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It starts from feasible shift schedules for remaining on-duty drivers, a set of tasks left unassigned by absent drivers, and standby drivers with fixed depot and time windows. The objective is to minimize canceled tasks. A tabu-search-based method with restricted graph search generates feasible alternatives, benchmarked against an adapted column-generation approach. Experiments use both a Stockholm regional system dataset and ROMSOC open data.",{"@graph":63,"@context":119},[64,80,102],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/late-train-crew-rescheduling-a-tabu-search-based-approach-using-restricted-dfs/393565/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/late-train-crew-rescheduling-a-tabu-search-based-approach-using-restricted-dfs/393565.png","ImageObject",442,249,{"name":88,"@type":89},"EvelynWrites","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-22","2026-09-26",true,{"@type":98,"interactionType":99,"userInteractionCount":101},"InteractionCounter",{"@type":100},"ViewAction",8,{"@type":103,"mainEntity":104},"FAQPage",[105,111,115],{"name":106,"@type":107,"acceptedAnswer":108},"What scheduling problem does the paper address?","Question",{"text":109,"@type":110},"It addresses short-term train crew rescheduling when some drivers are absent and some trains would otherwise have no driver, requiring duties to be reassigned for the operation day.","Answer",{"name":112,"@type":107,"acceptedAnswer":113},"How does the proposed method work?",{"text":114,"@type":110},"It uses a tabu-search-based heuristic combined with restricted graph search to generate feasible alternative duties, while allowing deadheading to reach a feasible schedule.",{"name":116,"@type":107,"acceptedAnswer":117},"How is the method evaluated and compared?",{"text":118,"@type":110},"Results are compared against an adapted column-generation approach under the same constraints and objective function, using runtime, solution quality, and additional performance indicators.","https://schema.org",{"og:url":78,"og:type":121,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":123,"canonical":78},"index,follow",{"doc_id":125,"site_id":56},393565,1791745995,{"code":4,"msg":5,"data":128},{"doc_id":125,"user_id":129,"nickname":88,"user_avatar":130,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":101,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":136,"language":137,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":138,"faqs":139,"seo_title":140,"seo_description":61,"update_tm":141,"read_time":142},687212989558,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","arXiv : 250 1 .08098v2 [math .OC] 9 Sep 2026  \nHighlights  \nLate Train-Crew Rescheduling: A Tabu-Search-Based Approach Using Restricted DFS  \nLiyun Yu, Carl Henrik Häll, Anders Peterson, Christiane Schmidt  \n• Research highlight 1: a fixed-timetable crew-rescheduling problem with driver absences and partially uncovered duties is introduced.  \n• Research highlight 2: a restricted-graph-search mechanism generates feasible alternative duties.  \n• Research highlight 3: a tabu-search-based approach is introduced to guide the neighbourhood search.  \nLate Train-Crew Rescheduling: A Tabu-Search-Based Approach Using Restricted DFS  \nLiyun Yua,∗, Carl Henrik Hälla , Anders Petersona , Christiane Schmidtaa Department of Science and Technology, Linköping University, Norrköping, Sweden  \nAbstract  \nIn this paper, we reschedule the duties of train drivers one day before the operation. Because several drivers are absent (for example, because of sick leave), some trains have no driver. Thus, duties need to be rescheduled for the day of operation. We start with a feasible crew schedule for each of the remaining operating drivers, a set of unassigned tasks originally assigned to the absent drivers, and a set of standby drivers with fixed start time, end time, start depot, and end depot. Our aim is to generate a crew schedule with as few canceled tasks as possible. We present a tabu-search-based approach with restricted graph search for crew rescheduling. We also adopt a column-generation approach as a benchmark to reflect the same restrictions and objective function as our tabu-search-based approach. We use it to compare the quality of the results, computational time, and other performance indicators. Our tabu-search-based approach needs less computational time than the column-generation approach to compute an acceptable result. We further test the performance of our approach under different settings. Oneof the datasets used in the experiments originated from a regional passengertrain system around Stockholm, Sweden, and was provided by Mälartåg. The other dataset is generated from the open-source crew scheduling data from the ROMSOC project.  \nKeywords: railway crew rescheduling, tabu search, column generation  \n∗ Email: [liyun.yu@liu.se](liyun.yu@liu.se)  \n1. Introduction  \nRailway disruptions, caused by, for example, late changes in the railway schedule, including timetable, rolling stock, and crew schedules, have been a broadly discussed topic for many decades. While that is certainly not the preferred measure to counter disruptions, railway undertakings (RUs) often react by cancelling trains. As a consequence, passengers possibly face severe delays during travel. It is important to recover the original schedule as fast as possible after major interruptions.  \nRailway undertakings aim to cancel as few trains as possible during rescheduling, as there are significant costs associated with cancellations. These costs include both financial loss and employees’working-time loss, because employees need to spend extra time to move the canceled train back to a specific depot. Changes and complete cancellations on short notice are negative, not only for the involved services, but also for the reliability of rail transport. Furthermore, several RUs are competing in Europe. They need to gain passengers’ trust by providing a more reliable service than their competitors, for example, fewer cancellations of trains.  \nCrew members are a resource, which, in case of absent crew members, must be reallocated. In this paper, we explore short-term crew rescheduling (without the need for rescheduling other resources) and try to cancel as few trains as possible. We consider the case where several train drivers take leave for the next day, and the RU needs to generate a new schedule for the remaining on-duty drivers and some standby drivers starting from various depots. We allow train cancellations as an option, but one that should be avoided. Hence, we aim to canc","cbCaiqWgH6fBRQDU","https://ap.wps.com/l/cbCaiqWgH6fBRQDU","pdf",2287812,38,"English","# Introduction\n## Related Work\n# Tabu-Search-Based Approach\n# Experiments and Results\n# Conclusion","[{\"question\":\"What scheduling problem does the paper address?\",\"answer\":\"It addresses short-term train crew rescheduling when some drivers are absent and some trains would otherwise have no driver, requiring duties to be reassigned for the operation day.\"},{\"question\":\"How does the proposed method work?\",\"answer\":\"It uses a tabu-search-based heuristic combined with restricted graph search to generate feasible alternative duties, while allowing deadheading to reach a feasible schedule.\"},{\"question\":\"How is the method evaluated and compared?\",\"answer\":\"Results are compared against an adapted column-generation approach under the same constraints and objective function, using runtime, solution quality, and additional performance indicators.\"}]","Late Train-Crew Rescheduling - A Tabu-Search-Based Approach Using Restricted DFS | PDF",1790389963,13]