TRIPS体系结构
样品(材料)
计算机科学
运筹学
运输工程
调度(生产过程)
工作(物理)
运营管理
业务
环境经济学
工程类
经济
机械工程
化学
色谱法
作者
Emma Gibson,Sarang Deo,Jónas Oddur Jónasson,Mphatso Kachule,Kara Palamountain
标识
DOI:10.1287/msom.2022.1182
摘要
Problem definition: Healthcare systems in resource-limited settings rely on diagnostic networks in which medical samples (e.g., blood, sputum) and results need to be transported between geographically dispersed healthcare facilities and centralized laboratories. Academic/practical relevance: Existing sample transportation (ST) systems typically operate fixed schedules, which do not account for demand variability and lead to unnecessary transportation visits as well as delays. Methodology: We design an optimized sample transportation (OST) system that comprises two components: (i) a new approach for timely collection of information on transportation demand (samples and results) using low-cost technology based on feature phones, and (ii) an optimization-based solution approach to the problem of routing and scheduling courier trips in a multistage transportation system. Results: Our solution approach performs well in a range of numerical experiments. Furthermore, we implement OST in collaboration with Riders For Health, who operate the national ST system in Malawi. Based on analysis of field data describing over 20,000 samples and results transported during July–October 2019, we show that the implementation of OST routes reduced average ST delays in three districts of Malawi by approximately 25%. In addition, the proportion of unnecessary trips by ST couriers decreased by 55%. Managerial implications: Our approach for improving ST operations is feasible and effective in Malawi and can be applied to other resource-limited settings, particularly in sub-Saharan Africa. History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition. Funding: This work was supported by Bill and Melinda Gates Foundation [Grant OPP1182217] and by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health [Grant U54EB027049]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1182 .
科研通智能强力驱动
Strongly Powered by AbleSci AI