En raison de problèmes opérationnels et d’autres événements inattendus, un grand nombre d’incidents se produisent quotidiennement dans les systèmes de transport ferroviaire. Certains d’entre eux ont un impact local, mais quelques fois, essentiellement dans les réseaux ferroviaires plus saturés, des petits incidents peuvent se propager à travers tout le réseau et perturber de manière significative les horaires des trains. Dans cette thèse doctorale, nous présentons le problème de réordonnancement de plan de circulation ferroviaire en cas d’incident comme la problématique de créer un plan de circulation provisoire de manière à minimiser les effets de la propagation des incidents. Ce travail est issu du projet MAGES (Module d’Aide à la Gestion des Sillons) qui développe des systèmes de régulation pour le trafic ferroviaire. Nous présentons deux modèles différents qui permettent de trouver des solutions à ce problème : Programmation Linéaire en Nombres Entiers (PLNE) et Programmation Par Contraintes (PPC). Du fait de la nature fortement combinatoire du problème et de la nécessité de répondre rapidement aux incidents, il ne paraît pas raisonnable d’envisager une résolution exacte. Les méthodes correctives proposées consistent donc à explorer un voisinage restreint des solutions : right-shift rescheduling; une méthode basée sur des coupes de proximité; une méthode d’analyse statistique de la propagation des incidents (SAPI) et un méthode basée sur la PPC. Additionnellement, certaines de ces méthodes ont été adaptées sous forme d’algorithmes itératifs avec l’objectif d’améliorer progressivement la solution quand le temps d’exécution le permet. SAPI est une des principales contributions de cette thèse. SAPI intègre les concepts de right-shift rescheduling avec les coupes de proximité. Du fait de la taille des réseaux en jeu et du nombre de circulations, les phénomènes complexes de propagation d’un incident font qu’il est très difficile de connaitre de manière précise les événements qui seront affectés. Toutefois, il est tout de même envisageable d’évaluer la probabilité qu’un événement soit affecté. Pour calculer cette probabilité, un modèle de régression logistique est utilisé avec des variables explicatives dérivées du réseau et des circulations. Diverses variantes de ces méthodes sont évaluées et comparées en utilisant deux réseaux ferroviaires localisés en France et au Chili. À partir des résultats obtenus, il est possible de conclure que SAPI est meilleure que les autres méthodes en terme de vitesse de convergence vers l’optimum pour les instances de petite taille et moyenne alors qu’une méthode coopérative PNLE/PPC est capable de trouver des solutions pour les instances de plus grande taille. La difficulté de comparer SAPI avec d’autres méthodes présentées dans la littérature nous a encouragés à appliquer la méthode à un autre problème. Ainsi, cette méthodologie a été également adaptée au problème de réordonnancement de passagers, vols et appareils (avions) en cas de perturbations, problème originalement proposé dans le contexte du Challenge ROADEF 2009. Les résultats montrent que SAPI est efficace pour résoudre ce problème avec des solutions au-dessus de la moyenne des équipes finalistes en obtenant la troisième place du challenge / For operational and unpredictable reasons, many small incidents occur day after day in rail transportation systems. Most of them have a local impact; but, in some cases, minimal disruptions can spread out through the whole network and affect significantly the train schedules. In this Thesis, we present the Railway Rescheduling Problem (RRP) as the problem of finding a new schedule of trains after one or several incidents by minimizing some measure of the effect, e.g., the total delay. This Thesis has been developed in the context of the MAGES project that builds mathematical models and algorithms for optimizing railway operations. Two complementary formulations are proposed to model this problem: Mixed-Integer Programming (MIP) and Constraint Programming (CP). Because of the impossibility of solving real-world instances by using standard solvers, we propose several solutions methods: right-shift rescheduling; a MIP-based local search method; Statistical Analysis of Propagation of Incidents (SAPI); and a CP-based approach. Some methods are presented in different versions by extending them to iterative approaches. Among them; SAPI is one of the major contributions of this Thesis. It integrates the concepts of right-shift rescheduling and the MIP-based local search method by fixing integer variables and adding linear inequalities (cuts). SAPI assumes that the effects of disruptions can be propagated to other upcoming events. Nevertheless, this propagation is not uniform to all events and could be forecasted by a statistical analysis. Different versions of the methods are compared in two different networks located in France and Chile. From the results, it is possible to conclude that SAPI finds good solutions faster than the other methods, while a cooperative CP/MIP approach that takes advantage of both formulations seems to be appropriate for large instances. Because of the difficulty to compare SAPI to other methods presented in the literature due to lack of public benchmarks, we applied it to another problem where public instances are available. Hence, the methodology was adapted and applied to the problem of rescheduling passengers, flights, and aircraft under disrupted operations in the context of the ROADEF challenge 2009. SAPI took the third position on this competition, showing that the method seems to be effective solving such type of problems efficiently

Airline disruption management traditionally seeks to address three problem dimensions – aircraft scheduling, crew scheduling, and passenger scheduling – in that order. However, current efforts have, at most, only addressed the first two con-currently and do not account for the propagative effects that uncertain scheduling outcomes in one dimension can have on another. Uncertainties in scheduling out-comes originate from random disruption events (like inclement weather and aircraft malfunction), the order in which they occur, and how they are resolved. As such, these uncertainties propagate through all problem dimensions of airline disruption management on day of operation. Existing approaches for airline operations recovery include human specialists who decide on the necessary corrective actions to airline schedule disruptions on the day of operation. However, human specialists are limited in their ability to process copious amounts of information, necessary to make robust decisions that simultaneously address all three problem dimensions in operations recovery. Therefore, there is a need to augment the decision-making capabilities of a human specialist with quantitative and qualitative tools that can rationalize complex interactions amongst the three dimensions in airline operations recovery, and provide objective insights to the specialists in the Airline Operations Control Center (AOCC).To this effect, this dissertation provides a discussion of an agnostic and systematic paradigm for enabling simultaneously-integrated recovery of all problem dimensions in airline disruption management, through an intelligent multi-agent system that employs principles from artificial intelligence and distributed ledger technology.

The objective of this thesis is to assess and map the logistics chain of Kaunis Iron AB in order to find areas of optimisation opportunities. By focusing on the internal aspects of the logistics chain, this thesis aims to provide Kaunis Iron AB with quantified suggestions of how to increase the internal efficiency and effectiveness. With data provided by Kaunis Iron AB and workshops with key stakeholders, a comprehensive analysis of the logistics chain was established, from which different Key Perfomance Indicators could be identified. In addition to the data received from Kaunis Iron AB, other sources of secondary data was collected, further validating the findings and tying together the internal subjective perspective with a more objective ditto. With a ramp-up in production volume being imminent, evaluations both pertaining to the current state of operations and a future state were conducted coupled with comparisons between the two. Utilising the aforementioned Key Performance Indicators, several sensitivity analyses were presented, which showcased how different adjustments in the logistics chain affected the output, both at the current state conditions and in the ramped-up production state. From this, the thesis presents the adjustments that, we as authors, find most suitable for Kaunis Iron AB in order to optimise their logistics chain.

Submitted by Maike Costa (maiksebas@gmail.com) on 2016-04-25T13:32:58Z No. of bitstreams: 1 arquivo total.pdf: 4525999 bytes, checksum: 439ef786ba011b8d141d0b5a92537aa3 (MD5) / Made available in DSpace on 2016-04-25T13:32:58Z (GMT). No. of bitstreams: 1 arquivo total.pdf: 4525999 bytes, checksum: 439ef786ba011b8d141d0b5a92537aa3 (MD5) Previous issue date: 2015-05-28 / The airlines that operate regular ights de ne in advance the airports to be operated and the landing and takeo schedule of its aircraft. This scheduling is likely to su er interruptions causing nancial losses due to delays and/or cancellations of ights. In these situations, the airlines usually use the experience of their professionals and seek to minimize the impacts by relocating the aircraft, crew and then passengers. There is no guarantee that such method will lead to good results from an economic point of view, especially in periods of high demands of passengers. Due to this di culty, several authors have studied the Airline Recovery Problem using di erent optimization techniques. This problem is basically composed of three sub-problems: Aircraft Recovery Problem (ARP), Crew Recovery Problem (CRP) and Passenger Recovery Problem (PRP). In order to de- ne the new least-cost aircraft scheduling of a Brazilian airline (in operation interruption situations) due to delays and/or cancellations of ights, this research presents an ARP solution proposal starting from the representation of ights through a network time-space and mathematical modeling analogous to the minimum cost ow problem. To analyze the ARP, data was used from a Brazilian airline for building the time-space networks with bands of 30, 20 and 15 minutes, and 100 instances were utilized to simulate the unavailability of up to 3 aircraft on di erent nodes of such networks. The solutions based on these bands were solved via Integer Linear Programming and with average improvements of 38.24%, 40.44% and 41.15%, respectively, with respect to the trivial solutions. The band of 15 min was more appropriate because it provided a more realistic analysis of takeo s and landings events and resulted in a greater di erence, on average, between the optimal solutions and the trivial ones. Other 95 instances were tested for a time-space network with 15 min band and a spare aircraft located at the busiest airport. In this case the results were 38.68% better than the situation without a spare aircraft, but it was not conclusive because an economic feasibility analysis on the acquisition and deployment of a new aircraft in the eet must be performed. / As companhias a ereas de voos regulares possuem previamente de nidos os aeroportos que ser~ao operados, os dias e os hor arios de pouso e decolagem das suas aeronaves. E poss vel que essa programa c~ao sofra interrup c~oes e causem preju zos nanceiros devido aos atrasos e/ou cancelamentos dos voos. Nessas situa c~oes, normalmente as companhias a ereas usam a experi^encia dos seus pro ssionais e procuram minimizar os impactos realocando as aeronaves, tripulantes e em seguida os passageiros. N~ao h a garantia que esse m etodo retorne um bom resultado do ponto de vista econ^omico, sobretudo em per odos de grande demanda por passageiros. Mediante essa di culdade, diversos autores t^em estudado o Airline Recovery Problem empregando diferentes t ecnicas de otimiza c~ao. Esse problema e composto basicamente por tr^es subproblemas: Aircraft Recovery Problem (ARP), Crew Recovery Problem (CRP) e Passenger Recovery Problem (PRP). Como forma de de nir o novo sequenciamento das aeronaves de uma companhia a erea brasileira que, em situa c~oes de interrup c~oes das opera c~oes, resulte no menor custo devido aos atrasos e/ou cancelamentos dos voos, esta pesquisa apresenta uma proposta de solu c~ao do ARP a partir da representa c~ao dos voos por uma rede tempo-espa co e modelagem matem atica an aloga ao problema do uxo de custo m nimo. Para a an alise do ARP, foram utilizados dados de uma companhia a erea brasileira para a constru c~ao das redes tempo-espa co com bandas de 30, 20 e 15 minutos e empregadas 100 inst^ancias que simularam a indisponibilidade de at e 3 aeronaves em diferentes n os dessas redes. As solu c~oes baseadas nessas bandas foram resolvidas via Programa c~ao Linear Inteira e apresentaram resultados m edios, respectivamente, 38; 24%; 40; 44% e 41; 15% melhores do que as solu c~oes triviais. A banda de 15 min mostrou-se mais adequada porque possibilitou uma an alise mais realista dos eventos de pousos e decolagens e resultou numa diferen ca m edia maior entre as solu c~oes otimas e as triviais. Outras 95 inst^ancias foram testadas para uma rede tempo-espa co com banda de 15 min e aeronave reserva localizada no aeroporto de maior movimento. O resultado foi 38; 68% melhor do que a situa c~ao sem aeronave reserva, mas n~ao conclusivo por ser necess aria uma an alise de viabilidade econ^omica sobre a aquisi c~ao e disponibiliza c~ao de uma nova aeronave na frota.

Disruptions severely undermine the reliability of railway systems. Consequently, a lot of investments are made to improve disruption management. Much has already been written about disruption management, often with the purpose of supporting operators in their decision making. However, to the best of our knowledge, this literature doesn't consider the structural differences of disruption management in different countries. An overview of the various ways in which disruptions are solved and conditions under which that happens could help rail infrastructure managers and train operating companies to reconsider the ways in which they operate. This paper takes stock of the similarities and differences in how disruptions are managed in Austria, Belgium, Denmark, Germany and the Netherlands. Of importance is not only how these systems work on paper, but above all what happens in practice, i.e. the habits and routines that operators have developed for solving disruptions.

In recent years European airspace has become increasingly congested and airlines can now observe that en-route capacity constraints are the fastest growing source of flight delays. In 2010 this source of delay accounted for 19% of all flight delays in Europe and has been increasing with an average yearly rate of 17% from 2005 to 2010. This paper suggests and evaluates an approach to how disruption management can be combined with flight planning in order to create more proactive handling of the kind of disruptions, which are caused by congested airspace. The approach is evaluated using data from a medium size European carrier and estimates a lower bound saving of several million USD.

The ongoing semiconductor shortage impacts organizations throughout the supply chain operationally and financially. Limited material for a sudden increase in demand during the Covid-19 pandemic led to a heightened supplier power and result in the prioritization of customers. Companies like the test-bench producer for the automobile industry Loccioni face thereby the challenges of being a relatively small and thereby almost invisible company in the automotive supply chain. The purpose of this master thesis is to research what pre- and post-disruptive strategies mitigate the negative impact of supply disruptions for organizations with small- and medium-sized enterprises (SME) character in the context of the semiconductor shortage. Additionally, it aims to understand the supplier-buyer power shift during such events and how supply chain trends like lean management contribute to the vulnerability of organizations to such risks. To answer the research question, a qualitative case study was conducted with the high-tech manufacturer Loccioni. Data was collected through a period of ethnography and un-/semi-structured interviews and assessed through thematic analysis. The developed conceptual framework provides a wholesome guidelinefor SMEs or SME alike companies on how to incorporate reactive risk management by considering the whole disruption cycle (pre- and postdisruptive) to mitigate impacts caused by the semiconductor shortage or similar crisis. The findings of this case study show that it is important to lower dependencies on single suppliers and strive for a flexible working culture to encourage turning challenges into opportunities and being decisive during disruptive events. Following a lean approach with a just-in-time strategy is considered a high risk when not having an evolved supply risk monitoring and analyzing system implemented. Therefore, proactive risk-mitigating approaches, such as safety buffers, building few but close relationships and engaging in cooperative activities, are encouraged. Also, even though the resources of SMEs are limited, is alertness towards supply chain risks considered crucial requiring activities like supplier audits and a well supporting IT infrastructure. During disruption, crucial quick and flexible reactions are enabled by agile project management and entrepreneurial efforts fostered by the company culture. Keeping a balance between being too flexible and structured is mandatory to act efficiently. It is important to create a database and safety stock of the critical material and adjust procurement strategies by multi-sourcing and increased escalation efforts. Internally and externally combined efforts can be key to optimizing operations, sourcing the shortage materials and finding alternative technologies to substitute. Strategic collaborations with supply chain partners, exploitationof customer relationships and continuous communication are crucial for smaller enterprises to maintain customer satisfaction. However, there is no single solution for every organization, but rather suggestions which need to be adjusted and evaluated against a company’s business strategy and competitive advantage.

The paper proposes the management of bus disruption (e.g. fleet failure) and maintain a resilient transportation system through a synergy between shared autonomous vehicles and the existing public transport system based on the organizational structure and demand characteristics. The methodology is applied to the region of Rennes (France) and its surroundings.