The problem addressed in this paper is the scheduling of activities in a project to maximize net present value given cash inflows, outflows, and resource restrictions. Although optimization procedures exist for small problems of this type, they generally cannot solve large-scale problems and may not be widely available. Six heuristic scheduling rules are compared on 80 test problems. The test problems include large-scale engineering construction projects as well as 50 resource constrained problems from the literature.
This paper presents an empirical test of the major hypothesis of the currently-accepted bidding models in two regions of differing tract value uncertainty. The most significant result is an empirical verification that the more bidders that bid on a particular parcel in the face of great uncertainty, the lower the bid levels of individual firms that participate given all else is constant. No such relationship was observed, however, in the sale where there was previous bidding experience and prior ownership of closely related tracks. Prior models have not been able to identify the inverse relationship because of a bias that occurs when only positive bids are employed in these models and the use of data where previous experience reduces uncertainty. In the analysis of the competitive bidding situation, we use a two-stage procedure which adjusts for this “selection bias” by allowing for the incorporation of information from the dichotomous bid/no-bid decision in the modeling of bid level.
In a previous paper Yamazaki et al. (Yamazaki, G., T. Kawashima, H. Sakasegawa. 1983. Reversibility of tandem blocking queueing systems. Kogakuin University, Tokyo, Japan.) prove that a certain tandem blocking queueing system and its reversed counterpart have the same capacity (throughput). The purpose of this note is to offer an alternative viewpoint based on a concept of duality. This leads to a more intuitive methodology and provides additional insight into the original and reversed systems.
This paper presents multiperiod, multiple objective goal programming as an alternative to the more conventional hedge ratio approaches to financial futures hedging. The described model offers the potential benefits of: (i) simultaneous achievement of multiple hedge-related objectives (i.e., minimization of transactions and margin opportunity costs; regulation of cash flow, and maximization of profits accruing from both the cash and futures positions); and (ii) periodic modification and updating of the futures position, as suggested by actual, observed prices and interest rates, throughout the hedging period. To assess its overall effectiveness, the model was applied to develop appropriate hedging strategies for three separate time periods, each representing a unique interest rate trend (i.e., upward movement, downward movement, and no change). Since real-world implementation of the model requires the use of forecast data, independent forecasts of both cash and futures prices were generated via (1) moving average, (2) exponential smoothing, and (3) random walk techniques. In progressing through each of the 13-week time periods, all forecasts were updated with the previous weeks' actual price data. Revised forecasts and actual price data were then incorporated into the model constraints on a weekly schedule. This approach, in essence, provided weekly futures activity recommendations based on the most recent price and interest rate developments observed at any point during the hedging period. The realized gains or losses were then compared to the previously-derived “perfect foresight” model results and to the traditional hedge ratio results. This allowed for evaluation of the model's effectiveness under varying forecast methods and varying interest rate trends. The results show that the goal programming model outperformed other strategies in most cases.
In final-offer arbitration the two parties to a dispute submit final offers to an arbitrator. The arbitrator then chooses as the binding solution that offer which is closest to his own view of the appropriate outcome. Because the disputants are imperfectly informed about the arbitrator's preferences, final-offer arbitration can be modeled as a game of imperfect information. Interesting questions arise concerning the nature of the optimal strategies and how they are affected by different characteristics of the arbitrator and the disputants. We provide conditions for an equilibrium to exist in a final-offer arbitration game when there are k issues, the probability function is not specified and the disputants are either risk averse or risk neutral. Furthermore, the players may have differing beliefs about the arbitrator's probability function. We demonstrate that increased risk aversion by one of the parties will result in both players choosing positions farther away from the more risk averse party. We also discover the affect of bias (as well as the effect of increased sensitivity) by the arbitrator on the positions taken by the players.
In order to avoid the task of assessing a complicated likelihood function, Morris uses an axiomatic approach to develop a multiplicative rule for aggregating a decision maker's and an expert's probabilities. An essential shortcoming of the multiplicative rule is that it does not allow the decision maker to model his beliefs about the dependence between his assessment and the expert's. The root of the problem lies in the fact that the decision maker must calibrate the expert's information. When the calibration is done properly, the decision maker is forced to tackle the task which Morris proposes to avoid.
Researchers in all academic disciplines benefit from an understanding of the intellectual development of their field. This understanding is essential for conducting studies which build systematically on prior research. The purpose of this study is to document the intellectual development of the ideas represented by published research in Management Information Systems (MIS) based on an author co-citation analysis. The resulting mapping is intended to serve as a benchmark for future assessments of MIS as a field as well as a means for documenting the emergence of new research specialties. The study sought to identify (1) the subfields which constitute MIS research, (2) the reference disciplines of these subfields, (3) the diffusion of the ideas represented by these subfields to other disciplines, and (4) which of these subfields represent active areas of current MIS research. Nine invisible colleges, or informal clusters of research were uncovered. These nine empirically defined conceptual groupings collectively define the intellectual foundations of MIS as well as the forces currently shaping MIS research. Four of the clusters represent early MIS research themes which are still popular, based on subsequent citation patterns. Despite the centrality of the concept of the organization to widely-accepted definitions of MIS, the results suggest that MIS research is not well-grounded in organization theory nor have MIS research results been widely diffused in the organizational literature. Suggestions for developing a better link between MIS and organizational theory are presented based on the concept of organizational effectiveness.
This paper answers the question, “Why do organizations process information?” Uncertainty and equivocality are defined as two forces that influence information processing in organizations. Organization structure and internal systems determine both the amount and richness of information provided to managers. Models are proposed that show how organizations can be designed to meet the information needs of technology, interdepartmental relations, and the environment. One implication for managers is that a major problem is lack of clarity, not lack of data. The models indicate how organizations can be designed to provide information mechanisms to both reduce uncertainty and resolve equivocality.
In multi-echelon repairable inventory systems with high set-up cost for order and/or high demand rates, the use of batch ordering may be more cost-effective than the common (S − 1, S) ordering policy. This paper addresses the issue of determining the optimal order batch size and stocking levels at the stocking locations in such a system. A power approximation is used to estimate the total system stock and backorder levels from which the optimal batch size can be readily determined. A search routine involving “one-pass” searches are then followed to obtain the stocking levels at the depot and the local sites of the system. This procedure has been evaluated using 900 test cases and has been found to be very effective. The power approximation approach also results in a simple analytical relationship to test whether or not (S − 1, S) is an optimal ordering policy for repairable items in a multi-echelon environment.
In his paper, “The Managerial Economics of Civil Litigation,” Levy (Levy, F. K. 1985. The managerial economics of civil litigation. Management Sci. 31 (March) 323–342.) develops a model to determine the optimal expenditures and maximal budgets for a plaintiff and a defendant in a civil suit. He uses a learning process to relate each litigant's probability of success to his level of expenditure. The specific learning process used is a difference equation where the rate of enhancement of probability is a constant fraction of the maximum possible gain. Levy's analysis would have been simpler if he had formulated that learning process as a differential equation.