OR Analyst Research Articles

Contributors

Contributors

Contributors

Contributors

Epistemic dialog between health services and operations research

The study proposes a dialogical approach between OR and Health Services Planning and Epidemiology based on the similarities of their own epistemological experiences, according to Habermas' Theory of Knowledge. As a field of application, healthcare services planning and epidemiology are characterized as Complex Societal Problems, requiring multidisciplinary and multi dimensional approaches. A review of the literature on OR efficiency healthcare services applications is made to confront the perspective of the OR analyst and that of the health manager. Finally, an agenda is proposed to enhance the interaction between the disciplines, by increasing actuality of the OR methods' findings, to guarantee that the results of health services research will really be put in practice by health policy decision makers.

Mixed integer programming: A historical perspective with Xpress-MP

The mathematical programming system Xpress-MP, Dash Optimization (2006), was first released in 1983 as a tool for modeling and solving linear programming (LP) models on a PC, and it was extended in 1986 to deal with mixed integer programming (MIP) models. Its initial authors, Bob Daniel and I, had previously worked on Scicon’s (now part of EDS) mathematical programming system for Univac mainframes and mini computers such as DEC’s VAX series. Although Xpress-MP was originally designed as a simple tool that every OR Analyst should have on his desk, by 1990 an Intel 486 based PC had as much power as an IBM 3090 mainframe for the numerical work required by mathematical programming and Xpress on such a PC ran faster in elapsed time than IBM’s industry standard MPSX package, IBM (1988), on a typical 3090. From that point onward desktop computers running Xpress-MP were, from a practical point-of-view, as powerful a tool for solving industrial MP problems as software on any standard mini or mainframe computer. My career in mathematical programming was motivated by an early experience of its impact on a British engineering business. With two of my students, I built a simple model of a wire-pulling factory in Liverpool. The factory took as input 8 mm copper rods, which it drew through dies and coated in varnish, to produce electrical wire that was mainly used by electric motor manufacturers. When we started work in 1987, the factory was losing around £800,000 per year and closure seemed imminent. Our simple model looked at a years’ worth of orders, what was required to process them, and their machine requirements. From just looking at the orders’ reduced costs we saw that different customers were placing orders for similar quantities of similar products at vastly different prices. The reason

Neural networks and operations research: An overview

Problems that seem to be encountered, time and time again, by the practicing OR analyst include: 1. (i) resource allocation (i.e. continuous and discrete optimization); 2. (ii) classification (i.e. pattern recognition, or discriminant analysis); 3. (iii) prediction/estimation; and 4. (iv) clustering. When faced with such problems, the OR analyst has a number of conventional methods to choose from, such as: linear programming, discrete optimization, statistically based discriminant analysis, regression and cluster analysis. In this paper we will address yet another alternative: the employment of neural networks. While certainly no panacea, the neural network approach may, in certain instances, offer advantages that range from minor to substantial. Further, a neural network approach permits solution by means of parallel processing, thus providing certain unique and significant advantages that are inherent to distributed computing. As such, the OR analyst who remains unfamiliar with this approach cannot, we believe, consider himself or herself to be fully prepared for the most effective solution of a variety of problems, both now and in the future. In this paper, we shall introduce the neural network approach, from an OR perspective—and indicate just where and how such a tool might find application.

The decision in your hands

Decision-making in managerial groups can require facilitation to provide an effective mechanism. In the past, the chief focus for facilitation has been skills training for decision group members, for example in chairing meetings. The application of information technology (IT) to group work provides a relatively new means of facilitation, particularly with regard to the provision of timely, relevant feedback to group members. In the system described here, this feedback is provided in the form of data and summaries representing attitudes, feelings or performance of group members. The role of the OR analyst is as facilitator, controlling the IT function and providing reassurance on quantitative elements of feedback.

On the relevance of fuzzy sets for operations research

Every researcher in OR models knows that data in this field of human science are deterministic or random or uncertain. Of course, if measurements are available, the scientist must use such strong data but, in many case, a lot of data are weaker and subjectivity is necessary. To combine in a good way, at the best, taking account the present level of knowledge, it is what we can do. Fuzzy sets—and specially, fuzzy numbers—is a good tool for the OR analyst facing partial uncertainty and subjectivity. We are able to associate with several hybrid operators, probabilistic and uncertain data. Several processes and examples will be explained. The goal: to build a model faithful at the best and intelligible for the decision maker.

Antenna array beam pattern synthesis via goal programming

It is interesting to note that the field of operations research and that of radar both began in World War II and, initially, the success of one was greatly influenced by that of the other. It is a matter of historical record [6] that one of the very first problems investigated by the early operational research groups was that of the siting and implementation of radar in both air battles and U-boat detection. Since World War II, the tools of the OR analyst have been refined and increased while, at the same time, the modern radar system has become vastly larger and more complex. In this paper we address one particular problem associated with the radar system: the synthesis of the beam patterns produced by largescale antenna arrays. The solution of this important problem is achieved by means of the implementation of a relatively new tool of optimization (speci-

The OR Analyst as a Hired Gun: A Short Case History

Operations Research analysts, by their very job descriptions, are continually involved in problems concerned with the rules governing the operations of various enterprises. It is unusual if an Operations Research project has no political overtones. Many times the politics may be simply due to the fact that there is resistance to change, change which may be brought about as a result of the OR analyst's work. Other times the politics may be due to conflicting objectives of different elements of the organization. In this case the analyst may find himself in a mediator role between the two elements or he may work behind the scenes as a partisan (or “hired gun”) analyst for one side or the other. It is the “hired gun” situation which we discuss here.

Operations research and computers in law enforcement: Some case studies of american police experience

The application of Operations Research and Systems Analysis to Law Enforcement was encouraged by the President's Crime Commission in 1967. Since then there have been a number of projects. This paper describes operations research activities in Chicago and Washington, D.C. which are illustrative of the kinds of opportunities that exist in Law Enforcement. Applicable objectives and measures of effectiveness are discussed along with some of the results of a systems analysis of the Chicago Police Department that yielded in 1969 a program budget; insight on the police response function (including forecasting demands for service) and on the prevention patrol function of the police, and tactical studies of response and prevention patrol. The problem of total manpower resource allocation is discussed from various police-community standpoints. Although there are some pitfalls for the OR analyst in Law Enforcement applications, the Law Enforcement problems are sufficiently amenable to OR and Computer sciences techniques that a bright prognosis for the future of OR is made.

Operations Research Education: Some Requirements and Deficiencies

In recent years some operations researchers have directed their introspection to the education of prospective OR analysts, with a resultant critical appraisal: they are ill prepared to solve problems. The purpose of this paper is to continue this introspection by presenting some conjectures on the education of the OR analyst. After examining the problem-solving process briefly and defining some requisite terminology, it gives a premise on which educational requirements for the OR analyst rest, suggests areas in which his education is deficient, discusses some reasons for these deficiencies, and notes some possible courses of remedial action.

On the Establishment of Standards for Comparing Algorithm Performance

One of the most frustrating tasks encountered by the OR practitioner is that of attempting to select an existing algorithm for application to an immediate real world problem. For purposes of illustration only, consider the OR analyst who is faced with having to solve a large zero-one integer programming problem. Being a member of ORSA, TIMS, SIAM, and other organizations, he realizes that a considerable number of publications have been addressed to this topic and thus he might be able to use one of these to solve the problem at hand. This is sometimes his first mistake. Let us assume that he has surveyed the literature and found “n” publications presenting algorithms which deal directly with his particular problem. At this point in his search he will begin to discover that the mere existence of pertinent algorithms in the literature is not nearly sufficient.

Right and Wrong

I think some clarity is needed in the flak flying in the REVIEW between what Engel represents (see PAR Communications July/August 1969) and what Hamilton represents (ibid.). Both are partially right and partially wrong in their conclusions. Engel is correct in stating that the methodologies gathered under the heading of operations can be helpful. That is, if: (1) the analyst realizes that the methodology that is used with in one type of system (e.g., physical) may necessitate reformulation for use in another system type (e.g., social systems in the civil sector), and (2) that the content of the analysis must include the political variables of attitudes and value structures, spectrum positions, and intensity levels of particular publics and policy formulators. If Mr. Engel inferred such when he stated using the combined knowledge of hard and soft scientist alike, it is not clear. Hamilton is correct in his disdain for the lack of success to date. A number of the RAND efforts in New York have been performed on the basis of collecting measurable data, running such through packaged computer analysis, and sitting in one's office or at hectic meetings drawing conclusions from the output. Much of this social system has been carried out by men trained only in physical systems analysis such as electrical engineering. The fireman's friend statement, by example, should not mean that a study not be made, but that operational purpose models necessitate the combined efforts of the theoretician trained in the behavioral sciences and OR methodologies and the administrator, politician, or low-level public servant engaged in the day-to-day activity. Much of the research by systems-thinking organizations has instead used a canned approach they have refined as a result of weapons and communication systems analysis. Although the OR analyst does not hold that facts . . . speak for themselves, he however is not accustomed to the amount of interpolation necessary in the modelling of large open systems with such a high degree of human activity. The firms referred to above are basically guilty of a lack of imagination and humility to the degree that they have produced much research but little that is relevant for implementation. If Mr. Hamilton inferred such with his statement bent on systematizing that which is not and cannot be a system within their rubric, it is not clear. STEPHEN D. SLINGSBY Public Administration and Management Science Faculties, and Director, Politicometrics Program Ohio State University