A Problem-driven Approach to Expert System Development

Expert system technology can fairly be described as high profile at present. Yet its track record as a useful method for tackling problems is subject to widely differing views. This paper considers the domain of production planning and control, which has been described as a good application area for expert systems a domain where OR has had little success. The authors argue that mainstream expert system methodology is exploratory rather than problem-driven and thus is not suited to the domain. A problem-driven approach to expert system development is presented, an approach which makes use of soft systems methodology. The reasons for such an approach within production planning and control are discussed, and the use of soft systems methodology within the approach is reflected on.     

Soft with a hard centre: discrete-event simulation in facilitation

Discrete event simulation is normally described as a ‘hard’ OR technique. This may not, however, always be the case. An example of a simulation of a user support helpline is described which, it is argued, has many of the traits of a ‘soft’ OR intervention. In particular, the study involved a facilitated discussion around a simulation model about possible improvements to a problem situation. The nature of the intervention is considered from both a methodological and paradigmatic perspective, and conclusions are drawn about where the intervention lies on a ‘hard’ to ‘soft’ continuum. It is argued that ‘soft’ issues need to be subsumed into the prescribed methodology for discrete-event simulation.     

Cost-efficiency analysis of weapon system portfolios

Decisions about the acquisition and maintenance of military equipment serve to build long-term capabilities in preparation of military conflicts. Typically, these decisions involve large investments which need to be supported by adequate cost-efficiency analyses. Yet the cost-efficiency analysis of weapon systems involves several challenges: for example, it is necessary to account for the possible interactions among different weapon systems; the relevance of several impact criteria; and the variety of combat situations in which these systems may be used. In this paper, we develop a portfolio methodology where these challenges are addressed by evaluating the cost-efficiencies of entire portfolios consisting of individual weapon systems. Our methodology accounts for possible interactions among systems by synthesizing impact assessment results that are either generated by combat simulation models or elicited from experts. It also admits incomplete preference information about the relative importance of different impact criteria. This methodology guides decision making by identifying which combinations of weapon systems are efficient with respect to multiple evaluation criteria in different combat situations at different cost levels. It can also be extended to settings where multiple combat situations are addressed simultaneously. The methodology is generic and can therefore be applied also in civilian settings when portfolios of activities (such as mitigation of harmful environmental emissions) may exhibit interactions.     

From Optimizing to Learning: A Development of Systems Thinking for the 1990s

In any subject concerned with rational intervention in human affairs, theory must lead to practice; but practice is the source of theory: neither theory nor practice is prime. We can examine this ‘groundless’ relation by asking what intellectual framework F is applied in what methodology M to what area of application A? If we do this for O.R., systems analysis, systems engineering etc., we see that F and M have changed dramatically between the 1950s and the 1980s, yielding the ‘hard’ and ‘soft’ traditions of systems thinking. The ‘hard’ tradition, based on goal seeking, is examined in the work of Simon and contrasted with the ‘soft’ tradition, based on learning, as exemplified in the work of Vickers and the development of soft systems methodology. The two are complementary, but the relation between them is that the ‘hard’ is a special case of ‘soft’ systems thinking. This analysis makes sense of the recent history of management science and helps to prepare us for the 1990s.     

An application of Soft Systems Dynamics Methodology (SSDM)

The paper argues that by combining some of systems dynamics (SD) and soft systems methodology (SSM) stages, within the intellectual framework proposed by soft systems dynamics methodology (SSDM), a systemic methodology developed by Rodriguez-Ulloa, much can be gained in a systemic intervention when tackling complex social problematic situations. A framework for comparing the ontological, epistemological and methodological principles of SD, SSM and SSDM is proposed and the synthesizing role of SSDM is advanced. SSDM ten stages are briefly outlined and a full account of an application on a small Peruvian enterprise is presented. A reflection on SSDM as a systemic intellectual tool is proposed and conclusions together with points for further research are suggested.     

Putting Soft OR Methods to Work: Information Systems Strategy Development at Richards Bay

The development of an information systems (IS) strategy for a mineral extraction and processing plant operated by Rio Tinto Zinc (RTZ) in South Africa is described. The participative approach used was developed from soft OR and systems thinking. The aim of the Richards Bay project was to test commercially whether the Participative Information Management Strategy (PIMS) methodology could be used as a standard method to be applied to the development of an information systems strategy at any mine site. The paper describes the implementation of the process and the problems encountered. More generally it discusses the diffusion of new ideas in an international company, illustrated by the use of the participative approach for other activities including systems development. Conclusions are drawn about the value of participative approaches based on soft OR in this context.     

A methodology to customize pull control systems

The objective of this paper is to develop a methodology for customizing pull control systems, replacing traditional control systems such as Kanban, Conwip, and Base Stock. The basic principle is to start with a pull control system that connects each stage of a given production line with each preceding stage; next, the optimization of the corresponding simulation model shows which of these potential control loops are actually implemented. The result of this methodology may be one of the traditional systems, but it may also be one of the following three new types: (1) the total line may be decomposed into several segments, each with its own classic control system, (2) the total line or its segments may combine different traditional systems, or (3) the line may be controlled through a new type of control system. Indeed, these three systems are found in a simulation experiment with a set of twelve production lines. These twelve lines are selected through a statistical design with ten factors that characterize production lines.     

On the Analysis of Multibody Systems in the Presence of Uncertainties

This contribution introduces and illustrates the concept of fuzzy arithmetic as a modeling and analysis tool for the simulation of multibody systems subjected to uncertainties. The uncertainties are described by the use of fuzzy numbers and the simulation procedure is performed by employing the Transformation Method, which is a practical methodology for the implementation of fuzzy arithmetic. (© 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Global and local optimization using radial basis function response surface models

The focus of this paper is the optimization of complex multi-parameter systems. We consider systems in which the objective function is not known explicitly, and can only be evaluated through computationally intensive numerical simulation or through costly physical experiments. The objective function may also contain many local extrema which may be of interest. Given objective function values at a scattered set of parameter values, we develop a response surface model that can dramatically reduce the required computation time for parameter optimization runs. The response surface model is developed using radial basis functions, producing a model whose objective function values match those of the original system at all sampled data points. Interpolation to any other point is easily accomplished and generates a model which represents the system over the entire parameter space. This paper presents the details of the use of radial basis functions to transform scattered data points, obtained from a complex continuum mechanics simulation of explosive materials, into a response surface model of a function over the given parameter space. Response surface methodology and radial basis functions are discussed in general and are applied to a global optimization problem for an explosive oil well penetrator.     

Dynamic soft variable structure control of singular systems

The dynamic soft variable structure control (VSC) of singular systems is discussed in this paper. The definition of soft VSC and the design of its controller modes are given. The stability of singular systems with the dynamic soft VSC is proposed. The dynamic soft variable structure controller is designed, and the concrete algorithm on the dynamic soft VSC is given. The dynamic soft VSC of singular systems which was developed for the purpose of intentionally precluding chattering, achieving high regulation rates and shortening settling times enhanced the dynamic quality of the systems. It is illustrated the feasibility and validity of the proposed strategy by a simulation example, and an outlook on its auspicious further development is presented.     

A philosophy for the modelling of realistic nonlinear systems

A nonlinear dynamical system is modelled as a nonlinear mapping from a set of input signals into a corresponding set of output signals. Each signal is specified by a set of real number parameters, but such sets may be uncountably infinite. For numerical simulation of the system each signal must be represented by a finite parameter set and the mapping must be defined by a finite arithmetical process. Nevertheless the numerical simulation should be a good approximation to the mathematical model. We discuss the representation of realistic dynamical systems and establish a stable approximation theorem for numerical simulation of such systems.

     

Beyond methodology choice: critical systems thinking as critically systemic discourse

Professional competence in applied disciplines such as OR/MS requires both technical expertise and critically reflective skills. Yet, a widespread misconception has taken hold of the OR/MS community: ‘critical’ and ‘emancipatory’ systems methodologies are opposed to ‘hard’ and ‘soft’ ones as if they were sensible alternatives. Accordingly, adequate ‘methodology choice’ is now widely considered a key condition of reflective professional practice; critical systems thinking (CST) is understood to deal mainly with this issue. The present paper argues that this conception of CST is neither theoretically sound nor conducive to reflective practice. An examination of the two major current strands of CST suggests some basic requirements of an alternative conception: (1) Reflective practice depends more on a framework of critical argumentation and discourse than on a framework of methodology choice. (2) A well-conceived discursive systems approach will give a proper place to the public sphere. (3) The much-discussed emancipatory orientation of CST inheres in the methodological requirements of discourse rather than in an arbitrary ‘commitment’ on the part of the systems practitioner. (4) Systemic boundary critique—the methodological core concept of critical systems heuristics (CSH)—allows us to translate these requirements into practical methodology. (5) Contrary to present conceptions of methodological pluralism or ‘complementarism’, boundary critique must not be subordinated to methodology choice, for it is constitutive of all critical inquiry and practice. These considerations lead to a reconstitution of CST, and to a new view of reflective professional practice in general, as critically systemic discourse.     

Temperature prediction at critical points in district heating systems

Current methodologies for the optimal operation of district heating systems use model predictive control. Accurate forecasting of the water temperature at critical points is crucial for meeting constraints related to consumers while minimizing the production costs for the heat supplier. A new forecasting methodology based on conditional finite impulse response (cFIR) models is introduced, for which model coefficients are replaced by coefficient functions of the water flux at the supply point and of the time of day, allowing for nonlinear variations of the time delays. Appropriate estimation methods for both are described. Results are given for the test case of the Roskilde district heating system over a period of more than 6 years. The advantages of the proposed forecasting methodology in terms of a higher forecast accuracy, its use for simulation purposes, or alternatively for better understanding transfer functions of district heating systems, are clearly shown.     

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.     

Knowledge-based improvement: simulation and artificial intelligence for identifying and improving human decision-making in an operations system

The performance of most operations systems is significantly affected by the interaction of human decision-makers. A methodology, based on the use of visual interactive simulation (VIS) and artificial intelligence (AI), is described that aims to identify and improve human decision-making in operations systems. The methodology, known as ‘knowledge-based improvement’ (KBI), elicits knowledge from a decision-maker via a VIS and then uses AI methods to represent decision-making. By linking the VIS and AI representation, it is possible to predict the performance of the operations system under different decision-making strategies and to search for improved strategies. The KBI methodology is applied to the decision-making surrounding unplanned maintenance operations at a Ford Motor Company engine assembly plant.     

Using SSM to structure the identification of inputs and outputs in DEA

This paper reports on an innovative combination of hard and soft methods—soft systems methodology (SSM) with data envelopment analysis (DEA). Problems in defining and agreeing appropriate inputs and outputs for DEA led to the use of SSM as a way or producing a comprehensive and systemic database of performance indicators. The contributions of the paper are: the use of SSM to improve DEA specifications; conceptual clarifications within both SSM and DEA; and an innovative example of multimethodology. These developments are illustrated through a study evaluating the performance of the basic research institutes of the Chinese Academy of Sciences.     

Contour estimation via two fidelity computer simulators under limited resources

The utilization of multiple fidelity simulators for the design and analysis of computer experiments has received increased attention in recent years. In this paper, we study the contour estimation problem for complex systems by considering two fidelity simulators. Our goal is to design a methodology of choosing the best suited simulator and input location for each simulation trial so that the overall estimation of the desired contour can be as good as possible under limited simulation resources. The proposed methodology is sequential and based on the construction of Gaussian process surrogate for the output measure of interest. We illustrate the methodology on a canonical queueing system and evaluate its efficiency via a simulation study.     

Simulation for business process re-engineering: case study of a database management system

This paper describes the use of discrete event simulation in the resource, throughput and content analysis and improvement of a business process in the Iranian Information and Documentation Centre (IRANDOC). The process was initially mapped using Data Flow Diagrams (DFD), and the objectives of the study for a range of users were captured using the Delphi method. The time taken for the tasks in the process were estimated by work study, and the simulation was developed in ServiceModel?. The simulation was valuable in understanding why the current systems were not performing satisfactorily and led to a number of proposals for improvement. This mixed use of hard (simulation) and soft (Delphi) methods proved a fruitful way to gain process improvements and could be employed in other organisations.     

The Waveform Relaxation method for systems of differential/algebraic equations

This paper reports efforts towards establishing a parallel numerical algorithm known as Waveform Relaxation (WR) for simulating large systems of differential/algebraic equations. The WR algorithm was established as a relaxation based iterative method for the numerical integration of systems of ODEs over a finite time interval. In the WR approach, the system is broken into subsystems which are solved independently, with each subsystem using the previous iterate waveform as “guesses” about the behavior of the state variables in other subsystems. Waveforms are then exchanged between subsystems, and the subsystems are then resolved repeatedly with this improved information about the other subsystems until convergence is achieved.

In this paper, a WR algorithm is introduced for the simulation of generalized high-index DAE systems. As with ODEs, DAE systems often exhibit a multirate behavior in which the states vary as differing speeds. This can be exploited by partitioning the system into subsystems as in the WR for ODEs. One additional benefit of partitioning the DAE system into subsystems is that some of the resulting subsystems may be of lower index and, therefore, do not suffer from the numerical complications that high-index systems do. These lower index subsystems may therefore be solved by less specialized simulations. This increases the efficiency of the simulation since only a portion of the problem must be solved with specially tailored code. In addition, this paper established solvability requirements and convergence theorems for varying index DAE systems for WR simulation.     

Simulation Model Development and Analysis in UNITY

We evaluate UNITY – a computational model, specification language and proof system defined by Chandy and Misra [5] for the development of parallel and distributed programs – as a platform for simulation model specification and analysis. We describe a UNITY-based methodology for the construction, analysis and execution of simulation models. The methodology starts with a simulation model specification in the form of a set of coupled state transition systems. Mechanical methods for mapping the transition systems first into a set of formal assertions, permitting formal verification of the transition systems, and second into an executable program are described. The methodology provides a means to independently verify the correctness of the transition systems: one can specify properties formally that the model should obey and prove them as theorems using the formal specification. The methodology is illustrated through generation of a simulation program solving the machine interference problem using the Time Warp protocol on a distributed memory parallel architecture.