Batch scheduling in the histopathology laboratory

A G Leeftink,M A M Verdaasdonk,P J Van Diest,I M H Vliegen,R J Boucherie,E W Hans

doi:10.1007/s10696-016-9257-3

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.

Highlights

Histopathology and anatomic pathology laboratories aim to deliver timely diagnoses to patients
Concluding, it is known that the hybrid flow shop (HFS) problem in which the tardiness is minimized in itself is a complex problem, since it is NP-hard
To the best of our knowledge, this system, the 3-stage hybrid flow shops with parallel batching (HFPB) with parallel batching in the second stage where the intermediate storage has to be kept to a minimum, has not been considered before in the literature and has never been applied in a hospital or manufacturing setting

Summary

Introduction

Histopathology and anatomic pathology laboratories aim to deliver timely diagnoses to patients. The work of Van Essen et al (2012) is the closest to our approach They developed several solution methods to minimize the interval between completion times of scheduled surgeries by optimizing their sequence. They proved this problem is strongly NP-hard for two or more operating rooms (Van Essen et al 2012) Their aim is to minimize the maximum interval, whilst we want to maximize the minimum interval. We consider the more advanced case, where the batches have to be scheduled over multiple machines This is against a cost of an increased solution space and additional decision making, which makes the problem even harder to solve. A list scheduling algorithm is a well known method to multi-machine job shop scheduling (Kim 1993) It generates fast solutions, and can be implemented in the histopathology practice. We propose a list scheduling heuristic to solve the scheduling problem

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