Optimization of blood sample collection with timing and quality constraints

Amir Elalouf; Dmitry Tsadikovich; Sharon Hovav

doi:10.1111/itor.12354

Optimization of blood sample collection with timing and quality constraints

Amir Elalouf, Dmitry Tsadikovich, Sharon Hovav

Department of Management

Bar-Ilan University

Research output: Contribution to journal › Article › peer-review

Abstract

We focus on planning transportation operations within a blood sample supply chain, which comprises clinics and a laboratory. Specifically, the main goal of this study is to obtain the optimal number of vehicles to be deployed and the scheduling of the pickup process. First, we formulate a mixed-integer programming (MIP) problem. Next, we develop a heuristic scheme composed of two heuristic algorithms and numerical search, and a new genetic algorithm. In an extensive numerical study, based on the data from a real-life blood sample collection process, we illustrate the potential of the new heuristic scheme.

Original language	English
Pages (from-to)	191-214
Number of pages	24
Journal	International Transactions in Operational Research
Volume	25
Issue number	1
DOIs	https://doi.org/10.1111/itor.12354
State	Published - 1 Jan 2018

Keywords

MIP
blood samples collection
genetic algorithm
optimization
tabu search

All Science Journal Classification (ASJC) codes

Business and International Management
Computer Science Applications
Strategy and Management
Management Science and Operations Research
Management of Technology and Innovation

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10.1111/itor.12354

Cite this

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title = "Optimization of blood sample collection with timing and quality constraints",

abstract = "We focus on planning transportation operations within a blood sample supply chain, which comprises clinics and a laboratory. Specifically, the main goal of this study is to obtain the optimal number of vehicles to be deployed and the scheduling of the pickup process. First, we formulate a mixed-integer programming (MIP) problem. Next, we develop a heuristic scheme composed of two heuristic algorithms and numerical search, and a new genetic algorithm. In an extensive numerical study, based on the data from a real-life blood sample collection process, we illustrate the potential of the new heuristic scheme.",

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AB - We focus on planning transportation operations within a blood sample supply chain, which comprises clinics and a laboratory. Specifically, the main goal of this study is to obtain the optimal number of vehicles to be deployed and the scheduling of the pickup process. First, we formulate a mixed-integer programming (MIP) problem. Next, we develop a heuristic scheme composed of two heuristic algorithms and numerical search, and a new genetic algorithm. In an extensive numerical study, based on the data from a real-life blood sample collection process, we illustrate the potential of the new heuristic scheme.

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KW - blood samples collection

KW - genetic algorithm

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KW - tabu search

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JO - International Transactions in Operational Research

JF - International Transactions in Operational Research

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ER -

Optimization of blood sample collection with timing and quality constraints

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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