A Comparison of Due Date Setting,Resource Assignment,and Job Preemption Heuristics for the Multiproject Scheduling Problem*

This research examines a heuristic, rule-based approach for setting due dates in a multiproject, multijob, or assembly shop. Due date estimation is a challenging problem because the operating environment is capacitated, involves the allocation of multiple resources, and allows for the preemption of resources from one project or job to another. The dynamic, continuous arrival of new jobs or orders frequently results in the preemption of resources through the application of managerially determined priority policies. These preemption policies have a significant impact on the ultimate completion time of a job or a project. A three-factor, full-factorial computer simulation experiment is used to assess the relative effectiveness of combinations of four due date setting heuristics, five resource assignment heuristics, and three resource preemption heuristics. Recommendations are made for the selection of due date and resource assignment heuristic combinations under the three preemption policies examined.     

A Model for Determining Retail Product Category Assortment and Shelf Space Allocation

We develop a category management model to aid retailers in the space constrained decisions of which products to stock (assortment) and how much shelf space to allocate to those products. The model is formulated as a constrained optimization problem with two basic decision variables: assortment and allocation of space to the items in the assortment. The non-linearities in the objective function and the zero-one decision variables disallow a closed form solution. We develop a heuristic solution procedure based on simulated annealing and test it on a problem with a known optimum. We also apply the technique to a larger problem without a known optimum. Finally, the solution found by simulated annealing is compared against a solution produced using a shelf allocation rule based on share of sales.     

INDUSTRY RESOURCE ALLOCATION TO BASIC RESEARCH UNDER NORMALLY DISTRIBUTED BENEFITS*

The failure of a free market system to attain socially optimal allocation of resources to research and development (R&D) is a generally recognized problem. However, we are just beginning to understand the types of R&D activities that receive relatively serious underinvestment from specific types of industries and the types of governmental intervention strategies that are likely to be effective and efficient in the correction of that underinvestment. Recently, Joglekar and Hamburg [16] [17] sought answers to these types of questions using models of the resource allocation behavior of firms considering investment in basic research related to their industry. It was assumed that the firms' benefits were exponentially distributed. In the present article, such benefits are assumed to be normally distributed, and an attempt is made to verify the earlier conclusions and policy implications of [16] and [17]. The results are similar for these two substantially different types of distribution, but the earlier conclusions and policy implications are clarified, qualified, and extended.     

Project Portfolio Selection: A Newsvendor Approach

We consider the problem of balancing the penalties associated with budgetary slack (being underbudget) and cost overruns in the project portfolio selection problem by addressing randomness in project costs and making individual project budgets decision variables. Setting the budget for a single project is shown to be analogous to the newsvendor problem. For related versions of the project portfolio selection problem we provide optimal and heuristic procedures. Numerical experiments are used to test the procedures and provide managerial guidelines. We show project budgets should be set so that each project in the portfolio has the same probability of running over budget, it is better to have a larger number of projects with less than ideal funding compared to a smaller number of projects with ideal funding, and substantial opportunities to select more projects with a higher expected profit are available if an aggregate portfolio budget is used.     

MANAGEMENT OF COLLEGE STUDENT RECRUITING ACTIVITIES USING GOAL PROGRAMMING*

A shrinking pool of potential students, due to a declining birthrate as well as uncertain economic times, is creating the need for more effective recruiting of college students. One approach using a goal programming model has been developed and is currently being used to manage recruitment activities in a small four-year college in Nebraska. The model identifies both the type and number of activities that must be completed each quarter in order to reach an enrollment goal for a given year. Factors such as budget, time, manpower, and marketing strategies are highlighted in the model. The results of the goal programming model encouraged more field activities in the first two quarters with emphasis on new-candidate identification. The third-quarter recruiting strategy is more balanced while the fourth-quarter emphasis is placed on follow-up activities that occur chiefly in the office. Use of this model will enable recruiters to meet enrollments while managing recruiting resources and activities in order to remain within the recruiting budget.     

ONE APPROACH TO REALLOCATING INSTRUCTIONAL RESOURCES: THE HOUSTON MODEL

In this paper a model for deciding which instructional departments should receive and surrender new or reallocatable resources is developed. It combines a two-dimensional assessment of resource needs based on instructional characteristics and the academic priority of the department to arrive at resource allocation priorities. Unusual features of the model are its reliance on judgments of departmental centrality to institutional mission as the discriminating variable in ascribing academic priorities and the careful avoidance of “halo effects” through independent assessment of resource needs and academic priorities. The model is sensitive enough to discriminate between competing claims for scarce resources while retaining a simplicity of logic that makes its mechanics understandable to those with little grounding in the decision sciences.     

少地址线实现大容量RAM扩展及其查找技术

介绍了采用少地址线实现大容量ＲＡＭ 数据存储器的扩展系统。具体设计了该系统的硬件组成线路、软件程序及其分页查找方法。     

MODELING LEARNING CURVE AND LEARNING COMPLEMENTARITY FOR RESOURCE ALLOCATION AND PRODUCTION SCHEDULING*

Research on learning effects in mathematical programming models for optimum resource allocation has called attention to the difficulty in solving such models in their original nonlinear form. In this paper, systematically varying sizes of linear segments are designed to approximate productivity changes along the learning curve, and a single separable linear programming model is developed. With production complementarity and learning transmission between products, a more realistic resource allocation and production scheduling problem emerges. Two cases of learning transmissions are considered, and the model design process, which defines a decision problem that can be solved by a simplex algorithm, is demonstrated.