DYNAMIC COURSE SCHEDULING FOR COLLEGE FACULTY VIA ZERO-ONE PROGRAMMING*

This paper formulates a multi-period course scheduling problem as a zero-one programming model. Under various constraints, and for a planning horizon of several terms, the model seeks to maximize: (1) the faculty course preferences in assigning faculty members to courses, and (2) the faculty time preferences in allocating courses to time blocks, via a two-stage optimization procedure. The multi-period structure of the model, strengthened by the explicit inclusion of a wide-range of constraints designed to represent various special requirements has enabled the model to capture the many dynamic features of the course scheduling problem at the college level. As such, it can be used not only for long-range or short-range departmental planning, but also as a suitable framework toward the development of a larger, and all-inclusive course scheduling decision system. The paper begins with a brief review of several related studies and then presents a multi-period scheduling model and its extensions. Some numerical examples are used to test the model, and the authors' experience, resulting from such tests with several computer codes, is reported. The possible directions for future research are also suggested.     

A DYNAMIC PROGRAMMING MODEL FOR PERIODICAL SELECTION

This paper presents a model for choosing the periodical collection in a reading room. The model consists of two stages. In the first stage the probability distribution of contacts by users with a given periodical is developed and employed to determine the utility of the periodical. In the second stage the problem of choosing an optimum periodical collection is formulated as a capital budgeting problem and solved with the aid of dynamic programming. The implementation of this model at the University of Connecticut is described in detail.     

SCHEDULING JOBS ON PARALLEL PROCESSORS WITH DYNAMIC PROGRAMMING

The problem of scheduling jobs on M-parallel processors is one of selecting a set of jobs to be processed from a set of available jobs in order to maximize profit. This problem is examined and a dynamic programming solution is presented which decomposes it into a sequencing problem within an allocation problem. The computation required for solution is found to depend on the sequencing problem as it is affected by the waiting cost function. Various forms of the waiting cost function are considered. The solution procedure is illustrated by an example, and possible extensions of the formulation are discussed.     

ASSESSMENT OF MULTIATTRIBUTED MEASURABLE VALUE AND UTILITY FUNCTIONS VIA MATHEMATICAL PROGRAMMING*

A major restriction on the use of decision analysis in practice is the frequent difficulty of determining a decision maker's multiattribute utility function. The assessment process can be complex and tedious and generally involves: (1) identifying relevant independence conditions, (2) assessing conditional utility functions, (3) assessing scaling constants, and (4) checking for consistency. Some of the assessment and modeling complexities encountered include an assessor's inability to respond in a quantitatively meaningful and consistent way to hypothetical gambles and an analyst's problem in selecting an appropriate functional form that accurately characterizes the conditional utility assessments. A simplified procedure that mitigates these difficulties is proposed. This procedure facilitates the determination of scaling constants by obtaining (via mathematical programming) a multiattributed measurable value function which is converted to a multiattributed utility function. The methodology can be developed advantageously to produce an interactive software package for use as an assessment aid.     

MARKET POWER AND INFORMATION REVELATION IN DYNAMIC TRADING

We study a strategic model of dynamic trading where agents are asymmetrically informed over common value sources of uncertainty. There is a continuum of buyers and a finite number n of sellers. All buyers are uninformed, while at least one seller is privately informed about the true state of the world. When n = 1, full information revelation never occurs in equilibrium and the only information transmission happens in the first period. With n > 1 the outcome depends both on the structure of the sellers' information and, even more importantly, on the intensity of competition allowed by the existing trading rules. When there is intense competition (absence of clienteles), information is fully and immediately revealed to the buyers in every equilibrium for n large enough, regardless of the number of informed sellers. On the other hand, for trading arrangements characterized by less intense forms of competition (presence of clienteles), for any n we always have equilibria where information is never fully revealed. Moreover, in that case, when only one seller is informed, for many parameter configurations there are no equilibria with full information revelation, even for large n. (JEL: C72, C78, D82, D83)     

A DYNAMIC PROGRAMMING MODEL FOR OPTIMAL ALLOCATION OF DECISION TIME

Recurrent decision making by a lower-level manager can be viewed as a sequential decision process in which time and uncertainty are limiting factors. Under these conditions, the manager must determine how to best utilize his decision making time consistent with his own particular set of decision values. A dynamic programming model was devised to determine the optimal (consistent) allocation of decision time among five different types of problems for a sequence of simulated recurrent decision situations. Fifty-one lower-level managers were interviewed about their use of decision time and decision procedures. The model was validated by comparing model assumptions and results with the findings from the interviews. The model was used to determine the effects of variations in the levels of time available and uncertainty upon the optimal allocation of decision time.     

DEVELOPING MARKETING STRATEGIES AND TACTICS IN HIGHER EDUCATION THROUGH TARGET MARKET RESEARCH

This paper describes research undertaken by a large midwestern university to determine the needs of the market it serves, to ascertain the image of the university, to develop procedures for identifying potential students with a high probability of matriculating and procedures for increasing the probability of matriculation. The population studied was prospective students who submitted ACT profiles. A modified Fishbein instrument was used for measuring their image of the university. Factor analysis was used to reduce 102 ACT variables to 20. These variables plus others developed through literature search and the image study were included in the set of input variables used in multiple discriminant analysis. The resulting classification functions provided a means for identifying variables important to each individual's matriculation decision and providing them with information designed to increase his/her awareness of the attractiveness of the university.