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Open Access Research article

On the improvement of blood sample collection at clinical laboratories

Alex Grasas12*, Helena Ramalhinho12, Luciana S Pessoa3, Mauricio GC Resende4, Imma Caballé5 and Nuria Barba5

Author Affiliations

1 Department of Economics and Business, Universitat Pompeu Fabra, Barcelona, Spain

2 Barcelona GSE, Barcelona, Spain

3 IN3 Universitat Oberta de Catalunya, c/ Roc Boronat, 117, 08018 Barcelona, Spain

4 AT&T Labs Research, Florham Park, NJ, USA

5 Catlab, Barcelona, Spain

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BMC Health Services Research 2014, 14:12  doi:10.1186/1472-6963-14-12

Published: 9 January 2014

Abstract

Background

Blood samples are usually collected daily from different collection points, such hospitals and health centers, and transported to a core laboratory for testing. This paper presents a project to improve the collection routes of two of the largest clinical laboratories in Spain. These routes must be designed in a cost-efficient manner while satisfying two important constraints: (i) two-hour time windows between collection and delivery, and (ii) vehicle capacity.

Methods

A heuristic method based on a genetic algorithm has been designed to solve the problem of blood sample collection. The user enters the following information for each collection point: postal address, average collecting time, and average demand (in thermal containers). After implementing the algorithm using C programming, this is run and, in few seconds, it obtains optimal (or near-optimal) collection routes that specify the collection sequence for each vehicle. Different scenarios using various types of vehicles have been considered. Unless new collection points are added or problem parameters are changed substantially, routes need to be designed only once.

Results

The two laboratories in this study previously planned routes manually for 43 and 74 collection points, respectively. These routes were covered by an external carrier company. With the implementation of this algorithm, the number of routes could be reduced from ten to seven in one laboratory and from twelve to nine in the other, which represents significant annual savings in transportation costs.

Conclusions

The algorithm presented can be easily implemented in other laboratories that face this type of problem, and it is particularly interesting and useful as the number of collection points increases. The method designs blood collection routes with reduced costs that meet the time and capacity constraints of the problem.

Keywords:
Blood sample transportation; Vehicle routing problem; Genetic algorithm; Operations research