Solution methods for the tray optimization problem

Journal Article (2018)
Author(s)

Twan Dollevoet ( Erasmus Universiteit Rotterdam)

J.T. van Essen (TU Delft - Discrete Mathematics and Optimization)

Kristiaan M. Glorie ( Erasmus Universiteit Rotterdam)

Research Group
Discrete Mathematics and Optimization
Copyright
© 2018 Twan Dollevoet, J.T. van Essen, Kristiaan M. Glorie
DOI related publication
https://doi.org/10.1016/j.ejor.2018.05.051
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 Twan Dollevoet, J.T. van Essen, Kristiaan M. Glorie
Research Group
Discrete Mathematics and Optimization
Issue number
3
Volume number
271
Pages (from-to)
1070-1084
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Abstract

In order to perform medical surgeries, hospitals keep large inventories of surgical instruments. These instruments need to be sterilized before each surgery. Typically the instruments are kept in trays. Multiple trays may be required for a single surgery, while a single tray may contain instruments that are required for multiple surgical procedures. The tray optimization problem (TOP) consists of three main decisions: (i) the assignment of instruments to trays, (ii) the assignment of trays to surgeries, and (iii) the number of trays to keep in inventory. The TOP decisions have to be made such that total operating costs are minimized and such that for every surgery sufficient instruments are available. This paper presents and evaluates several exact and heuristic solution methods for the TOP. We compare solution methods on computation time and solution quality. Moreover, we conduct simulations to evaluate the performance of the solutions in the long run. The novel methods that are provided are the first methods that are capable of solving instances of realistic size. The most promising method consists of a highly scalable advanced greedy algorithm. Our results indicate that the outcomes of this method are, on average, very close to the outcomes of the other methods investigated, while it may be easily applied by (large) hospitals. The findings are robust with respect to fluctuations in long term OR schedules.

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