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ALLOCATING AND SCHEDULING MOBILE DIAGNOSTIC IMAGING EQUIPMENT AMONG HOSPITALS
Author(s) -
RAJAGOPALAN S.,
HADJINICOLA GEORGE C.
Publication year - 1993
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/j.1937-5956.1993.tb00096.x
Subject(s) - renting , computer science , revenue , scheduling (production processes) , operations research , flow network , integer (computer science) , integer programming , revenue management , mathematical optimization , operations management , business , economics , finance , operating system , mathematics , algorithm , political science , law
The allocation and weekly scheduling of mobile magnetic resonance imaging (MRI) units leased to a group of hospitals that share the equipment can be a complex problem. Similar problems occur in other domains where expensive equipment or facilities such as video conference facilities, aircraft, and supercomputers are leased. The crux of the problem was determining the number of days and which days of the week various types of equipment types should be leased to hospitals, so as to maximize the rental revenues and satisfy client preferences for days of the week and equipment types. We found rental revenues were a decreasing function of the number of days allocated to a hospital. We considered two sub‐problems linked by a set of variables to model the problem. We show that one of these subproblems is a minimum cost network flow problem and the other is an integer multi‐commodity transportation problem. We developed a procedure for solving the latter problem by exploiting earlier results for specialized networks. We conducted a computational study to evaluate the performance of this procedure and showed that it generally provides near‐optimal integer solutions. We describe the development and implementation of a spreadsheet‐based decision support system based on this model. This system was successfully implemented by a small firm with no expertise or prior experience using models.