Batch scheduling in the histopathology laboratory

A.G. Leeftink* (Corresponding Author), R.J. Boucherie, E.W. Hans, M.A.M. Verdaasdonk, I.H.M. Vliegen, P.J van Diest

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
49 Downloads (Pure)

Abstract

Histopathology laboratories aim to deliver high quality diagnoses based on patient tissue samples. Timely and high quality care are essential for delivering high quality diagnoses, for example in cancer diagnostics. However, challenges exist regarding employee workload and tardiness of results, which both impact the diagnostic quality. In this paper the histopathology operations are studied, where tissue processors are modeled as batch processing machines. We develop a new 2-phased decomposition approach to solve this NP-hard problem, aiming to improve the spread of workload and to reduce the tardiness. The approach embeds ingredients from various planning and scheduling problems. First, the batching problem is considered, in which batch completion times are equally divided over the day using a Mixed Integer Linear Program. This reduces the peaks of physical work available in the laboratory. Second, the remaining processes are scheduled to minimize the tardiness of orders using a list scheduling algorithm. Both theoretical as well as historical data were used to assess the performance of the method. Results show that using this decomposition method, the peaks in histopathology workload in UMC Utrecht, a large university medical center in The Netherlands, may be reduced with up to 50 % by better spreading the workload over the day. Furthermore, turnaround times are reduced with up to 20 % compared to current practices. This approach is currently being implemented in the aforementioned hospital.
Original languageEnglish
Pages (from-to)171-197
Number of pages27
JournalFlexible services and manufacturing journal
Volume30
Issue number1-2
DOIs
Publication statusPublished - 1 Jun 2018

Keywords

  • UT-Hybrid-D
  • Healthcare
  • Hybrid flow shop
  • Parallel batching
  • Pathology
  • List scheduling
  • Workload

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