Budgeting costs of nursing in a hospital
Article Abstract:
Issues in building decision support models for budgeting nursing workforce requirements in a hospital are examined. A linear programming based model for assessing needs for permanent staff, overtime pay and contracting temporary help by medical service, nursing skill class and time period is extended to include demand uncertainty. A family of eight models is introduced as possible alternatives for building the decision support system. Results indicate that the time-varying nature of demand does not play a prominent role in affecting budget estimates; ignoring demand uncertainty, however, results in the underestimation of budget needs and induces added costs to the system. A simple formula using a single-period demand estimate gives excellent approximations to budget estimates.
Publication Name: Management Science
Subject: Business, general
ISSN: 0025-1909
Year: 1985
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The tolerance approach to sensitivity analysis in linear programming
Article Abstract:
Sensitivity analysis, which is becoming increasingly important in coping with uncertainties in linear programming models, makes it difficult for a decision-maker to deal with perturbations in more than one coefficient or term at a time. The tolerance approach addresses this difficulty, considering simultaneous and independent changes in the objective function coefficients and in the right-hand side terms. A maximum tolerance percentage is yielded such that, if selected coefficients or terms are accurate to within that percentage of their estimated values, the same basis is optimal. The tolerance approach yields the result that the same solution is optimal if the objective function coefficients are accurate to within the maximum tolerance percentage of their specified values.
Publication Name: Management Science
Subject: Business, general
ISSN: 0025-1909
Year: 1985
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IDEA and AR-IDEA: models for dealing with imprecise data in IDEA
Article Abstract:
A study was conducted to explore the issue of imprecise data in data envelopment analysis, which is a method used to assess the relative efficiency of decision making variables which employ multiple inputs to produce multiple outputs. The goal of the study was to provide a flexible approach to treating mixtures involving both exact and imprecise data by integrating previous techniques of handling the various aspects of imprecise data in data envelopment analysis assessments of performance. This proposed method called imprecise data envelopment analysis transform mixtures of both precisely known and imprecise data into common linear programming forms. The model is then extended to include assurance region and similar approaches.
Publication Name: Management Science
Subject: Business, general
ISSN: 0025-1909
Year: 1999
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