Models for planning capacity expansion in local access telecommunication networks

The rapid progress of communications technology has created new opportunities for modeling and optimizing the design of local telecommunication systems. The complexity, diversity, and continuous evolution of these networks pose several modeling challenges. In this paper, we present an overview of the local telephone network environment, and discuss possible modeling approaches. In particular, we (i) discuss the engineering characteristics of the network, and introduce terminology that is commonly used in the communications industry and literature; (ii) describe a general local access network planning model and framework, and motivate different possible modeling assumptions; (iii) summarize various existing planning models in the context of this framework; and (iv) describe some new modeling approaches. The discussion in this paper is directed both to researchers interested in modeling local telecommunications systems and to planners interested in using such models. Our goal is to present relevant aspects of the engineering environment for local access telecommunication networks, and to discuss the relationship between engineering issues and the formulation of economic decision models. We indicate how changes in the underlying switching and transmission technology affect the modeling of the local telephone network. We also review various planning issues and discuss possible optimization approaches for treating them.This research was initiated through a grant from GTE Laboratories, IncorporatedSupported in part by an AT&T research award.Supported in part by Grant No. ECS-8316224 from the Systems Theory and Operations Research Program of the National Science Foundation.     

On the organization of large shared Model bases

Central to the Model Management (MM) function is the creation and maintenance of a knowledge-based model repository. The Model Knowledge Base (MKB) provides the basis by which information about models can be shared to facilitate consistent and controlled utilization of existing models for decision making, as well as the development of new models. Various schemes for representing individual models have been proposed in the literature. This paper focuses on how best to structure, control, and administer a large MKB to support organization-wide modeling activities. Guided by a recently proposed systems framework for MM, we describe a number of concepts which are useful for capturing the semantics and structural relationships of models in an MKB. These concepts, and the nature of the MMS functions to be supported, are then used to derive specific information management requirements for model bases. Four major requirements are identified: (1) management of composite model configurations; (2) management of model version histories; (3) support for the model consultation and selection functions of an MMS; and (4) support for multiple logical MKBs (private, group, and public). We argue that traditional record-based approaches to data management appear to fall short of capturing the rich semantics present in an MM environment. The paper proposes an architecture for an MMS, focusing on its major component — the MKB Management Subsystem. An implementation of this architecture is briefly described.     

A fuzzy clustering based technique for piecewise affine approximation of a class of nonlinear systems

In recent years piecewise affine (PWA) modeling has developed as an attractive tool for the approximation of various complex nonlinear systems. In spite of the wide application of PWA modeling, the optimal approximation of a continuous time nonlinear system with scalar functions by the minimum number of affine systems has not been addressed properly in literature. This paper deals with a fuzzy clustering based approach for the optimal PWA approximation of a class of continuous time nonlinear systems. The technique is based on the trade-off between increasing the approximation accuracy of the various nonlinear functions and simplifying the approximation by the minimum number of subsystems. As an application, the technique is utilized to obtain a PWA approximation of the glucose regulation system. Numerical simulations depicted that, for a given number of subsystems, the derived glucose regulation model provides an optimal approximation of the original nonlinear system. The model also provided some biological insight about the interactions involved in glucose regulation.     

Hybrid simulation of space plasmas: Models with massless fluid representation of electrons. II. Slow and intermediate shocks

This is a second article in a series of reviews on hybrid simulation of low-frequency processes in space plasmas. A hybrid model is described with ions represented by particles and electrons by a massless fluid. The main numerical schemes for the implementation of this model are described: the generalized Ohm law scheme and the predictor-corrector scheme. The first part of the article provides basic back-ground information: MHD models (ideal, resistive, and Hall model); the Rankine-Hugoniot relationship for MHD discontinuities; the Hoffman-Teller coordinate system; and a classification of discontinuities. The review part of the article surveys the literature on simulation of slow shocks (including switch-off shocks) and intermediate shocks. The survey of literature on hybrid simulation of intermediate shocks is concluded with a review of studies that use two different numerical codes (the hybrid model and the resistive Hall MHD model). The computation results produced by the two codes are compared. The concluding part presents some remarks concerning the existence of intermediate shocks and their relationship with rotational discontinuities in various numerical models (ideal MHD, resistive MHD, the hybrid model). Translated from Prikladnaya Matematika i Informatika, No. 2, pp. 5–33, 1999.     

Coordinatewise estimates for vector outputs of multivariable phase control systems

Two classes of multidimensional phase control systems with differentiable vector periodic functions are considered, the class of continuous systems described by ordinary differential equations and the class of discrete systems described by difference equations. The number of cycle slips for angular coordinates in phase systems with differentiable nonlinearities is studied. The study is based on the direct Lyapunov method and uses periodic Lyapunov functions, extensions of the phase space of the system, and the Yakubovich-Kalman lemma. This lemma provides necessary and sufficient conditions for the existence of Lyapunov functions by using the transfer matrix of the linear part of the system. As a result, for phase systems possessing global asymptotics, frequency criteria making it possible to sharpen estimates for the deviation of angular coordinates from their initial values are obtained. These criteria contain multiparameter frequency inequalities with variable parameters satisfying certain algebraic inequalities.     

On a family of models of cell division cycle

The aim of this work is to generalize and study a model of cell division cycle proposed recently by Zheng et al. [Zheng Z, Zhou T, Zhang S. Dynamical behavior in the modeling of cell division cycle. Chaos, Solitons & Fractals 2000;11:2371–8]. Here we study the qualitative properties of a general family to which the above model belongs. The global asymptotic stability (GAS) of the unique equilibrium point E (idest of the arrest of cell cycling) is investigated and some conditions are given. Hopf’s bifurcation is showed to happen. In the second part of the work, the theorems given in the first part are used to analyze the GAS of E and improved conditions are given. Theorem on uniqueness of limit cycle in Lienard’s systems are used to show that, for some combination of parameters, the model has GAS limit cycles.     

Holding costs under push or pull conditions - The impact of the Anchor Point

Holding costs are traditionally determined from the investment in physical stock during a cycle. An alternative approach instead derives holding costs from Net Present Value (NPV) functions. It is known that applying both frameworks to the same system can lead to different holding cost valuations, but little explanation has been offered. By introducing the Anchor Point in a model, this paper shows, for four different systems, that traditional holding cost models (implicitly) assume pull conditions, while current NPV approaches model push conditions. This explains in part the differences between the methods. It is shown that the Anchor Point concept allows the construction of NPV models under pull conditions, giving results in better correspondence with traditional models. The traditional framework is restricted to pull conditions and important considerations could be easily overlooked, leading to wrong valuations of holding costs. NPV seems superior as such considerations are automatically incorporated. The application to multi-echelon inventory systems provides interesting insights on the roles of echelon stocks and lead-times, and offers potential for future research.     

A review of methods for computer-aided reconstruction of solid models of objects by their orthographic views

The paper contains a review of methods for computer-aided reconstruction of three-dimensional solid models of objects by their orthographic views. This problem arises when passing from engineering 2D-drawings to spatial models of objects in systems of three-dimensional solid modeling. In spite that there exists a large variety of such systems, none of them provides a computer-aided solution of this problem. This is due to the fact that the existing methods for solution are imperfect and require further elaboration. In this paper, we review the existing approaches and compare their application domains.     

VLES turbulence model for an Eulerian–Lagrangian modeling concept for bubble plumes

Traditional Reynolds-averaged Navier–Stokes (RANS) approaches to turbulence modeling, such as the k-ϵ model, have some well-known shortcomings when modeling transient flow phenomena. To mitigate this, a filtered URANS model has been derived where turbulent structures larger than a given filter size (typically grid size) is captured by the flow equations and smaller structures are modeled according to a modified k-ϵ model. This modeling approach is also known as a VLES model (Very Large Eddy Scale model), and provides more details of the transient turbulence than the k-ϵ model at little extra computational cost.In this study a two-phase extension to the VLES model is described. A modeling concept for bubble plumes has been developed in which the bubbles are tracked as particles and the flow of liquid is solved by the Navier–Stokes equations in a traditional mesh based approach. The flow of bubbles and liquid is coupled in an Eulerian–Lagrangian model. Turbulent dispersion of the bubbles is treated by a random walk model. The random walk model depends on an estimation of the eddy life time. The eddy life time for the VLES model differs from a k-ϵ model, and its mathematical expression is derived.The model is applied to ocean plumes emanating from discharge of gas at the ocean floor. Validation with experiments and comparison with k-ϵ model are shown.     

A formal apparatus for modeling trust in computing environments

Recent research in computer systems security has evolved into trust issues, which are now becoming an important topic. The majority of approaches for trust modeling addressed trust by actually focusing on security, and some of them addressed also trust as such. This paper presents a formal apparatus that concentrates on trust as such. It is flexible enough to accommodate the driving factors behind trust and consequently different trust-focused methodologies and technologies. The basic goal of the work presented in this paper is the definition of qualitative trust modeling methodology for trust management in contemporary computing environments that efficiently complements existing quantitative methodologies. Further, an open conceptual model for trust management is presented that accommodates various qualitative and quantitative trust management methodologies. This model has also been implemented in the web services environment, and this is discussed in this paper as well.     

Integrated system modeling and optimization via quasilinearization

System modeling and system optimization are two coupled and strongly related concepts in the modern approach to large-scale systems. Yet, they have been treated as two separate problems in the literature. The identification of system parameters, often referred to as system modeling, is essential in order to obtain an optimal control policy. This work considers the two problems jointly and provides a computational methodology in tackling the integrated problem formulation. This is done by viewing one of the objective functions in the bicriterion problem formulation as a constraint. A computational strategy such as quasilinearization is employed for the solution of the integrated problem. An example problem is introduced, and numerical results using an IBM 360/91 digital computer are presented.The authors are very grateful to Professor C. T. Leondes for his invaluable assistance, guidance, and comments. This research was supported in part by the Air Force Office of Scientific Research, Grant No. 699-67, and in part by the National Science Foundation, Grant No. GK-4086.     

Probabilistic decision graphs for optimization under uncertainty

This paper provides a survey on probabilistic decision graphs for modeling and solving decision problems under uncertainty. We give an introduction to influence diagrams, which is a popular framework for representing and solving sequential decision problems with a single decision maker. As the methods for solving influence diagrams can scale rather badly in the length of the decision sequence, we present a couple of approaches for calculating approximate solutions. The modeling scope of the influence diagram is limited to so-called symmetric decision problems. This limitation has motivated the development of alternative representation languages, which enlarge the class of decision problems that can be modeled efficiently. We present some of these alternative frameworks and demonstrate their expressibility using several examples. Finally, we provide a list of software systems that implement the frameworks described in the paper.     

Probabilistic decision graphs for optimization under uncertainty

This paper provides a survey on probabilistic decision graphs for modeling and solving decision problems under uncertainty. We give an introduction to influence diagrams, which is a popular framework for representing and solving sequential decision problems with a single decision maker. As the methods for solving influence diagrams can scale rather badly in the length of the decision sequence, we present a couple of approaches for calculating approximate solutions. The modeling scope of the influence diagram is limited to so-called symmetric decision problems. This limitation has motivated the development of alternative representation languages, which enlarge the class of decision problems that can be modeled efficiently. We present some of these alternative frameworks and demonstrate their expressibility using several examples. Finally, we provide a list of software systems that implement the frameworks described in the paper.     

Intelligent management systems in operations: a review

Operations management is an area that has recently started to benefit from the use of AI techniques such as expert systems, neural networks and genetic algorithms. These techniques can extend the usefulness of OR modelling and enable new types of decision tasks to be supported by computer-based systems. This paper attempts to review ‘intelligent’ decision support systems and their potential to address some of the problems faced in various areas of operations management. Some useful techniques developed in the field of artificial intelligence are outlined and examples of attempts to use these approaches to support decision making in various areas of operations management are described. Recognising the scale of a complete review of all these areas, emphasis has been given to the most significant and more recent publications.     

Developing a Visual Interactive Model for Corporate Cash Management

The operational research/management science journals contain an extensive literature that addresses the corporate cash management problem; yet few, if any, companies make use of any of this published work in their daily cash-management decision making. A review of the literature suggests that the reason for this lack of applications may well be poor problem formulation—the problems that are solved in the literature as ‘cash management’ problems evolve from a ‘hard systems’ view of real-world cash management. However, the problem as perceived by cash managers involves both dynamic and loosely structured components which are difficult to model using classical (i.e. ‘hard systems’) approaches.We therefore decided to approach the cash management problem as an experiment in the use of a novel visual interactive problem solving (VIPS) methodology. The aim of the experiment was to develop an implementable, visual interactive model to support daily cash management decision making. Working closely with a corporate cash manager, we first developed a visual model of his daily decision problem and then agreed on the feasible options and the interactive requirements. At this stage, the problem was sufficiently well defined for a mathematical model to be built and the visual model made ‘smart’.This paper discusses the results of this experiment and suggests that VIPS may have distinct advantages as a problem-solving technique in loosely structured, ‘messy’ problem situations.     

Behavioral modeling in weight loss interventions

Designing systems with human agents is difficult because it often requires models that characterize agents’ responses to changes in the system’s states and inputs. An example of this scenario occurs when designing treatments for obesity. While weight loss interventions through increasing physical activity and modifying diet have found success in reducing individuals’ weight, such programs are difficult to maintain over long periods of time due to lack of patient adherence. A promising approach to increase adherence is through the personalization of treatments to each patient. In this paper, we make a contribution toward treatment personalization by developing a framework for predictive modeling using utility functions that depend upon both time-varying system states and motivational states evolving according to some modeled process corresponding to qualitative social science models of behavior change. Computing the predictive model requires solving a bilevel program, which we reformulate as a mixed-integer linear program (MILP). This reformulation provides the first (to our knowledge) formulation for Bayesian inference that uses empirical histograms as prior distributions. We study the predictive ability of our framework using a data set from a weight loss intervention, and our predictive model is validated by comparison to standard machine learning approaches. We conclude by describing how our predictive model could be used for optimization, unlike standard machine learning approaches that cannot.     

Survey on fuzzy shop scheduling

The real life scheduling problems often have several uncertainties. The solutions of these problems can provide deeper insights to the decision maker than those of deterministic problems. Fuzzy set theory as most important tool to model uncertainty represents an attractive tool to aid research in the production management. Since to the best of our knowledge, there is not a comprehensive review on the fuzzy scheduling literature, the goal of this paper is to provide an extensive review for the fuzzy machine scheduling which it covers more than 140 papers. For this purpose, first, this paper classifies and reviews the literature according to shop environments, including single machine, parallel machines, flowshop, job shop and open shop. Then the reviewed literature is quantified and measured. At the end the paper concludes by presenting some problems receiving less attention than the others and proposing some research opportunities in the field.     

Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains

Operational forecasting in supply chain management supports a variety of short-term planning decisions, such as production scheduling and inventory management. In this respect, improving short-term forecast accuracy is a way to build a more agile supply chain for manufacturing companies. Demand forecasting often relies on well-established univariate forecasting methods to extrapolate historical demand. Collaboration across the supply chain, including information sharing, is suggested in the literature to improve upon the forecast accuracy of such traditional methods. In this paper, we review empirical studies considering the use of downstream information in demand forecasting and investigate different modeling approaches and forecasting methods to incorporate such data. Where empirical findings on information sharing mainly focus on point-of-sale data in two-level supply chains, this research empirically investigates the added value of using sell-through data originating from intermediaries, next to historical demand figures, in a multi-echelon supply chain. In a case study concerning a US drug manufacturer, we evaluate different methods to incorporate this data and consider both time series methods and machine learning techniques to produce multi-step ahead weekly forecasts. The results show that the manufacturer can effectively improve its short-term forecast accuracy by integrating sell-through data into the forecasting process and provide useful insights as to the different modeling approaches used. The conclusion holds for all forecast horizons considered, though it is most pronounced for one-step ahead forecasts. Therefore, our research provides a clear incentive for manufacturers to assess the forecast accuracy that can be achieved by using sell-through data.     

Optimal design and control of queues

We have divided this review into two parts. The first part is concerned with the optimal design of queueing systems and the second part deals with the optimal control of queueing systems. The second part, which has the lion’s share of the review since it has received the most attention, focuses mainly on the modelling aspects of the problem and describes the different kinds of threshold (control) policy models available in the literature. To limit the scope of this survey, we decided to limit ourselves to research on papers dealing with the three policies (N, T, and D), where a cost function is designed specifically and optimal thresholds that yield minimum cost are sought.