Inclusion of flexibility benefits in discounted cash flow analyses for investment evaluation: A simulation/optimization model

It has been argued that conventional discounted cash flow (DCF) techniques, which are commonly used for investment justification, are inadequate and may even be inappropriate for the justification of advanced manufacturing systems whose strategic value comes from such attributes as flexibility. The problem lies in the proper estimation of the value of flexibility in financial or cash flow terms, so that the DCF techniques, which are otherwise conceptually sound, become relevant. This involves an assessment of the value of the flexibility of the manufacturing system in dealing with the uncertainties in its operating environment. We propose a simulation-optimization methodology for this assessment in cash flow terms and use it in a DCF framework. We use simulation to generate the environmental parameters in each period of an appropriate evaluation horizon. We develop a mathematical programming model to determine the distribution of the possible net revenues of the system in each period by capturing the combined effect of the different types of flexibilities that the manufacturing system may possess. We illustrate the application of our methodology using numerical examples and discuss how it can be used to assess the value of flexibility in cash flow terms. We show that our approach facilitates the justification of capital investment in advanced manufacturing systems which tend to get undervalued under the traditional DCF approaches. It would also help managers address such important questions as “how much incremental investment should we be willing to make now for the additional flexibility features?” and “does the expected present value of the future benefits of added flexibility justify the incremental capital investment now?” In essence, our paper addresses the question as to appropriate techniques or approaches for justifying proposed strategic investment decisions.     

Something out of nothing: a Bayesian learning computational model for the social construction of value

ABSTRACT

This article develops a formalism for the social construction of value. Using a model based on Bayesian agents, it demonstrates how “something” arises out of “nothing” via the emergence of durable value conventions and shows how the developed framework can be used to investigate socially constructed valuations under a variety of circumstances. The resulting analysis clarifies why assumptions that collectives will converge upon the “intrinsic” (i.e., non-socially originating) value of an object (e.g., market efficiency) may not hold for mixed social and non-social valuation regimes, explains the dependency of socially constructed valuations on early accidents, demonstrates the effects of confident actors on constructed values, and identifies the production of time-dependent ratcheting effects from the interaction of bubbles with value conventions.     

A generalized time quantifier approach to approximate reasoning

In this paper, a rather expressive fuzzy temporal logic for linear time is introduced. First, this logic is a multivalued generalization (Lukasiewicz style) of a two-valued linear-time temporal logic based on, e.g., the “until” operator. Second, it is obtained by introducing a generalized time quantifier (a generalization of the partition operator investigated by Shen) applied to fuzzy time sets.In this fuzzy temporal logic, generalized compositional rules of inference, suitable for approximate reasoning in a temporal setting, are presented as valid formulas.Some medical examples illustrate our approach.     

Machine-part cell formation in group technology using a modified ART1 method

Group Technology (GT) is a useful way of increasing the productivity for manufacturing high quality products and improving the flexibility of manufacturing systems. Cell formation (CF) is a key step in GT. It is used in designing good cellular manufacturing systems using the similarities between parts in relation to the machines in their manufacture. It can identify part families and machine groups. Recently, neural networks (NNs) have been widely applied in GT due to their robust and adaptive nature. NNs are very suitable in CF with a wide variety of real applications. Although Dagli and Huggahalli adopted the ART1 network with an application in machine-part CF, there are still several drawbacks to this approach. To address these concerns, we propose a modified ART1 neural learning algorithm. In our modified ART1, the vigilance parameter can be simply estimated by the data so that it is more efficient and reliable than Dagli and Huggahalli’s method for selecting a vigilance value. We then apply the proposed algorithm to machine-part CF in GT. Several examples are presented to illustrate its efficiency. In comparison with Dagli and Huggahalli’s method based on the performance measure proposed by Chandrasekaran and Rajagopalan, our modified ART1 neural learning algorithm provides better results. Overall, the proposed algorithm is vigilance parameter-free and very efficient to use in CF with a wide variety of machine/part matrices.     

Subdivision Strategies for Boxes in Branch-and-Bound Nonlinear Solvers and Verification

Most verified solvers for nonlinear interval systems of equations comprise two strategies: a branch-and-bound-type “location” phase for excluding regions that cannot contain a solution, and a “verification” phase for proving that the remaining regions do indeed contain solutions. In the first phase, subdivision is crucial for the efficiency of the solvers. We discuss several ways for subdivision and present robust strategies that are suited for a variety of nonlinear systems. Particular focus is on the choice of subdivision directions, subdivision points and the handling of unbounded intervals. Furthermore we discuss a method to discard parts of a box within subdivision. Numerical evaluations are given based on the nonlinear interval solver SONIC. In the verification phase, further subdivision can increase the strength of the verification tests. In this paper, we address methods for the rigorous implementation of symmetrical subdivision which is needed, e.g., in existence tests based on Borsuk’s theorem.     

The algebra of fuzzy logic

In the following, human thinking based on premises with no complete truth value is reviewed for controlling the algebra of fuzzy sets operations. Assuming a system may be developed in this sphere, it should be considered as the algebra of fuzzy sets, as the same algebra is satisfied by classical logic and sets. As will be proved, this algebra is not a lattice and consequently the Zadeh definitions do not constitute an adequate representation. The binary operations of my algebra are “interactive” types. An axiom system is given that, in my opinion, is the foundation of the conception, adequately and without redundancy. The agreement of the theorems deduced from the axiom system with the intuitive expectations is shown. A special arithmetical structure satisfying this algebra is given, and the relation between this structure and the theory of probability is analyzed.Adapting a process of classical logics, fuzzy quantifiers are defined on the basis of the operations of propositional algebra. A “qualifier” is also defined. The qualifier is functional; applying it to Ax we get the statement “usually Ax” s a middle cource between the statements “at least once Ax” and “always Ax”. The concept of entailment of fuzzy logics is introduced. This concept is an innovative generalization of the classical deduction theory, opposite to the concept of entailment of classical multi-valued logics. An important error of the abbreviated system of notation of the fuzzy theory [e.g. m(x, AvB)] appears: the functional type operations (e.g. quantifiers) cannot be interpreted in propositional calculus. Therefore a new system of symbols is proposed in this paper.     

Fuzzy variant of a statistical test point Kalman filter

In this paper, we propose the conceptual use of fuzzy clustering techniques as iterative spatial methods to estimate a posteriori statistics in place of the weighted averaging scheme of the Unscented Kalman filter. Specifically, instead of a linearization methodology involving the statistical linear regression of the process and measurement functions through some deterministically chosen set of test points (sigma points) contained within the “uncertainty region” around the state estimate, we present a variant of the Unscented transformation involving fuzzy clustering techniques which will be applied to the test points yielding “degrees of membership” in which Gaussian shapes can be “fit” using a least squares scheme. Implementation into the Kalman methodology will be shown along with simple state and parameter estimation examples.     

Quantile regression metamodeling: Toward improved responsiveness in the high-tech electronics manufacturing industry

Both technology and market demands within the high-tech electronics manufacturing industry change rapidly. Accurate and efficient estimation of cycle-time (CT) distribution remains a critical driver of on-time delivery and associated customer satisfaction metrics in these complex manufacturing systems. Simulation models are often used to emulate these systems in order to estimate parameters of the CT distribution. However, execution time of such simulation models can be excessively long limiting the number of simulation runs that can be executed for quantifying the impact of potential future operational changes. One solution is the use of simulation metamodeling which is to build a closed-form mathematical expression to approximate the input–output relationship implied by the simulation model based on simulation experiments run at selected design points in advance. Metamodels can be easily evaluated in a spreadsheet environment “on demand” to answer what-if questions without needing to run lengthy simulations. The majority of previous simulation metamodeling approaches have focused on estimating mean CT as a function of a single input variable (i.e., throughput). In this paper, we demonstrate the feasibility of a quantile regression based metamodeling approach. This method allows estimation of CT quantiles as a function of multiple input variables (e.g., throughput, product mix, and various distributional parameters of time-between-failures, repair time, setup time, loading and unloading times). Empirical results are provided to demonstrate the efficacy of the approach in a realistic simulation model representative of a semiconductor manufacturing system.     

Using self‐dissimilarity to quantify complexity

For many systems characterized as “complex” the patterns exhibited on different scales differ markedly from one another. For example, the biomass distribution in a human body “looks very different” depending on the scale at which one examines it. Conversely, the patterns at different scales in “simple” systems (e.g., gases, mountains, crystals) vary little from one scale to another. Accordingly, the degrees of self‐dissimilarity between the patterns of a system at various scales constitute a complexity “signature” of that system. Here we present a novel quantification of self‐dissimilarity. This signature can, if desired, incorporate a novel information‐theoretic measure of the distance between probability distributions that we derive here. Whatever distance measure is chosen, our quantification of self‐dissimilarity can be measured for many kinds of real‐world data. This allows comparisons of the complexity signatures of wholly different kinds of systems (e.g., systems involving information density in a digital computer vs. species densities in a rain forest vs. capital density in an economy, etc.). Moreover, in contrast to many other suggested complexity measures, evaluating the self‐dissimilarity of a system does not require one to already have a model of the system. These facts may allow self‐dissimilarity signatures to be used as the underlying observational variables of an eventual overarching theory relating all complex systems. To illustrate self‐dissimilarity, we present several numerical experiments. In particular, we show that the underlying structure of the logistic map is picked out by the self‐dissimilarity signature of time series produced by that map. © 2007 Wiley Periodicals, Inc. Complexity 12: 77–85, 2007     

Nonlinear processes in Hamiltonian reconnection

The generation of small spatial scales and their interplay with large scale coherent structures is one of the outstanding phenomena of plasma physics and fluid mechanics. In high temperature space and laboratory plasmas dissipative effects become important at length scales that are much smaller than those where microscopic dynamical effects, related e.g., to electron inertia, come into play. Here we discuss the role of this dissipationless small scale dynamics on the nonlinear evolution of collisionless magnetic reconnection within the framework of the so called “two-field” and “four-field models”.     

A new methodology for fuzzy multi-attribute group decision making with multi-granularity and non-homogeneous information

The aim of this paper is to develop a new methodology for solving fuzzy multi-attribute group decision making problems with non-homogeneous information, including multi-granular linguistic term sets, fuzzy numbers, interval values and real numbers. In this methodology, different distances are defined to measure differences between alternatives and the ideal solution as well as the negative ideal solution. A relative closeness method is developed by introducing the multi-attribute ranking index based on the particular measure of closeness to the IS. The proposed method determines a compromise solution for the group, providing a maximum “group utility” for the “majority” and a minimum of an individual regret for the “opponent”. The implementation process, effectiveness and feasibility of the method proposed in this paper are illustrated with a real example of the missile weapon system design project selection.     

Modelling the clogging of gas turbine filter houses in heavy-duty power generation systems

ABSTRACT

A prognostic approach based on a MISO (multiple inputs and single output) fuzzy logic model was introduced to estimate the pressure difference across a gas turbine (GT) filter house in a heavy-duty power generation system. For modelling and simulation of clogging of the GT filter house, nine real-time process variables (ambient temperature, humidity, ambient pressure, GT produced load, inlet guide vane position, airflow rate, wind speed, wind direction and PM10 dust concentration) were fuzzified using a graphical user interface within the framework of an artificial intelligence-based methodology. The results revealed that the proposed fuzzy logic model produced very small deviations and showed a superior predictive performance than the conventional multiple regression methodology, with a very high determination coefficient of 0.974. A complicated dynamic process, such as clogging phenomenonin heavy-duty GT system, was successfully modelled due to high capability of the fuzzy logic-based prognostic approach in capturing the nonlinear interactions.     

Reasoning under uncertainty with FIR methodology

The aim of this research is to develop a new methodology called UNFIR (uncertainty in FIR) as an extension of the fuzzy inductive reasoning (FIR) technique. The main idea behind UNFIR is to expand the modeling capacity of the FIR methodology allowing it to work with classical fuzzy rules. On the one hand, UNFIR is able to automatically construct fuzzy rules starting from a set of pattern rules obtained by FIR. On the other hand, UNFIR affords the prediction of systems behavior by using a mixed pattern/fuzzy inference system that takes advantage of the uncertainty inherent to the data. The pattern rule base that the FIR methodology generates can be very large, obstructing the prediction process and reducing its efficiency. The new methodology preserves as much as possible the knowledge of the pattern rules in a compact fuzzy rule base. In this process some precision is lost but the robustness is considerably increased.The performance of UNFIR methodology as a systems’ prediction tool is also studied in this work. Three different applications are used for this purpose, i.e., a linear system, a non-linear system and an industrial process.     

Complete statistical ranking of populations,with tables and applications

Recently a new statistical methodology, developed over the last three decades, has become available to practitioners. This methodology is called “ranking and selection” theory. In this article we review procedures for completely ranking a set of populations (from “best”, “second best”, etc., down to “worst”); we also give new tables needed to implement these procedures, and we consider several practical examples using real data.     

Generating a Random Collection of Discrete Joint Probability Distributions Subject to Partial Information

In this paper, we develop a practical and flexible methodology for generating a random collection of discrete joint probability distributions, subject to a specified information set, which can be expressed as a set of linear constraints (e.g., marginal assessments, moments, or pairwise correlations). Our approach begins with the construction of a polytope using this set of linear constraints. This polytope defines the set of all joint distributions that match the given information; we refer to this set as the “truth set.” We then implement a Monte Carlo procedure, the Hit-and-Run algorithm, to sample points uniformly from the truth set. Each sampled point is a joint distribution that matches the specified information. We provide guidelines to determine the quality of this sampled collection. The sampled points can be used to solve optimization models and to simulate systems under different uncertainty scenarios.     

Ten questions and one problem on fuzzy logic

Recently, I had a very interesting friendly e-mail discussion with Professor Parikh on vagueness and fuzzy logic. Parikh published several papers concerning the notion of vagueness. They contain critical remarks on fuzzy logic and its ability to formalize reasoning under vagueness [10,11]. On the other hand, for some years I have tried to advocate fuzzy logic (in the narrow sense, as Zadeh says, i.e. as formal logical systems formalizing reasoning under vagueness) and in particular, to show that such systems (of many-valued logic of a certain kind) offer a fully fledged and extremely interesting logic [4, 5]. But this leaves open the question of intuitive adequacy of many-valued logic as a logic of vagueness. Below I shall try to isolate eight questions Parikh asks, add two more and to comment on all of them. Finally, I formulate a problem on truth (in)definability in Łukasiewicz logic which shows, in my opinion, that fuzzy logic is not just “applied logic” but rather belongs to systems commonly called “philosophical logic” like modal logics, etc.     

Convergence of powers of a fuzzy matrix

A Boolean matrix is a matrix with elements having values of either 1 or 0; a fuzzy matrix is a matrix with elements having values in the closed interval [0, 1]. Fuzzy matrices occur in the modeling of various fuzzy systems, with products usually determined by the “max(min)” rule arising from fuzzy set theory. In this paper, some sufficient conditions for convergence under “max(min)” products of the powers of a square fuzzy matrix and of a fuzzy state process are established.     

Complex systems in drug research: I. The chemical levels

The hierarchy of chemical systems is examined. It is argued that an intermediate level of complexity, that of functional groups, exists between atoms and molecules. Molecular properties have an emergent nature, e.g., the “chameleonic behavior.” Aggregates of molecules and solutions also behave as complex systems. Emergent properties are particularly noteworthy in biological macromolecules and correspond to basins of attraction resulting from a complex interplay between intramolecular and intermolecular interactions.     

The impact of finite precision arithmetic and sensitivity on the numerical solution of partial differential equations

In this paper, we address some fundamental issues concerning “time marching” numerical schemes for computing steady state solutions of boundary value problems for nonlinear partial differential equations. Simple examples are used to illustrate that even theoretically convergent schemes can produce numerical steady state solutions that do not correspond to steady state solutions of the boundary value problem. This phenomenon must be considered in any computational study of nonunique solutions to partial differential equations that govern physical systems such as fluid flows. In particular, numerical calculations have been used to “suggest” that certain Euler equations do not have a unique solution. For Burgers' equation on a finite spatial interval with Neumann boundary conditions the only steady state solutions are constant (in space) functions. Moreover, according to recent theoretical results, for any initial condition the corresponding solution to Burgers' equation must converge to a constant as t → ∞. However, we present a convergent finite difference scheme that produces false nonconstant numerical steady state “solutions.” These erroneous solutions arise out of the necessary finite floating point arithmetic inherent in every digital computer. We suggest the resulting numerical steady state solution may be viewed as a solution to a “nearby” boundary value problem with high sensitivity to changes in the boundary conditions. Finally, we close with some comments on the relevance of this paper to some recent “numerical based proofs” of the existence of nonunique solutions to Euler equations and to aerodynamic design.     

再制造供应链“投保策略”运营模式研究:谁应来投“产品质量保险”？

以汽车零部件制造业为研究背景,运用斯坦伯格博弈,探析了在制造和再制造双供应链寡头竞争中,采用为再制造品购买“产品质量保险”这一促销策略时,再制造供应链参与者“谁应来投保”的问题。依据集体生产者责任(CPR)理论,“投保策略”的实施模式可分为两种:(1)再制造商主导投保;(2)再制造品零售商主导投保。根据消费者行为研究,构造了反映投保策略的需求函数,并针对投保者不同,探究了两种投保模式的可取条件及其最优利润。研究表明:从整个再制造供应链效益来看,再制造商主导投保模式恒优于零售商主导投保模式;但若两种模式同时可取,再制造商及其零售商均会消极投保,以期谋求“搭便车”收益,此时再制造商可在一定范围内提高收益出让比率以促进零售商投保,否则再制造商应积极主导投保。