Optimization of production and transport infrastructure based on cluster analysis methods
In order to solve the problems of optimizing production and transportation systems, a clustering procedure for objects is suggested. The procedure is a universal methodology for dividing a set of objects into subsets with their centers possessing optimal properties. At the same time, the use of poin...
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EDP Sciences
2020-01-01
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doaj-6903ffff7e6f4330bd4e03b366efa0892021-04-02T13:23:00ZengEDP SciencesE3S Web of Conferences2267-12422020-01-011640300810.1051/e3sconf/202016403008e3sconf_tpacee2020_03008Optimization of production and transport infrastructure based on cluster analysis methodsMoskvichev Oleg0Nikishchenkov Sergei1Moskvicheva Elena2Samara State University of TransportSamara State University of TransportSamara State University of TransportIn order to solve the problems of optimizing production and transportation systems, a clustering procedure for objects is suggested. The procedure is a universal methodology for dividing a set of objects into subsets with their centers possessing optimal properties. At the same time, the use of point proximity metrics used in cluster analysis models the minimization of distances during transportation. If the volume of produced/extracted containerisable products of a production point is considered as the “weight” of each point, than the problem of minimizing transportation costs can be solved as a problem of optimizing clusters and their centers. A set of analytical models has been developed to describe and optimize the choice of location and number of container terminals (CT) at the first level and container storage and distribution centers (СSDC) at the second level of a two-level terminal model and logistics infrastructure of the container transport system (CTS). New clustering algorithms are suggested to determine the locations of CT and СSDC based on the condition of minimizing transportation costs and creating a terminal and logistics infrastructure, taking into account given or random number of clusters.https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/24/e3sconf_tpacee2020_03008.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Moskvichev Oleg Nikishchenkov Sergei Moskvicheva Elena |
spellingShingle |
Moskvichev Oleg Nikishchenkov Sergei Moskvicheva Elena Optimization of production and transport infrastructure based on cluster analysis methods E3S Web of Conferences |
author_facet |
Moskvichev Oleg Nikishchenkov Sergei Moskvicheva Elena |
author_sort |
Moskvichev Oleg |
title |
Optimization of production and transport infrastructure based on cluster analysis methods |
title_short |
Optimization of production and transport infrastructure based on cluster analysis methods |
title_full |
Optimization of production and transport infrastructure based on cluster analysis methods |
title_fullStr |
Optimization of production and transport infrastructure based on cluster analysis methods |
title_full_unstemmed |
Optimization of production and transport infrastructure based on cluster analysis methods |
title_sort |
optimization of production and transport infrastructure based on cluster analysis methods |
publisher |
EDP Sciences |
series |
E3S Web of Conferences |
issn |
2267-1242 |
publishDate |
2020-01-01 |
description |
In order to solve the problems of optimizing production and transportation systems, a clustering procedure for objects is suggested. The procedure is a universal methodology for dividing a set of objects into subsets with their centers possessing optimal properties. At the same time, the use of point proximity metrics used in cluster analysis models the minimization of distances during transportation. If the volume of produced/extracted containerisable products of a production point is considered as the “weight” of each point, than the problem of minimizing transportation costs can be solved as a problem of optimizing clusters and their centers. A set of analytical models has been developed to describe and optimize the choice of location and number of container terminals (CT) at the first level and container storage and distribution centers (СSDC) at the second level of a two-level terminal model and logistics infrastructure of the container transport system (CTS). New clustering algorithms are suggested to determine the locations of CT and СSDC based on the condition of minimizing transportation costs and creating a terminal and logistics infrastructure, taking into account given or random number of clusters. |
url |
https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/24/e3sconf_tpacee2020_03008.pdf |
work_keys_str_mv |
AT moskvichevoleg optimizationofproductionandtransportinfrastructurebasedonclusteranalysismethods AT nikishchenkovsergei optimizationofproductionandtransportinfrastructurebasedonclusteranalysismethods AT moskvichevaelena optimizationofproductionandtransportinfrastructurebasedonclusteranalysismethods |
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