Computer-assisted planning for regional health care programs

In designing programs to improve regional health care, it is often necessary to synthesize hierarchies. Notwithstanding the fact that the health care milieu is highly complex, this process is frequently dealt with intuitively resulting in fragmented and redundant programs. It is proposed that formal procedures which relieve the individual of much of the complexity in developing programs and arranging their structure can contribute to improve understanding of the system and effectiveness of program design.This paper presents a rational approach to computer-assisted program design utilizing binary matrices and elementary Boolean operations.     

Mathematical programming applications in educational planning

The specific aim of this article is to examine recent developments in the applications of mathematical programming techniques to problems encountered in educational planning. Applications are given for selected problems at national, state, regional and local levels of planning and the implications of this work for educational research methodology is noted. Special attention is given to applications at the microanalytic or school district level, since most applications in this domain are of recent origin and have been developed in disciplines other than education, e.g. industrial engineering, econometrics, public administration, business and operations research.Applications are discussed in light of their relationship to theoretical and empirical research on educational production functions. The final section contains some directions and implications for future research which are discussed in terms of recent developments in socio-economic and public sector planning and the emerging major research needs in educational policy planning.     

Multi-objective programming models: New ways in regional decision-making

The article focuses on the use of multiple objective functions in regional decision-making. After a survey of various multi-objective programming models an interactive procedure for determining a best-compromise solution is discussed. The method is illustrated by means of an empirical application in the field of regional industrialization policy.     

Test of a technique for planning medical procedures in preventive health examination programs

This paper reports on a retrospective test of a linear programming (LP) technique for planning of medical procedures in preventive health examination (PHE) programs. The technique itself has been published earlier; hence only a brief review is presented here. The test was, in essence, a comparison between a PHE program run along conventional lines (all procedures to all patients) and a PHE program guided by the LP technique. The results showed that the LP technique has several advantages over the conventional mode of operation.     

A control rule for recruitment planning in engineering consultancy

The most valuable asset of a professional service firm is its people. Owing to the high labor turnover, staffing decision is very critical in its operations. We take engineering consultancy as a professional service and emphasize the importance of developing knowledge stock of skilled consultants in a planned manner for efficient productivity management. Our focus is management of knowledge-mix, which is the mix of consultants at different productivity levels. Our model is designed to determine the steady-state number of consultant-mix to meet demand at a desired service level. This is done through the use of control theory and chance constrained programming.      

A literature review of decision-making models and approaches for partner selection in agile supply chains

The paper reviews the literature on supply partner decision-making published between 2001 and 2011, a period that has seen a significant increase in work published in this field. The progress made in developing new models and methods that can be applied to this task is assessed in the context of the previous literature. Particular attention is given to those methods that are especially relevant for use in agile supply chains. The paper uses a classification framework that enables models intended for similar purposes to be compared and tracked over time. It is also used to identify a number of gaps in the literature. The findings highlight an on-going need to develop methods that are able to meet the combination of qualitative and quantitative objectives that are typically found in partner selection problems in practice.     

Welfare gains from optimization in dynamic planning models

This paper compares the results of two multisectoral models used to plan the dynamic consistency of sectoral investment. Solution of an optimizing model forecasts a frontier of the economy's future choice set. This frontier represents a ten year welfare gain only 2 or 3 percent greater than any investment program simulated by a dynamic Leontief system. The paper explains what efficient behavior accounts for the better performance of the optimizing model. Developing dynamic programming models is costly in terms of data, computational complexity, man-machine interaction, and solution interpretation. Therefore, it is recommended that the lessons derived by working with the dynamic LP be applied to improve the planner's control of the less expensive input/output simulation model.Final Draft for Economics of Planning-April, 1975H. G. Bergendorff is a staff economist at the Development Research Center of the World Bank, P. B. Clark is Staff Director, Center for Energy Policy (Boston, Mass., U.S.A.) and L. Taylor is Professor of Nutrition Economics at Massachusetts Institute of Technology. The project has been sponsored by the Development Research Center of the World Bank. The authors are grateful for the collaboration of Alejandro Foxley and Paul Ide in early versions of this study and for the assistance of Lucy Cardwell, Jon Eaton, Ruth Sheshinski, and Mieko Nishimizu during the computation phase. Any errors and the interpretations expressed are the responsibility of the authors alone.     

Quality control for structural credit risk models

Over the last four decades, a large number of structural models have been developed to estimate and price credit risk. The focus of the paper is on a neglected issue pertaining to fundamental shifts in the structural parameters governing default. We propose formal quality control procedures that allow risk managers to monitor fundamental shifts in the structural parameters of credit risk models. The procedures are sequential — hence apply in real time. The basic ingredients are the key processes used in credit risk analysis, such as most prominently the Merton distance to default process as well as financial returns. Moreover, while we propose different monitoring processes, we also show that one particular process is optimal in terms of minimal detection time of a break in the drift process and relates to the Radon–Nikodym derivative for a change of measure.     

Qualitative discrete multiple criteria choice models in regional planning

The paper provides a concise review of evaluation methods for decision problems in which qualitative data is involved. Special attention is paid to a new qualitative evaluation method: the regime method. An application of the regime method is given in the field of multiregional conflict analysis.     

A technique using analytical hierarchy process in multiobjective planning models

Social and economic planning usually includes the need to consider and reconcile multiple, conflicting objectives. A number of multiple objective planning models have been proposed, using goal programming as well as other multiple objective techniques. Goal programming provides a useful tool which quickly provides a solution matching the set of ranked objective targets provided, but does not provide much exploratory analysis of planning problems. Other techniques have been used which provide generation of new alternatives for consideration, but these other techniques often are limited to original model extreme points. This paper presents an alternative means of generating planning alternatives, utilizing Analytic Hierarchy Process as a means of obtaining a good starting point, and imposition of objective attainment levels through constraints. The proposed process provides a means of decision maker learning, easily allows adjustment of objective attainment levels and is not limited to the original model extreme points.     

Application of optimal control in man power planning

Many techniques are met in the literature (see for instance Bartholomew and Forbes (Statistical Techniques for Manpower Planning. wiley, New York 1979); Gunz (Organiz. Stud. 9(4), 529–554, 1988); Becker and Huselid (Human Resour. Manage. 38, 287–301, 1999); Wagner et al. (J. Manage. Med. 14(5/6), 383–405, 2000); Harris and Ogbonna (J. Business Res. 51, 157–166, 2001); Rogg et al. (J. Manage. 27, 431–449, 2001), among others), for planning the manpower resources. However, we haven’t seen in the literature an empirical study regarding the proper application of optimal control, which considered to be the most efficient method for multi-objective programming. With this in mind, we analyse in this paper the way of applying optimal control for manpower planning. For this purpose, and in order to facilitate the presentation, we first adopted a comparatively simple dynamic system (plant), with analytical presentation of stocks and flows. Next we proceed to the formulation of an optimal control problem, aiming to achieve in the most satisfactory way some preassigned targets. These targets mainly refer to a desirable trajectory of the plant stocks over time, in order to fully satisfy the needs for human resources over the planning horizon. Finally we present a method of solution of the formulated control problem which is based on the use of the generalized inverse Lazaridis (Qual. Quan. 120, 297–306, 1986). We believe that it is very important for successful management, that the policy makers have to know the effect of their polices and to determine the optimal path of the state variables (i.e. the ones describing the system) before the realization of the plan, so as to be able to reform their strategies, reallocate the resources and arrange the infrastructure accordingly, if all these are necessary, as it can be depicted from the optimal control solution.     

Capacity planning techniques for manufacturing control systems: Information requirements and operational features

Substantial interest has been focused on techniques that determine the amount and timing of work center capacity to satisfy the master production schedule. Although several techniques have been developed for preparing work center capacity plans, very little analysis of the operational features and the application of these techniques has been reported. Four techniques, capacity planning using overall factors (CPOF), capacity bills, resource profiles, and capacity requirements planning (CRP), vary substantially in their complexity and the level of detail required to plan work center capacities. All four procedures require the use of the master production schedule to develop a capacity plan, but some have much more demanding data requirements. Important differences are the means by which capacity requirements are estimated by the techniques and the manner in which the bill of material, manufacturing lead time and inventory status information are incorporated into the capacity plan. The purpose of this paper is to describe the operational features of the four capacity planning techniques, the underlying data base requirements, and some key managerial issues in choosing among these techniques.     

Operational models for epidemics of problematic drug use: the Mover-Stayer approach to heterogeneity

A modified version of a susceptibles-infectives-removed (S-I-R) model for the HIV/AIDS epidemic, proposed at the beginning of the 1990s and recently generalized, is presented to mirror the epidemics of problematic drug use. The generalized model, which belongs to the susceptibles-infectives-susceptibles (S-I-S) class of epidemic models, can be used both to estimate interesting epidemic macro-parameters and to make scenario analyses. The model is a compartmental Mover-Stayer-type structure (Soc. Methods Res. 11 (1983) 345; Bull. Narcotics LIII (1-2) (2001) 39; Biometrics 55(4) (1999) 1252; Math. Biosci. 107 (1991) 521) and allows taking into account possible heterogeneous behaviours of the susceptibles. Such models, in fact, consider the susceptible population as subdivided into two main groups: the group of stayers, that is, the group of individuals who are considered not at risk of “infection”, and the group of movers (possibly divided into sub-groups with different risk behaviour) who are at risk of infection. Due to the interactions between infectious individuals and the susceptibles, some of these may pass to the drug user compartments and begin a “drug user career”. The model is presented and studied from a qualitative point of view using a Markov hybrid approximation. Some qualitative evaluation of the possible impact of interventions directed both towards susceptibles (primary prevention) and towards users (secondary prevention and/or law enforcement) are presented. Specific “what if” scenario analyses are obtained by simulation. Possible future developments are outlined.     

Classroom exercises in applying the delphi method for decision-making

This paper describes the results of a series of graduate-engineering classroom exercises in applying the Delphi procedures for formulating group judgments. The exercises were conducted in two phases. The first phase of the exercises was essentially a demonstration of the basic principles of the Delphi method using almanac, factual-type questions for which the answers are known. The second phase was a group exercise in formulating opinions about the educational goals of the United States for the 1970–1980 decade, using questions for which the true answers are not known and, therefore, value-judgments must be made.     

Linking performance drivers in production planning and inventory control to process choice

This study investigates how process choice relates to production planning and inventory control decisions. We empirically examine the validity of deductively derived patterns about these types of decisions. More importantly, we look for normative insights by exploring how production planning and inventory control decisions affect operations performance. Our findings show that production line and continuous flow plants use more of a level production strategy, and carry less raw material and work-in-process inventory. The performance drivers for these plants, through which the operations function excels, are effective utilization of equipment, reduced finished goods inventory, and reduced setup down time. To gain forward demand visibility and batching economies, job and batch shops rely much more on backlogs in their planning process. These plants use more of a production chase strategy and position inventory lower in the bills of materials. Four performance drivers for top-performing job and batch shops are to find ways that better anticipate customers' orders, have a more responsive chase strategy, carry less raw material or purchased inventory, and shorten production planning horizon, partly through less reliance on backlogs. It is intriguing that top-performing plants not only do the expected things, given their choice of process, but also excel in selected dimensions—some of which fit the profile normally associated with a different process choice. To monitor and continuously improve operations, evaluation ‘scorecards’ should pay particular attention to performance drivers, which change depending on the plant's process choice.