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.     

Empirical properties of group preference aggregation methods employed in AHP: Theory and evidence

We study various methods of aggregating individual judgments and individual priorities in group decision making with the AHP. The focus is on the empirical properties of the various methods, mainly on the extent to which the various aggregation methods represent an accurate approximation of the priority vector of interest. We identify five main classes of aggregation procedures which provide identical or very similar empirical expressions for the vectors of interest. We also propose a method to decompose in the AHP response matrix distortions due to random errors and perturbations caused by cognitive biases predicted by the mathematical psychology literature. We test the decomposition with experimental data and find that perturbations in group decision making caused by cognitive distortions are more important than those caused by random errors. We propose methods to correct the systematic distortions.     

Modeling uncertainty in multi-criteria decision analysis

his paper provides a review of multiple criteria decision analysis (MCDA) for cases where attribute evaluations are uncertain. The main aim is to identify different tools which can be used to represent uncertain evaluations, and to broadly survey the available decision models that can be used to support uncertain decision making. The review includes models using probabilities or probability-like quantities; explicit risk measures such as quantiles and variances; fuzzy numbers, and scenarios. The practical assessment of uncertain outcomes and preferences associated with these outcomes is also discussed.     

On the inconsistency of the Malmquist–Luenberger index

Apart from the well-known weaknesses of the standard Malmquist productivity index related to infeasibility and not accounting for slacks, already addressed in the literature, we identify a new and significant drawback of the Malmquist–Luenberger index decomposition that questions its validity as an empirical tool for environmental productivity measurement associated with the production of bad outputs. In particular, we show that the usual interpretation of the technical change component in terms of production frontier shifts can be inconsistent with its numerical value, thereby resulting in an erroneous interpretation of this component that passes on to the index itself. We illustrate this issue with a simple numerical example. Finally, we propose a solution for this inconsistency issue based on incorporating a new postulate for the technology related to the production of bad outputs.     

Influence of model management systems on decision making: empirical evidence and implications

Model management (MM) regards decision models as an important organisational resource deserving prudent management. Despite the remarkable volume of model management literature compiled over the past twenty-odd years, very little is known about how decision makers actually benefit from employing model management systems (MMS). In this paper, we report findings from an experiment designed to verify the idea that the adequacy of modeling support provided by a MMS influences the decision maker's problem solving performance and behaviour. We show that the decision makers who receive adequate modelling support from MMS outperform those without such support. Also, we provide empirical evidence that the MMS help turn the decision makers' perception of problem solving from a number crunching task into development of solution strategies, consequently changing their decision making behaviour.     

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.     

How to benefit from decision analysis in environmental life cycle assessment (LCA)

Environmental life cycle assessment (LCA) is one method to support environmental information needs by multi-attribute product evaluations. LCA describes the environmental effects associated with a product, process or activity over its whole life cycle by calculating the material and energy requirements as well as emissions to air, water and soil and by assessing the environmental impacts of those. An LCA study has both objective and subjective steps. So far, LCA has been developed without much consideration of the literature on decision modelling. We want to show that approaches and tools from decision analysis would be beneficial both in the planning of an LCA study and in the interpretation and understanding of the results. We describe the LCA methodology and application, and discuss how the integration of decision analysis and LCA could improve LCA as a tool for decision making. We use an LCA study on beverage packaging systems to illustrate the new approach.     

Hit or miss: A decision support system framework for signing new musical talent

In the music industry, the process of signing new musical talent is one of the most complex decision-making problems. The decision, which is generally made by an artist and repertoire (A&R) team, involves consideration of various quantitative and qualitative criteria, and usually results in a low success rate. We conducted a series of mental model interviews with the aim of developing a decision support framework for A&R teams. This framework was validated by creating a decision support system that utilises multi-criteria decision analysis to support decision-making. Our framework and subsequent implementation of the decision support system involving decision rule and weighted sum methods show an improvement in the ability to analyse and decide on greater amounts of talent. This paper serves as a building block for developing systems to aid in this complex decision-making problem.     

Updating attribute reduction in incomplete decision systems with the variation of attribute set

In rough set theory, attribute reduction is a challenging problem in the applications in which data with numbers of attributes available. Moreover, due to dynamic characteristics of data collection in decision systems, attribute reduction will change dynamically as attribute set in decision systems varies over time. How to carry out updating attribute reduction by utilizing previous information is an important task that can help to improve the efficiency of knowledge discovery. In view of that attribute reduction algorithms in incomplete decision systems with the variation of attribute set have not yet been discussed so far. This paper focuses on positive region-based attribute reduction algorithm to solve the attribute reduction problem efficiently in the incomplete decision systems with dynamically varying attribute set. We first introduce an incremental manner to calculate the new positive region and tolerance classes. Consequently, based on the calculated positive region and tolerance classes, the corresponding attribute reduction algorithms on how to compute new attribute reduct are put forward respectively when an attribute set is added into and deleted from the incomplete decision systems. Finally, numerical experiments conducted on different data sets from UCI validate the effectiveness and efficiency of the proposed algorithms in incomplete decision systems with the variation of attribute set.     

A survey of contingency factors affecting the validation of end-user spreadsheet-based decision support systems

Decision support systems have become widespread in recent years. However, most of these systems are not carefully validated and this has led in some cases to serious errors. One of the principal reasons for the lack of validity is the lack of an operationalised validation methodology. A simplified validity framework has already been developed by the authors as the first part of a 3-phase approach towards devising a practical validation methodology. This paper describes the second phase, reporting as it does on a survey that has been undertaken to unearth the contingency factors that influence how the process of validation, employing the validity framework, will be carried out for a particular kind of decision support system—one involving a spreadsheet model.     

Impact of information errors on supply chain performance

This study investigates how information errors affect supply chain performance when a retailer and a supplier share the end-customer demand information. An Enterprise Resource Planning (ERP) system, often used to share information in a supply chain, is not perfect and often contains erroneous information. Companies contemplating the use of ERP systems must evaluate the benefit of using them despite the existence of errors in the system. Our study quantifies the impact of information errors by comparing the supplier costs with and without errors. Our analytical and computational results indicate that the detrimental impact of errors outweighs the beneficial impact of information sharing when the variance of information errors exceeds the variance of end-customer demands. Therefore, when that happens, it is best to operate as if the information is not available. Finally, if it is possible to expend effort and reduce information errors, we present an analytical model for determining the optimal level of investment.     

Remarks on the existence of Cartier divisors

We characterize those invertible sheaves on a noetherian scheme which are definable by Cartier divisors and correct an erroneous counterexample in the literature.     

Developments in linear and integer programming

In this review we describe recent developments in linear and integer (linear) programming. For over 50 years Operational Research practitioners have made use of linear optimisation models to aid decision making and over this period the size of problems that can be solved has increased dramatically, the time required to solve problems has decreased substantially and the flexibility of modelling and solving systems has increased steadily. Large models are no longer confined to large computers, and the flexibility of optimisation systems embedded in other decision support tools has made on-line decision making using linear programming a reality (and using integer programming a possibility). The review focuses on recent developments in algorithms, software and applications and investigates some connections between linear optimisation and other technologies.     

Realizations of abstract regular polytopes from a representation theoretic view

Peter McMullen has developed a theory of realizations of abstract regular polytopes, and has shown that the realizations up to congruence form a pointed convex cone which is the direct product of certain irreducible subcones. We show that each of these subcones is isomorphic to a set of positive semi-definite hermitian matrices of dimension m over either the real numbers, the complex numbers or the quaternions. In particular, we correct an erroneous computation of the dimension of these subcones by McMullen and Monson. We show that the automorphism group of an abstract regular polytope can have an irreducible character \({\chi}\) with \({\chi \neq \overline{\chi}}\) and with arbitrarily large essential Wythoff dimension. This gives counterexamples to a result of Herman and Monson, which was derived from the erroneous computation mentioned before. We also discuss a relation between cosine vectors of certain pure realizations and the spherical functions appearing in the theory of Gelfand pairs.     

Planning performance based contracts considering reliability and uncertain system usage

This paper investigates a novel quantitative approach for planning and contracting performance-based logistics in the presence of uncertain system usage. Our efforts focus on an integrated service delivery environment where the manufacturer develops capital-intensive systems and also provides after-sales support. We propose an analytical model to characterize system operational availability by comprehending five performance drivers: inherent failure rate, usage rate, spare parts inventory, repair time, and the fleet size. This analytical insight into the system performance allows the service supplier to minimize the total cost across system design, production, maintenance, and repair. Two contracting schemes are investigated under cost minimization and profit maximization schemes. For the first time in literature, reliability design and service parts logistics are seamlessly integrated into one decision support model for improving operational availability while lowering the lifecycle cost. Numerical examples are provided to demonstrate the applicability and the effectiveness of the proposed decision support tool.     

The AC Stark Effect, Time-Dependent Born–Oppenheimer Approximation, and Franck–Condon Factors

We study the quantum mechanics of a simple molecular system that is subject to a laser pulse. We model the laser pulse by a classical oscillatory electric field, and we employ the Born–Oppenheimer approximation for the molecule. We compute transition amplitudes to leading order in the laser strength. These amplitudes contain Franck–Condon factors that we compute explicitly to leading order in the Born–Oppenheimer parameter. We also correct an erroneous calculation in the mathematical literature on the AC Stark effect for molecular systems. Communicated by Christian Gérard. Submitted: August 15, 2005; Accepted: October 13, 2005     

Some Experiences with the Teaching of Decision Support Systems to Management Students

There are essentially two schools of thought regarding the function of decision support systems. One is that the main purpose is to support the decision-maker in whatever style of decision-making he or she wishes. The other is that the support, while suiting the decision-maker's individual style, must also be based on an appropriate theoretical paradigm. The work with students of decision support systems reported here suggests that the latter, normative, view is not one which comes naturally to many of the students, even when working on an example where it seemed relatively easy to incorporate. Other conclusions are also presented, relating to the choice of software for decision support system development, the process of teaching about decision support systems, and group formation.     

Energy related system dynamic models: a literature review

System dynamics is extensively used as a decision support method in the energy sector. There exists a wide body of applications worldwide that are used not only within power companies but also by governmental agencies at the regional and national level. This review includes most of the relevant energy publications related to system dynamics and presents them within a literature review table divided into four key energy topics. This literature review is carried out in a chronological way and focuses on the period since the year 2000. The main purpose of this study is to summarise the remarkable body of work and the latest system dynamics trends related to the energy sector, in particular renewable energy that system dynamics practitioners have accumulated in the last 15 years.     

Finding reliable solutions: event-driven probabilistic constraint programming

Real-life management decisions are usually made in uncertain environments, and decision support systems that ignore this uncertainty are unlikely to provide realistic guidance. We show that previous approaches fail to provide appropriate support for reasoning about reliability under uncertainty. We propose a new framework that addresses this issue by allowing logical dependencies between constraints. Reliability is then defined in terms of key constraints called “events”, which are related to other constraints via these dependencies. We illustrate our approach on three problems, contrast it with existing frameworks, and discuss future developments.