Charles H. Smith

Multiple Criteria Decision Support Software

We present an overview of the current state of multiple criteria decision-making (MCDM) decision support software. Many approaches have been proposed in the literature to solve multiple criteria decision-making problems, and there is an abundance of software that implements these approaches. Much of the software is still quasi-experimental, developed by academic researchers to test specific algorithms or to solve a specific problem on an ad hoc basis.

Quality function deployment and decision analysis :A R&D case study

This article presents and compares two techniques for making multiobjective product design decisions: quality function deployment (QFD) and decision analysis (DA). A research and development (RD however, for multiobjective decisions, decision analysis provides some significant advantages for generating alternatives and performing analysis. We recommend combining the best features of QFD and DA for R&D decision making.

Evaluating Multiple‐Attribute Decision Support Systems

Differences in the perceived usefulness of five multiple-attribute decision support systems versus a basic spreadsheet are empirically assessed. Twenty-four college faculty members participated in a repeated measures experiment in which they used decision support software for personal computers to analyse semistructured multiple-attribute problems. The software packages employed were Criterium, Expert Choice, Logical Decision, VIMDA, VISA and the spreadsheet package Quattro Pro. A questionnaire was developed to assess user-friendliness of the software, confidence in the procedure implemented by the software, and users’ confidence in their results when employing the decision aids. Time to reach a decision was also measured. Significant differences were fund in the overall way users ranked these aids, in the perceived user-friendliness and the confidence in procedure among aids, and in the time it took to arrive at a result. Comments from users are also reported.

Optimal inventories for repairable redundant systems with aging components

Abstract Complex systems that are required to perform very reliably are often designed to be “fault-tolerant,” so that they can function even though some component parts have failed. Often fault-tolerance is achieved through redundancy, involving the use of extra components. One prevalent redundant component configuration is the m-out-of-n system, where at least m of n identical and independent components must function for the system to function adequately. Often machines containing m-out-of-n systems are scheduled for periodic overhauls, during which all failed components are replaced, in order to renew the machines reliability. Periodic overhauls are appropriate when repair of component failures as they occur is impossible or very costly. This will often be the case for machines which are sent on “missions” during which they are unavailable for repair. Examples of such machines include computerized control systems on space vehicles, military and commercial aircraft, and submarines. An interesting inventory problem arises when periodic overhauls are scheduled. How many spare parts should be stocked at the maintenance center in order to meet demands? Complex electronic equipment is rarely scrapped when it fails. Instead, it is sent to a repair shop, from which it eventually returns to the maintenance center to be used as a spare. A Markov model of spares availability at such a maintenance center is developed in this article. Steady-state probabilities are used to determine the initial spares inventory that minimizes total shortage cost and inventory holding cost. The optimal initial spares inventory will depend upon many factors, including the values of m and n, component failure rate, repair rate, time between overhauls, and the shortage and holding costs. In a recent paper, Lawrence and Schaefer [4] determined the optimal maintenance center inventories for fault-tolerant repairable systems. They found optimal maintenance center inventories for machines containing several sets of redundant systems under a budget constraint on total inventory investment. This article extends that work in several important ways. First, we relax the assumption that the parts have constant failure rates. In this model, component failure rates increase as the parts age. Second, we determine the optimal preventive maintenance policy, calculating the optimal age at which a part should be replaced even if it has not failed because the probability of subsequent failure has become unacceptably high. Third, we relax the earlier assumption that component repair times are independent, identically distributed random variables. In this article we allow congestion to develop at the repair shop, making repair times longer when there are many items requiring repair. Fourth, we introduce a more efficient solution method, marginal analysis, as an alternative to dynamic programming, which was used in the earlier paper. Fifth, we modify the model in order to deal with an alternative objective of maximizing the job-completion rate. In this article, the notation and assumptions of the earlier model are reviewed. The requisite changes in the model development and solution in order to extend the model are described. Several illustrative examples are included.

Sensitivity of a Bayesian inference to prior assumptions

The sensitivity of-a Bayesian inference to prior assumptions is examined by Monte Carlo simulation for the beta-binomial conjugate family of distributions. Results for the effect on a Bayesian probability interval of the binomial parameter indicate that the Bayesian inference is for the most part quite sensitive to misspecification of the prior distribution. The magnitude of the sensitivity depends primarily on the difference of assigned means and variances from the respective means and variances of the actually-sampled prior distributions. The effect of a disparity in form between the assigned prior and actually-sampled distributions was less important for the cases tested.

Bicriteria decision model for farm management of the Colorado potato beetle: development and validation

This paper describes the development and testing of a deterministic simulation model for pest and resistance management of the Colorado potato beetle (CPB). Historically, CPB have developed resistance to new pesticides quickly. A potato farmers pesticide application decisions trade off current crop yields and resistance development. The simulation incorporates major aspects of beetle life processes, pesticide resistance, and potato growth in order to predict the consequences of a pesticide application strategy over a ten-year planning horizon.

On Designing Health Care Plans and Systems from the Multiple Criteria Decision Making (MCDM) Perspective

Many studies of medical decision making focus on the cost effectiveness of taking action A for disease X. These focused evaluations do not consider that the recommended actions must compete in aggregate for available resources. Relatively fewer studies have considered the aggregate decision making required for the rational design of health care. The operations research perspective and, in particular, the MCDM perspective can aid our understanding of the design of health care plans from the insurer’s and payer’s perspectives. This paper considers issues in defining health benefits plans and suggests how MCDM methodology can assist in structuring the appropriate decision context. Issues to be addressed in designing health plans include global budgets, new technology, system objectives, the hierarchy of decision makers in medical care, and data requirements. While MCDM methods may be useful in health policy making, considering these complex applications can also provide a focus for further conceptual and methodological developments in MCDM.

The College Selection Process from a Multi-Criteria Decision Analysis Perspective

Given the large number of colleges and universities in the USA alone, and the diversity among these institutions, the problem faced by an American high school senior to make the “right” choice is not trivial. Decision criteria may include the geographic location of the institution, its perceived quality and prestige, the perceived feasibility of attending that school (including the likelihood of getting accepted, and the expected financial burden of attending), the expected quality of life at the institution, etc. Typically, the decision-making procedure consists of at least three stages: (1) selecting a manageable number of colleges for further investigation; (2) selecting a subset for formai application, after information on the institutions has been obtained; and (3) making the final decision after receiving acceptances as well as information on financial support from some of the institutions to which applications were submitted. The suitability of multi-criteria methodology in any of these stage and the general obstacles faced are investigated.

The decision to add a second system producer

Typically in the United States only one contractor (the developer or first producer) makes a weapon system because it is too expensive or for some other reason infeasible to develop a second source. Congress and higher headquarters are constantly challenging the Armed Services to add other sources who can competitively make their systems. This paper describes a method of analyzing this complex problem which often has very large monetary and other significant impacts. Procedures and a case study are presented which consider cost and other criteria which might be used to make the second source decision.