Railway is an environmentally sustainable mode of transportation, which offers convenience for passengers and provides a cost-effective and efficient solution for the movement of goods. However, railway transportation also has drawbacks, especially disruptions due to some incidents, which are difficult to accurately predict and prevent. Infrastructure failure, extreme weather, human error, and lack of staff are typical examples of such incidents. The disruptions cause different levels of train delays and even cancellations. Frequent delays and cancellations make the railway less competitive with other modes of transport. As a result, it is crucial to investigate the possible strategies for rapidly restoring railway traffic after disruptions. In this thesis, we focus on railway rescheduling after near-operational disruptions. We aim to achieve acceptable results within a short time and a small computational effort. This thesis introduces some fundamental concepts related to railway rescheduling, outlines the motivation and research questions of this thesis, discusses further concepts of railway rescheduling including timetable, rolling stock, and crew rescheduling, and displays the relevant methods used for rescheduling. In this thesis, we propose approaches for near-operational rescheduling of the timetable and crew schedules.

In Paper I, we focus on the problem of timetable rescheduling, which is the first step of railway rescheduling. We draw an overall picture of timetable rescheduling for freight trains including its different restrictions. The methodology involves an optimization model, which is aimed to be a baseline model of timetable rescheduling in substitute all this when a group of freight trains must be postponed due to an unexpected marshalling-yard closure and can be compared with other heuristic approaches in the future under similar scenarios.

Crew rescheduling is the last step of railway rescheduling. We discuss this problem in Papers II and III. The purpose of Paper II is to investigate the possibility of reducing the computational time for solving short-term crew rescheduling problems. We introduced a tabu-search-based approach and compared the results along with its computational time and space with a column-generation approach, which is a common method for crew rescheduling. The results show that our tabusearch- based approach can achieve similar results with significantly less computational time and space compared to the column-generation approach. In Paper III, we extend our tabu-search-based approach from Paper II with more flexible options for what can be assigned to a crew member. By doing that, we significantly improved the assignment rate for the operating tasks. The results show that our tabu-search-based approach has a high potential to be applied in real-world scenarios.

Järnväg är ett miljövänligt och hållbart transportsätt som erbjuder bekvämlighet för passagerare och en kostnadseffektiv samt effektiv lösning för godstransporter. Dock medför järnvägstransporter även utmaningar, särskilt i form av störningar till följd av händelser som är svåra att exakt förutse och förebygga. Exempel på sådana händelser inkluderar infrastrukturfel, extrema väderförhållanden, mänskliga misstag och personalbrist. Dessa störningar leder till olika nivåer av tågförseningar och till och med till inställda avgångar. Frekventa förseningar och inställda tåg minskar järnvägens konkurrenskraft i förhållande till andra transportsätt. Därför är det av stor vikt att strategier för att snabbt skapa återställa järnvägstrafiken efter störningar. Denna avhandling fokuserar på omplanering efter driftstörningar med målsättningen att uppnå acceptabla resultat inom kort tid och med begr änsad beräkningskapacitet. Avhandlingen introducerar grundläggande koncept relaterade till omplanering inom järnvägen diskuterar viktiga delar såsom tidtabells-, fordons- och personalomplanering, samt presenterar relevanta metoder. Vidare föreslås metoder för omplanering av både tidtabeller och personalplanering vid driftstörningar nära realtid.

I Paper I behandlas problemet med tidtabellsomplanering, vilket är det första steget i omplaneringen. En övergripande bild ges av tidtabellsomplanering för godståg, inklusive dess olika begränsningar. Metodologin bygger på en optimeringsmodell som utgör en basmodell för tidtabellsomplanering när en grupp godståg måste försenas på grund av en oväntad stängning av en rangerbangård. Modellen kan i framtida studier jämföras med heuristiska metoder för liknande scenarier.

Personalomplanering utgör det sista steget i omplaneringen och behandlas i Paper II och III. Syftet med Paper II är att minska beräkningstiden för att lösa kortsiktiga personalomplaneringsproblem. En tabu-sökningsbaserad metod presenteras och dess resultat, beräkningstid och resursförbrukning jämförs med en kolumngenereringsmetod, som är en vanlig metod för personalomplanering. Resultaten visar att den tabu-sökningsbaserade metoden kan uppnå likvärdiga resultat med avsevärt kortare beräkningstid och mindre resursförbrukning än kolumngenereringsmetoden. I Paper III vidareutvecklas den tabu-sökningsbaserade metoden från Paper II genom att inkludera mer flexibla alternativ för hur arbetsuppgifter kan tilldelas personal. Därigenom kan antalet icke bemannade arbetsuppgifts minskas avsevärt. Resultaten visar att den tabu-sökningsbaserade metoden har stor potential att tillämpas i verkliga scenarier.

In this study, we consider rescheduling freight trains to reduce the effects of major interruptions. We assume that the interruption is an unexpected marshalling-yard closure, and we develop a macroscopic Mixed-Integer-Linear-Programming (MILP) model to reschedule the timetable. Furthermore, we design a rescheduling strategy of letting trains wait on the way when the destination yard has a closure. We consider stopping restrictions and the capacity of each segment and station in the model. The order of the trains affected by the interruption is not fixed. We present experimental results for three different cases of various sizes.

This study is about rescheduling freight trains to reduce the eff ects of major interruptions. In this paper, we consider that the interruption is an unexpected marshallingyard closure. We develop a macroscopic Mixed-Integer Linear Programming (MILP)model to reschedule railway timetables. One important principle is that we simultaneously reschedule several trains, instead of one-by-one. Furthermore, we consider arescheduling strategy of letting trains wait on the way when the destination yard havea closure. The model considers stopping restrictions and the capacity of each segmentand station. The order of the trains aff ected by the interruption is not fi xed. We presentexperimental results of three diff erent cases, which are all based on artifi cial data.

This study is about rescheduling freight trains to reduce the effects of major interruptions. In this paper, we consider that the interruption is an unexpected marshalling-yard closure. We develop a macroscopic Mixed-Integer Linear Programming (MILP) model to reschedule railway timetables. One important principle is that we simultaneously reschedule several trains, instead of one-by-one. Furthermore, we consider a rescheduling strategy of letting trains wait on the way when the destination yard have a closure. The model considers stopping restrictions and the capacity of each segment and station. The order of the trains affected by the interruption is not fixed. We present experimental results of three different cases, which are all based on artificial data.