The paper presents a hierarchical framework for production control of hospitals which deals with the balance between service and eae ciency, at all levels of planning and control. The framework is based on an analysis of the design requirements for hospital production control systems. These design requirements are translated into the control functions at diå erent levels of planning required for hospital production control. The framework consists of ® ve levels of planning and control: patient planning and control, patient group planning and control, resources planning and control, patient volumes planning and control and strategic planning, though this last level does not make part of production control as such. Each of the levels of the framework is further elaborated in terms of the decisions made regarding patient¯ows and resources, and the co-ordination of the diå erent planning levels. Implications of the framework are discussed by describing some points where current practice deviates from assumptions made in our approach. Recommendations for future research and development of the planning framework are formulated.

We simulate the performance of a simple production system in which due‐dates are set internally. We investigate priority rules aimed at minimizing tardiness, emphasizing a “modified duedate rule” which functions effectively in conjunction with internally‐set deadlines and which adapts to both tight and loose conditions in the due‐dates. This rule is simple and logical, and shows considerable promise for application in complex production systems.

No abstract

No abstract

Gives an overview of quantitative model‐based research in operations management, focusing on research methodology. Distinguishes between empirical and axiomatic research, and furthermore between descriptive and normative research. Presents guidelines for doing quantitative model‐based research in operations management. In constructing arguments, builds on learnings from operations research and operations management research from the past decades and on research from a selected number of other academic disciplines. Concludes that the methodology of quantitative model‐driven empirical research offers a great opportunity for operations management researchers to further advance theory.