain the stationary distributions and develop the average cost objective functions involving holding, shortage and clearing costs for each model. We then compute the optimal value of the decision variables that minimize the objective functions. We present numerical examples for each of the four models and compare the behaviour of different solutions.AMS 2000 Subject Classification: 90B05 Inventory, storage, reservoirs; 90B22 Queues and service; 90B30 Production models     

Validating an Expert Support System for Tender Enquiry Evaluation: A Case Study

An expert system was developed as a support tool for a large electro-mechanical engineering company. The system was developed to assist in tender enquiry evaluation and the bid versus no bid decision. It was developed using repertory grid techniques based on the expertise of two senior managers. The importance of the system's validation is stressed and an appropriate formal validation framework developed. The results from the validation study suggest that the system provides an appropriate model of the organization's consensual business perspective regarding the bid versus no bid decision. Furthermore, the study drew attention to some dissenting views amongst the organization's senior management which the management believed to be helpful in clarifying their understanding of the issues affecting the bid versus no bid decision.     

Successive Proportional Additive Numeration Using Fuzzy Linguistic Labels (Fuzzy Linguistic SPAN)

The SPAN (Successive Proportional Additive Numeration or Social Participatory Allocation Network) is a procedure that converts individual judgments into a group decision. The procedure is based on a voting design by which individual experts allocate their votes iteratively between their preferred options and other experts. The process ends when all the votes are allocated to options, and the one with the highest number of votes is selected. The method requires the experts to specify an exact allocation of votes to both options and other experts. The Fuzzy Linguistic SPAN allows experts to allocate their votes using linguistic labels such as “most of” or “a few”, and determine the preferred option. This method is demonstrated using the Max–Min aggregation function used to develop a proportional representation of the option and member voting schemes. The method is also demonstrated using the LOWA aggregation function. The Fuzzy Linguistic SPAN method is beneficial since the linguistic voting process is easier for the experts and significantly reduces the computational process compared to the traditional SPAN. The paper presents the method and two examples with comparisons to the numerical SPAN method.     

Sensitivity analysis for product design selection with an implicit value function

In product design selection the decision maker (DM) often does not have enough information about the end users’ needs to state the “preferences” with precision, as is required by many of the existing selection methods. We present, for the case where the DM gives estimates of the preferences, a concept for calculating a “robustness index.” The concept can be used with any iterative selection method that chooses a trial design for each iteration, and uses the DM’s preference parameters at that trial design to eliminate some design options which have lower value than the trial design. Such methods, like our previously published method, are applicable to cases where the DM’s value function is implicit. Our robustness index is a metric of the allowed variation between the actual and estimated preferences for which the set of non-eliminated trial designs (which could be singleton) will not change. The DM, through experience, can use the robustness index and other information generated in calculating the index to determine what action to take: make a final selection from the present set of non-eliminated designs; improve the precision of the preference estimates; or otherwise cope with the imprecision. We present an algorithm for finding the robustness index, and demonstrate and verify the algorithm with an engineering example and a numerical example.     

Reading “A variant of the Hales–Jewett theorem” on its anniversary

In a recent paper “A variant of the Hales–Jewett theorem”, M. Beiglböck provides a version of the classic coloring result in which an instance of the variable in a word giving rise to a monochromatic combinatorial line can be moved around in a finite structure of specified type (for example, an arithmetic progression). We give an elementary proof and infinitary extensions.     

Simulation optimization for an emergency department healthcare unit in Kuwait

This paper integrates simulation with optimization to design a decision support tool for the operation of an emergency department unit at a governmental hospital in Kuwait. The hospital provides a set of services for different categories of patients. We present a methodology that uses system simulation combined with optimization to determine the optimal number of doctors, lab technicians and nurses required to maximize patient throughput and to reduce patient time in the system subject to budget restrictions. The major objective of this decision supporting tool is to evaluate the impact of various staffing levels on service efficiency. Experimental results show that by using current hospital resources, the optimization simulation model generates optimal staffing allocation that would allow 28% increase in patient throughput and an average of 40% reduction in patients’ waiting time.     

The equivalence of team theory's integral equations and a Cauchy system: sensitivity analysis of a variational problem

Team decision theory studies the problem of how a group of decision makers should use information to coordinate their actions. Mathematically, the task is to find functions that maximize an objective functional. The Euler equations take the form of a system of integral equations. In this paper, it will be shown that a class of such integral equations has solutions that are identical to the solutions of a system of initial-valued integrodifferential equations. This Cauchy system describes the sensitivity of the solutions to underlying parameters and provides an efficient technique for solving difficult team decision problems. An analysis of a profit maximizing firm demonstrates the usefulness of the Cauchy system.     

Effective Discretization of the Energy Integral and Grunsky Coefficients in Annuli

Let be an analytic Jordan curve in the complex plane. We formulate a discrete minimal energy problem in a suitable class of functions whose solution provides a geometrically fast converging approximation to the equilibrium measure of . For this purpose an extremal point system that was introduced by K. Menke in 1972 is applied. In particular, an explicit error bound for the discretization of the energy integral is computed. The key to this error estimate is a univalence criterion for Laurent series, proved by R. Kuhnau in 1972. Finally, an estimate for the discrepancy between the approximating measures and the equilibrium measure is derived from the discretization error of the energy integral.     

Scheduling of a machining center

A machining center is an advanced NC (Numerical Control) machine that has the capability to perform a variety of operations on a part by automatically changing the cutting tools. Because of its versatile processing capabilities, a machining center is often a production bottleneck, and effective scheduling can result in significant improvement of system performance. The problem, however, is very difficult since many factors such as machine setups, pallets, tool magazine, and possible tool overlapping among different part types, etc., have to be considered. This paper presents an optimization-based approach for the scheduling of a machining center with two pallets. A novel “separable” problem formulation that considers the above mentioned factors is presented. Lagrangian relaxation is applied to decompose the problem into simple subproblems, which are efficiently solved without encountering complexity difficulties. The subgradient method is then used to update the multipliers. Testing results indicate that the approach is effective, and the algorithm provides a valuable tool for solving stand-alone machining center problems. The approach also points out a direction on how to consider machining centers within a job shop environment.