Formal Modeling Approach Research Articles

A Generic Approach to Connector Architectures Part I: The General Framework

The aim of this paper is to present a generic framework for the modelling of componentbased systems using architectural connectors. More precisely, concepts of component, connector and architecture are presented in a formal generic way, which are independent of any semi-formal or formal modelling approach. The idea is that one could use this framework to define component and connector notions for every given modelling formalism. As a main result, we define the semantics of architectures using graph transformation, showing that the semantics is independent of the order in which the connections are computed, and that the semantics is compatible with transformation. In the continuation of this paper, we show the applicability of our ideas. In particular, our framework is instantiated by Petri nets and CSP, including a case study using Petri Nets.

Introduction to the special issue on formal modeling of semantic concepts

We introduce the special issue on formal models of semantic concepts. After outlining the research questions that motivated the issue, we summarize the rich set of data provided by the Leuven Natural Concepts Database, and provide an overview of the seven research articles in the special issue. Each of these articles applies a formal modeling approach to one or more parts of the database, attempting to further our understanding of how people represent and use semantic concepts.

Implementing an integrated framework for internal logistics management in automated semiconductor manufacturing

Nowadays, productivity and ergonomic reasons impose the use of Automated Material Handling Systems (AMHS) to transfer work-in-process within modern semiconductor factories. It is necessary to manage the AMHS to attain high throughput and short cycle times. Traditional simulation-based models considered the AMHS as an isolated system and do not provide formal modelling approaches necessary for its efficient reuse and management. This paper studies the implementation of a global modelling framework for the operational behaviour of wafer factories by using an efficient AMHS control policy.

Using expert knowledge to assess uncertainties in future polar bear populations under climate change

Summary Polar bear Ursus maritimus population dynamics under conditions of climate change has become a controversial topic. A survey of expert opinion based on modelled sea‐ice data was performed in order to quantify the trends and variance surrounding possible impacts of climate change on polar bear populations. Polar bears have become an iconic species in the communication of climate change. Negative impacts of climatic warming on polar bears have been suggested, but cannot be fully quantified as no Arctic‐wide models yet exist to analyse the relationship between polar bear population dynamics and climate change. Ten polar bear experts participated in an expert opinion survey in early 2007, quantifying the trends and variance surrounding possible impacts of climate change on polar bear populations. The experts were provided with maps and time‐series of sea‐ice extent and duration to 2050, simulated under a mid‐range emissions scenario. Expert projections of future polar bear habitat range and population size across the Arctic, and for population size in five regions, were obtained. Experts were asked to define ‘best conservation practice’, and to re‐evaluate the total Arctic population projection if this best practice was implemented. Most experts project a substantial decline in polar bear range and population size across the Arctic and in population size across each region. Expert best estimates for total Arctic polar bear population size lie from no change to a 70% decrease by 2050 relative to today; with half the experts projecting at least a 30% decrease. The median best estimates show the Barents Sea, Hudson Bay and the Chukchi Sea populations experiencing the greatest population decline under this scenario. There is much uncertainty both within and between expert responses, especially in little‐researched regions such as the Chukchi Sea. Synthesis and applications. Based on projected changes in sea‐ice extent, experts suggest that polar bear populations will undergo significant declines by 2050, even implementing best management practices, under the scenario of climatic warming outlined here. The expert survey approach could be applied to a wide range of species for which there is a lack of available data and considerable uncertainty surrounding all aspects of the problem that prevent quantification with more formal modelling approaches.

Decision making in bipolar disorder: A cognitive modeling approach

A formal modeling approach was used to characterize decision-making processes in bipolar disorder. Decision making was examined in 28 bipolar patients (14 acute and 14 remitted) and 25 controls using the Iowa Gambling Task ( Bechara et al., 1994), a decision-making task used for assessing cognitive impulsivity. To disentangle motivational and cognitive aspects of decision-making processes, we applied a formal cognitive model to the performance on the Iowa Gambling Task. The model has three parameters: The relative impact of rewards and punishments on evaluations, the impact of recent and past payoffs, and the degree of choice consistency. The results indicated that acute bipolar patients were characterized by low choice consistency, or a tendency to make erratic choices. Low choice consistency improved the prediction of acute bipolar disorder beyond that provided by cognitive functioning and self-report measures of personality and temperament.

The Intellectual Origins of New Political Science

In 1967, the burgeoning discontent of many political scientists culminated in the establishment of the Caucus for a New Political Science. The Caucus included political scientists of many diverse viewpoints, but it was united methodologically by a critique of behavioralism and by the idea that political science should abandon the myth of a value-free science. In recent years, political scientists have authored numerous commentaries on “the tragedy” of political science, “the crisis” in political science, and “the flight from reality in political science,” while in 2000 these discontents resurfaced in the “perestroika” rebellion, which again denounced the American Political Science Association as an organization that promotes a “narrow parochialism and methodological bias toward the quantitative, behavioral, rational choice, statistical, and formal modeling approaches.” This paper reviews the intellectual origins of New Political Science by examining some of the major works of the late 1960s and early 1970s purporting to establish the foundations of a new political science. It concludes that new political science offers a methodological critique of behaviorialism and a sociological critique of the relationship between political science and political power, but there is no consensus on what constitutes a new political science beyond its critical stance toward the existing discipline.

Toward Verified Biological Models

The last several decades have witnessed a vast accumulation of biological data and data analysis. Many of these data sets represent only a small fraction of the system's behavior, making the visualization of full system behavior difficult. A more complete understanding of a biological system is gained when different types of data (and/or conclusions drawn from the data) are integrated into a larger-scale representation or model of the system. Ideally, this type of model is consistent with all available data about the system, and it is then used to generate additional hypotheses to be tested. Computer-based methods intended to formulate models that integrate various events and to test the consistency of these models with respect to the laboratory-based observations on which they are based are potentially very useful. In addition, in contrast to informal models, the consistency of such formal computer-based models with laboratory data can be tested rigorously by methods of formal verification. We combined two formal modeling approaches in computer science that were originally developed for non-biological system design. One is the inter-object approach using the language of live sequence charts (LSCs) with the Play-Engine tool, and the other is the intra-object approach using the language of statecharts and Rhapsody as the tool. Integration is carried out using InterPlay, a simulation engine coordinator. Using these tools, we constructed a combined model comprising three modules. One module represents the early lineage of the somatic gonad of C. elegans in LSCs, while a second more detailed module in statecharts represents an interaction between two cells within this lineage that determine their developmental outcome. Using the advantages of the tools, we created a third module representing a set of key experimental data using LSCs. We tested the combined statechart-LSC model by showing that the simulations were consistent with the set of experimental LSCs. This small-scale modular example demonstrates the potential for using similar approaches for verification by exhaustive testing of models by LSCs. It also shows the advantages of these approaches for modeling biology.

Modélisation des systèmes physiques à commutations par approche énergétique. Application à un convertisseur DC-DC

This paper deals with two formal modelling approaches of switching physical systems, based on energetic considerations. The modelling of the continuous part is first presented followed by the determination of the automaton of the non controlled system. Both approaches are based on an ideal switching model leading to a variable topologic representation. The first one uses bond graph formalism and the second one, is based on interconnection graphs and hamiltonian formalism. An application on a DC-DC converter illustrates these two approaches.

Wise use of statistical tools in ecological field studies

The currently dominating hypothetico-deductive research paradigm for ecology has statistical hypothesis testing as a basic element. Classic statistical hypothesis testing does, however, present the ecologist with two fundamental dilemmas when field data are to be analyzed: (1) that the statistically motivated demand for a random and representative sample and the ecologically motivated demand for representation of variation in the study area cannot be fully met at the same time; and (2) that the statistically motivated demand for independence of errors calls for sampling distances that exceed the scales of relevant pattern-generating processes, so that samples with statistically desirable properties will be ecologically irrelevant. Reasons for these dilemmas are explained by consideration of the classic statistical Neyman-Pearson test procedure, properties of ecological variables, properties of sampling designs, interactions between properties of the ecological variables and properties of sampling designs, and specific assumptions of the statistical methods. Analytic solutions to problems underlying the dilemmas are briefly reviewed. I conclude that several important research objectives cannot be approached without subjective elements in sampling designs. I argue that a research strategy entirely based on rigorous statistical testing of hypotheses is insufficient for field ecological data and that inductive and deductive approaches are complementary in the process of building ecological knowledge. I recommend that great care is taken when statistical tests are applied to ecological field data. Use of less formal modelling approaches is recommended for cases when formal testing is not strictly needed. Sets of recommendations, "Guidelines for wise use of statistical tools", are proposed both for testing and for modelling. Important elements of wise-use guidelines are parallel use of methods that preferably belong to different methodologies, selection of methods with few and less rigorous assumptions, conservative interpretation of results, and abandonment of definitive decisions based a predefined significance level.

A formal modeling approach for supply chain event management

As supply chains become more dynamic, there is a need for a sense-and-respond capability to react to events in a real-time manner. In this paper, we propose Petri nets extended with time and color (to represent case data) as a formalism for managing events. We designed seven basic patterns to capture modeling concepts that arise commonly in supply chains. These basic patterns may be used by themselves and also combined to create new patterns. We also show how to combine the patterns to build a complete Petri net and analyze it using dependency graphs and simulation. Dependency graphs can be used to analyze the various events and their causes. Simulation was, in addition, used to analyze various performance indicators (e.g., fill rates, replenishment times, and lead times) under different strategies. We showed it is possible to perform sensitivity analysis to study the effect of changing parameter values on the performance indicators. This approach thus makes a very complex problem tractable.

Modeling centralized organization of organizational change

Organizations change with the dynamics of the world. To enable organizations to change, certain structures and capabilities are needed. As all processes, a change process has an organization of its own. In this paper it is shown how within a formal organization modeling approach also organizational change processes can be modeled. A generic organization model (covering both organization structure and behavior) for organizational change is presented and formally evaluated for a case study. This model takes into account different phases in a change process considered in Organization Theory literature, such as unfreezing, movement and refreezing. Moreover, at the level of individuals, the internal beliefs and their changes are incorporated in the model. In addition, an internal mental model for (reflective) reasoning about expected role behavior is included in the organization model.

Requirements modelling and formal analysis using graph operations

The increasing complexity of enterprise systems requires a more advanced analysis of the representation of services expected than is currently possible. Consequently, the specification stage, which could be facilitated by formal verification, becomes very important to the system life-cycle. This paper presents a formal modelling approach, which may be used in order to better represent the reality of the system and to verify the awaited or existing system's properties, taking into account the environmental characteristics. For that, we firstly propose a formalization process based upon properties specification, and secondly we use Conceptual Graphs operations to develop reasoning mechanisms of verifying requirements statements. The graphic visualization of these reasoning enables us to correctly capture the system specifications by making it easier to determine if desired properties hold. It is applied to the field of Enterprise modelling.

Kripke modelling approaches of a multiple robots system with minimalist communication: A formal approach of choice

Real-time, critical systems such as avionics software and hardware chips, owe their reliability to formal modelling approaches from their design phase. Following this philosophy, an intuitive yet mathematically rigorous approach of Kripke modelling is used for representing a co-operative, decentralised mobile robot group. The robot group operates with minimalist communication and no a priori knowledge of the operating environment for achieving interception and coordinated time-over-target. This scenario is a prototype of a simple task envisaged for a group of unmanned aerial vehicles (UAVs). As such, the resulting behaviour of the UAVs must be designed and analysed, so that guaranteed performance is ensured if only to achieve flight certification. Hence, there is a need for a mathematically rigorous approach, or a formal modelling technique. Several such techniques are briefly reviewed and it is argued that Kripke modelling is the best suited one. Kripke models consist of, firstly, a set of possible worlds (system configurations), secondly, an accessibility relation in the set (transitions between the worlds) and thirdly, a labelling function (which logical statements are true in each world). This approach represents continuous dynamics and discrete decision making of the robots in a unified way. Desirable properties of co-operation can be precisely expressed using temporal logic statements defining safety, liveness etc. Whether a group of robots, whose behaviour is formalised with Kripke models, possesses such properties is then verified using automated model checking tools. Rationale for this approach along with simulation and verification results are presented in the paper.

Formal agent-based modelling of intracellular chemical interactions

Individual-based or agent-based models have proved useful in a variety of different biological contexts. This paper presents an agent-based model using a formal computational modelling approach to model a crucial biological system—the intracellular NF-κB signalling pathway. The pathway is vital to immune response regulation, and is fundamental to basic survival in a range of species. Alterations in pathway regulation underlie many diseases, including atherosclerosis and arthritis. Our modelling of individual molecules, receptors and genes provides a more comprehensive outline of regulatory network mechanisms than previously possible with equation-based approaches. The model has been validated with data obtained from single cell experimental analysis.

Modelling inter-task dependencies and control of workflow managements systems based on supervisory control theory

A workflow is a computerized facilitation or automation of a business process, in whole or in part. Workflow Management Systems integrate humans, computer systems, information resources and organizational processes to provide a unified solution. With rapid increases in application domains that use Workflow Management Systems, it is essential to provide comprehensive and formal modelling approaches that can be used by various workflow software product developers to implement such systems. In this paper, a novel approach to modelling workflow systems based on Discrete Event System and Supervisory Control Theory is introduced. Supervisory controllers that are shown to guarantee desirable properties related to the execution of workflow processes are defined. Both centralized and decentralized supervisory controller designs are considered in order to address computational complexity and scalability issues.

A formal approach for modeling and verification of RTCORBA-based applications

We introduce a formal model for describing Real-Time CORBA-based applications, and a set of guidelines to formally check that the design of such an application is consistent with its specification. The model and the guidelines are then applied to the verification of a simple test application.

Formal modelling approaches for integrated quality-management systems in delocalised enterprises: a taxonomy of requirements

This paper provides a description of the assortment of functional requirements for an ideal formal modelling approach (FMA) to support the analysis, design and operation of quality management system (QMS). Particular attention is devoted to integration and to the new organisational delocalisation aspects, also recently experienced in the automotive sector. Based on a wide literature search, a taxonomy consisting of a set of five categories of functional requirements was built, useful in defining appropriate FMAs for different application purposes of QMSs. Formal modelling concepts and strategies are also discussed. A general evaluation of a number of classes of approaches available is also given.

Local Social Capital and Entrepreneurship

We aim to contribute to the analysis of the role that spacebound social capital plays in local/regional entrepreneurship. We compare social capital with other forms of capital. The conclusion is that in the main, social capital can be analyzed in the same way as other capital but it has some important special attributes. With these attributes as starting point we sketch an outline model of how the spatially-defined producer surplus and consumer surplus form a place surplus, and of the role played by social capital in the creation of the producer surplus. A "rereading" of Schumpeter shows that he was aware of several aspects of what we denominate entrepreneurship-inhibiting and facilitating aspects of social capital. Finally, we discuss some formal economic modeling approaches to the theoretical relationship between social capital and entrepreneurship.

Allocation of quality improvement targets based on investments in learning

AbstractPurchased materials often account for more than 50% of a manufacturer's product nonconformance cost. A common strategy for reducing such costs is to allocate periodic quality improvement targets to suppliers of such materials. Improvement target allocations are often accomplished via ad hoc methods such as prescribing a fixed, across‐the‐board percentage improvement for all suppliers, which, however, may not be the most effective or efficient approach for allocating improvement targets. We propose a formal modeling and optimization approach for assessing quality improvement targets for suppliers, based on process variance reduction. In our models, a manufacturer has multiple product performance measures that are linear functions of a common set of design variables (factors), each of which is an output from an independent supplier's process. We assume that a manufacturer's quality improvement is a result of reductions in supplier process variances, obtained through learning and experience, which require appropriate investments by both the manufacturer and suppliers. Three learning investment (cost) models for achieving a given learning rate are used to determine the allocations that minimize expected costs for both the supplier and manufacturer and to assess the sensitivity of investment in learning on the allocation of quality improvement targets. Solutions for determining optimal learning rates, and concomitant quality improvement targets are derived for each learning investment function. We also account for the risk that a supplier may not achieve a targeted learning rate for quality improvements. An extensive computational study is conducted to investigate the differences between optimal variance allocations and a fixed percentage allocation. These differences are examined with respect to (i) variance improvement targets and (ii) total expected cost. For certain types of learning investment models, the results suggest that orders of magnitude differences in variance allocations and expected total costs occur between optimal allocations and those arrived at via the commonly used rule of fixed percentage allocations. However, for learning investments characterized by a quadratic function, there is surprisingly close agreement with an “across‐the‐board” allocation of 20% quality improvement targets. © John Wiley & Sons, Inc. Naval Research Logistics 48: 684–709, 2001

Allocation of variance reduction targets under the influence of supplier interaction

A common quality improvement strategy used by manufacturers is to periodically allocate quality improvement targets among their suppliers. We propose a formal modelling and optimization approach for assessing quality improvement targets for suppliers. In this approach it is understood that a manufacturer's quality improvement results from reductions in supplier process variances, which occurs only through investments in learning. A constrained nonlinear optimization model is developed for determining an optimal allocation of variance reduction target that minimizes expected total cost, where the relationship between performance measures and the set of design parameters is generally represented by second-order polynomial functions. An example in the fabrication of a tyre tread compound is used both to demonstrate the implementation of our proposed models as well as to provide an empirical comparison of optimal learning rates for different functional relationships between the performance measures and the set of design parameters.