Problem Owners Research Articles

Views of mathematical programming models and their instances

Large-scale mathematical models are built, managed and applied by people with different cognitive skills. This poses a challenge for the design of a multi-view architecture of a system that accommodates these differences. A primary objective of mathematical modeling is providing insights into problem behavior, and there are many constituencies who require different views for different questions. One constituency is composed of modellers who have different views of basic model components. Another constituency is composed of problem owners for whom models are built. These two constituencies, which are not exhaustive, have significantly different needs and skills. This paper addresses this issue of multiview architecture by presenting a formal framework for the design of a view creation and management system. Specific views we consider include algebraic, block schematic, graphic, and textual. Both form and content are relevant to view creation, and the merits of views are determined by their value in aiding comprehension and insight. The need for a central, formal structure to create and manage views is demonstrated by the inadequacy of direct mappings from any of the popular systems that are typically designed to support only one view of linear programming models and their instances.

Regional development: who are the problem owners?

In this article we raise the question: who is actually responsible for and competent in regional economic development? The discussion builds on a Scandinavian empirical base and a British understanding of the concept ‘enterprise development’, but is also linked to a cultural understanding of the concepts of responsibility and competence. By referring to experiences based on the action research method we discuss the probable roles of four different groups of actors in a given region. That is: citizens, environmentalists, the local authorities and the local enterprises. Our conclusion is that a fifth group of actors-the highly educated unemployed and graduate students (potentially unemployed)-are the problem owners of regional development, because they are the actors who may work innovatively in interdisciplinary groups and thus overcome the barriers to innovation mentioned in the text

Whither VIM? — A developer's view

Visual Interactive Modelling is where it is today because of the output of the (relatively few) VIM ‘software package’ developers, driven by rapid improvements in the technology of micros and colour screens. There are now many VIM users — most Operational Research units use VIM, and they are all convinced of its appeal. There is undoubtedly a rapid widening in the range of VIM application areas. But of more importance to the package developers is the extension of the VIM user profile to include those with, perhaps, less modelling ability than the Operational Research professional. These market pressures raise some questions in the developer's mind: What is the intrinsic nature of a problem which makes it susceptible to the VIM approach? What should be the intrinsic nature of VIM software? Does ‘ease of use’ necessarily mean ‘less powerful’? Is there a danger in putting a powerful technique into the hands of the inexperienced modeller? How can model development deep pace with the thought process of the problem owner, and help to ‘uncover’ the problem?

Task-allocation between DSS and problem owner: The example of box & jenkins time-series analysis

The advent of so-called knowledge-based elements in DSS seems to shed new light on the allocation of tasks between decision maker and DSS. Expert Systems or Knowledge Based DSSs incorporate domainknowledge, thereby taking over tasks that used to be the sole responsibility of users and enabling non-expert decision-makers to make expert-quality decisions. In view of these new capabilities, we critically analyse in this article the task-allocation between these ‘enhanced’ DSSs and the problem-owner. The discussion is based on a framework in which the unstructured nature of DSS-tasks and the implied goals of the problem-owner play a central role. The implications are illustrated with a case on Box-Jenkins time-series analysis, a typical example of a DSS application based on an elaborate mathematical model for a relatively complex task.

An Undergraduate Course in Engineering, Manufacture and Management

SUMMARY This paper describes a 4-year undergraduate course which covers mechanical/manufacturing engineering and management functions. At the start of the course the emphasis is on technology but in later years management/business studies predominate. The factor which is common to both technology and management functions is problem-solving. This is practised within a variety of projects undertaken throughout the 4-years. Most of the projects deal with problems being experienced by local (usually small) companies. They range from product improvement, to new products, to marketing strategy and business plans for future developments. The exercises involve: problem identification and strategy formulation through techniques suck as brainstorming, information gathering and analysis, familiarisation with previously unknown technologies, design of ad hoc tests and analysis of results, report preparation and verbal submission of solutions to the problem owners (the company directors). Because they work close to company executives, students have yardsticks with which to measure their own capabilities. The excitement generated by problem-solving exercises and the confidence gained through working one-to-one with company managers leads the aspirations of many of the undergraduates towards careers with strong management functions. Currently the most popular job area is management consultancy, followed by manufacturing engineering. The path of true entrepreneurship, exemplified by setting up a business, is being taken by an increasing number of students.

A theory of requisite decision models

A requisite decision model is defined as a model whose form and content are sufficient to solve a particular problem. The model is constructed through an interactive and consultative process between problem owners and specialists (decision analysts). The process of generating the model uses participants' sense of unease about current model results to further development of the model. Sensitivity analyses facilitate the emergence of new intuitions about the problem; when no new intuitions arise, the model is considered requisite. At all stages of development, the model represents the social reality of the shared understanding of the problem by the problem owners. The goal of creating a requisite model is to help construct a new reality, to create a future. Validating a requisite decision model is done with reference to a requisite validation model whose form will be recognizably multi-attributed and whose content may draw on a variety of disciplines both scientific and clinical. A requisite model is more likely to be adequate if problem owners contributing to its development represent a variety of views, if the adversarial process is used to advantage, and if the specialist can provide a neutral perspective and setting. The role of decision analysis is to provide a framework for the development of a coherent model and to provide structure to thinking. While requisite models may be applicable in other areas of social science, they certainly highlight the need for a psychology of what people can do.

Heuristics—the relational approach

A review of the literature on heuristics would suggest two approaches to their use in problem solving: mathematical and engineering. It is suggested however that there is a third approach for real world application, which the authors have called relational. Instead of investigating a problem through the medium of mathematical models and then deriving heuristics because direct optimising techniques are not available, it is advocated that a close relationship between problem owner and problem solver can be achieved by setting down together the decision rules that the owner employs. In this way, a heuristic model is developed directly, with the opportunity to introduce additional procedures as the situation allows. The result is a consistent control mechanism which is invaluable for both strategic and operational decision making. The model can be a predictor of the effects of policy decisions as well as a means by which those decisions can be implemented and monitored, dependent as much upon the balancing of sometimes conflicting objectives by management as upon the setting of bounds to achieve a guaranteed performance.