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Epidemic-Logistics Network Considering Time Windows and Service Level
In this chapter, we present two optimization models for optimizing the epidemic-logistics network. In the first one, we formulate the problem of emergency materials distribution with time windows to be a multiple traveling salesman problem. Knowledge of graph theory is used to transform the MTSP to...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120198/ http://dx.doi.org/10.1007/978-981-13-9353-2_13 |
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author | Liu, Ming Cao, Jie Liang, Jing Chen, MingJun |
author_facet | Liu, Ming Cao, Jie Liang, Jing Chen, MingJun |
author_sort | Liu, Ming |
collection | PubMed |
description | In this chapter, we present two optimization models for optimizing the epidemic-logistics network. In the first one, we formulate the problem of emergency materials distribution with time windows to be a multiple traveling salesman problem. Knowledge of graph theory is used to transform the MTSP to be a TSP, then such TSP route is analyzed and proved to be the optimal Hamilton route theoretically. Besides, a new hybrid genetic algorithm is designed for solving the problem. In the second one, we propose an improved location-allocation model with an emphasis on maximizing the emergency service level. We formulate the problem to be a mixed-integer nonlinear programming model and develop an effective algorithm to solve the model. In this chapter, we present two optimization models for optimizing the epidemic-logistics network. In the first one, we formulate the problem of emergency materials distribution with time windows to be a multiple traveling salesman problem. Knowledge of graph theory is used to transform the MTSP to be a TSP, then such TSP route is analyzed and proved to be the optimal Hamilton route theoretically. Besides, a new hybrid genetic algorithm is designed for solving the problem. In the second one, we propose an improved location-allocation model with an emphasis on maximizing the emergency service level. We formulate the problem to be a mixed-integer nonlinear programming model and develop an effective algorithm to solve the model. |
format | Online Article Text |
id | pubmed-7120198 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
record_format | MEDLINE/PubMed |
spelling | pubmed-71201982020-04-06 Epidemic-Logistics Network Considering Time Windows and Service Level Liu, Ming Cao, Jie Liang, Jing Chen, MingJun Epidemic-logistics Modeling: A New Perspective on Operations Research Article In this chapter, we present two optimization models for optimizing the epidemic-logistics network. In the first one, we formulate the problem of emergency materials distribution with time windows to be a multiple traveling salesman problem. Knowledge of graph theory is used to transform the MTSP to be a TSP, then such TSP route is analyzed and proved to be the optimal Hamilton route theoretically. Besides, a new hybrid genetic algorithm is designed for solving the problem. In the second one, we propose an improved location-allocation model with an emphasis on maximizing the emergency service level. We formulate the problem to be a mixed-integer nonlinear programming model and develop an effective algorithm to solve the model. In this chapter, we present two optimization models for optimizing the epidemic-logistics network. In the first one, we formulate the problem of emergency materials distribution with time windows to be a multiple traveling salesman problem. Knowledge of graph theory is used to transform the MTSP to be a TSP, then such TSP route is analyzed and proved to be the optimal Hamilton route theoretically. Besides, a new hybrid genetic algorithm is designed for solving the problem. In the second one, we propose an improved location-allocation model with an emphasis on maximizing the emergency service level. We formulate the problem to be a mixed-integer nonlinear programming model and develop an effective algorithm to solve the model. 2019-10-04 /pmc/articles/PMC7120198/ http://dx.doi.org/10.1007/978-981-13-9353-2_13 Text en © Science Press and Springer Nature Singapore Pte Ltd. 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Liu, Ming Cao, Jie Liang, Jing Chen, MingJun Epidemic-Logistics Network Considering Time Windows and Service Level |
title | Epidemic-Logistics Network Considering Time Windows and Service Level |
title_full | Epidemic-Logistics Network Considering Time Windows and Service Level |
title_fullStr | Epidemic-Logistics Network Considering Time Windows and Service Level |
title_full_unstemmed | Epidemic-Logistics Network Considering Time Windows and Service Level |
title_short | Epidemic-Logistics Network Considering Time Windows and Service Level |
title_sort | epidemic-logistics network considering time windows and service level |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120198/ http://dx.doi.org/10.1007/978-981-13-9353-2_13 |
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