Planning Electric-Rolling-Stock Maintenance in Conditions of Limited Resources
Planning electric-rolling-stock (ERS) maintenance in conditions of limited resources can be carried out based on the following criteria of efficiency of construction of the cycle diagram of the electric rolling stock: meeting the requirements of the railway-traffic safety provided by adjusting the p...
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Veröffentlicht in: | Russian electrical engineering 2017, Vol.88 (12), p.839-841 |
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creator | Sidorenko, V. G. Aung, Chzho Min Alekseev, V. M. Rozenberg, E. N. Umanskii, V. I. |
description | Planning electric-rolling-stock (ERS) maintenance in conditions of limited resources can be carried out based on the following criteria of efficiency of construction of the cycle diagram of the electric rolling stock: meeting the requirements of the railway-traffic safety provided by adjusting the planned movement time of the electric rolling stock for the purpose of not allowing an excessive lapse of time between the maintenance over that permissible and uniformity of maintenance. The solution of the set problem using the graph theory allows obtaining the whole set of the permissible values of maintenance and selecting a value that, on the one hand, corresponds to the planned train time schedule (PTTS) and, on the other hand, differs minimally from the optimal with respect to the selected criterion. This takes a significant amount of time. The problem can be quickly solved using a genetic algorithm. The introduction of a new criterion—total excess time lapse between maintenance works over the permissible interval—allows obtaining the solution with any initial data, which is not always achievable when using the uniform-maintenance criterion. The crossover and permutation algorithm implemented within the genetic algorithm is adapted taking into account considering the peculiarities of the agents engaged in solving the problem that has been set out. We have studied the possibility of using various types of crossover and permutation to construct the cycle diagrams and influence of the parameters of the genetic algorithm on the results. The obtained analytical results are tested for the conditions of the Moscow subway. |
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The solution of the set problem using the graph theory allows obtaining the whole set of the permissible values of maintenance and selecting a value that, on the one hand, corresponds to the planned train time schedule (PTTS) and, on the other hand, differs minimally from the optimal with respect to the selected criterion. This takes a significant amount of time. The problem can be quickly solved using a genetic algorithm. The introduction of a new criterion—total excess time lapse between maintenance works over the permissible interval—allows obtaining the solution with any initial data, which is not always achievable when using the uniform-maintenance criterion. The crossover and permutation algorithm implemented within the genetic algorithm is adapted taking into account considering the peculiarities of the agents engaged in solving the problem that has been set out. We have studied the possibility of using various types of crossover and permutation to construct the cycle diagrams and influence of the parameters of the genetic algorithm on the results. The obtained analytical results are tested for the conditions of the Moscow subway.</description><identifier>ISSN: 1068-3712</identifier><identifier>EISSN: 1934-8010</identifier><identifier>DOI: 10.3103/S106837121712015X</identifier><language>eng</language><publisher>Moscow: Pleiades Publishing</publisher><subject>Criteria ; Engineering ; Genetic algorithms ; Graph theory ; Machines ; Maintenance ; Manufacturing ; Permutations ; Processes ; Rolling stock ; Traffic safety</subject><ispartof>Russian electrical engineering, 2017, Vol.88 (12), p.839-841</ispartof><rights>Allerton Press, Inc. 2017</rights><rights>Copyright Springer Science & Business Media 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c279X-c75aad6f3a7ef40285cef19ad2cd9b1c3a9f9f06615d08cd70f8c93067e7bf413</citedby><cites>FETCH-LOGICAL-c279X-c75aad6f3a7ef40285cef19ad2cd9b1c3a9f9f06615d08cd70f8c93067e7bf413</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.3103/S106837121712015X$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.3103/S106837121712015X$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51298</link.rule.ids></links><search><creatorcontrib>Sidorenko, V. 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The solution of the set problem using the graph theory allows obtaining the whole set of the permissible values of maintenance and selecting a value that, on the one hand, corresponds to the planned train time schedule (PTTS) and, on the other hand, differs minimally from the optimal with respect to the selected criterion. This takes a significant amount of time. The problem can be quickly solved using a genetic algorithm. The introduction of a new criterion—total excess time lapse between maintenance works over the permissible interval—allows obtaining the solution with any initial data, which is not always achievable when using the uniform-maintenance criterion. The crossover and permutation algorithm implemented within the genetic algorithm is adapted taking into account considering the peculiarities of the agents engaged in solving the problem that has been set out. We have studied the possibility of using various types of crossover and permutation to construct the cycle diagrams and influence of the parameters of the genetic algorithm on the results. The obtained analytical results are tested for the conditions of the Moscow subway.</description><subject>Criteria</subject><subject>Engineering</subject><subject>Genetic algorithms</subject><subject>Graph theory</subject><subject>Machines</subject><subject>Maintenance</subject><subject>Manufacturing</subject><subject>Permutations</subject><subject>Processes</subject><subject>Rolling stock</subject><subject>Traffic safety</subject><issn>1068-3712</issn><issn>1934-8010</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp1UEtLAzEQDqJgrf4AbwueozOb3c3uUUp9wPqgVehtSfMoqdukJtuD_96UCh7EwzDDfI8ZPkIuEa4ZAruZI1Q145hjKsBycURG2LCC1oBwnOYE0z1-Ss5iXAOUVV4UI_L82gvnrFtl017LIVhJZ77v04LOBy8_sidh3aCdcFJn1mUT75QdrHcx8yZr7cYOWmUzHf0uSB3PyYkRfdQXP31M3u-mb5MH2r7cP05uWypz3iyo5KUQqjJMcG0KyOtSaoONULlUzRIlE41pDFQVlgpqqTiYWjYMKq750hTIxuTq4LsN_nOn49Ct0wMunexyAI4pk4IlFh5YMvgYgzbdNtiNCF8dQrePrfsTW9LkB01MXLfS4df5f9E3HVxvGQ</recordid><startdate>2017</startdate><enddate>2017</enddate><creator>Sidorenko, V. 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I.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c279X-c75aad6f3a7ef40285cef19ad2cd9b1c3a9f9f06615d08cd70f8c93067e7bf413</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Criteria</topic><topic>Engineering</topic><topic>Genetic algorithms</topic><topic>Graph theory</topic><topic>Machines</topic><topic>Maintenance</topic><topic>Manufacturing</topic><topic>Permutations</topic><topic>Processes</topic><topic>Rolling stock</topic><topic>Traffic safety</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sidorenko, V. G.</creatorcontrib><creatorcontrib>Aung, Chzho Min</creatorcontrib><creatorcontrib>Alekseev, V. M.</creatorcontrib><creatorcontrib>Rozenberg, E. N.</creatorcontrib><creatorcontrib>Umanskii, V. I.</creatorcontrib><collection>CrossRef</collection><jtitle>Russian electrical engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sidorenko, V. G.</au><au>Aung, Chzho Min</au><au>Alekseev, V. M.</au><au>Rozenberg, E. N.</au><au>Umanskii, V. I.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Planning Electric-Rolling-Stock Maintenance in Conditions of Limited Resources</atitle><jtitle>Russian electrical engineering</jtitle><stitle>Russ. Electr. 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The solution of the set problem using the graph theory allows obtaining the whole set of the permissible values of maintenance and selecting a value that, on the one hand, corresponds to the planned train time schedule (PTTS) and, on the other hand, differs minimally from the optimal with respect to the selected criterion. This takes a significant amount of time. The problem can be quickly solved using a genetic algorithm. The introduction of a new criterion—total excess time lapse between maintenance works over the permissible interval—allows obtaining the solution with any initial data, which is not always achievable when using the uniform-maintenance criterion. The crossover and permutation algorithm implemented within the genetic algorithm is adapted taking into account considering the peculiarities of the agents engaged in solving the problem that has been set out. 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subjects | Criteria Engineering Genetic algorithms Graph theory Machines Maintenance Manufacturing Permutations Processes Rolling stock Traffic safety |
title | Planning Electric-Rolling-Stock Maintenance in Conditions of Limited Resources |
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