Need and Opportunities for a ‘Plan B’ in Rail Track Inspection Schedules

AbstractTrack inspection is purposely performed to recover tracks from defects and damage and eliminate potential safety hazards. It is scheduled through an exhaustive process that usually integrates many disciplines such as optimization, statistics, risk management, etc. Spending so much of a monetary and an emotional investment in an original schedule (referred to as master schedule hereafter) that the scheduler wants to deliver might be a good excuse not to develop a solid ‘Plan B’. Plan B here refers to scheduler responses or a contingency plan when the master schedule does not go as expected. It is found that there is often low to moderate probability of a crisis occurring when a schedule is executed in a real environment. Nevertheless, its impact can leave transportation services to the mercy of the disruption as shown by the Christmas 2014 incident where a huge volume of passengers using King's Cross and Paddington services experienced both inconvenience and discomfort due to engineering delays and train disruption. Thus, this paper aims to discuss the potential of considering ‘Plan B’ or contingency plan if incidents arise that were not expected during track inspection schedule execution. Benefits, general guidelines and relevant strategies for creating a contingency plan are also discussed. We highlight the rationale to support the claim that an original schedule of track inspection jobs should be adapted to respond to a new context e.g. inspection vehicle machine breakdown, new inspection requests, man-made hazards, terrorist attack, extreme weather, climate change, etc. It is however proposed to develop an appropriate set of performance measure that is used to guide rescheduling in track inspection due to financial, equipment inventory, manpower, safety regulations, time and spatial constraints

Regulación de tráfico en redes de autobuses urbanos

Frecuentemente, las líneas de transporte urbano se ven afectadas en su programación y distribución de unidades por distintos tipos de percances en el transcurso de la ruta, que puede o no estar bajo control de la empresa. Para muchas empresas la solución a esta falla depende directamente de las habilidades del coordinador de operaciones. Entonces, la reprogramación del coordinador basada en sus conocimientos empíricos difícilmente será óptima, asunto que conlleva a una mala re-planeación/programación. En este trabajo proponemos un modelo para el sistema de apoyo a la toma de decisiones que pueda ser de utilidad para encontrar buenas soluciones, en tiempos adecuados con el uso de algoritmos eficientes para los problemas de regulación de frecuencias de paso y reprogramación de unidades para minimizar la afección a la planeación original

Algorithmic Support for Railway Disruption Management

Disruptions of a railway system are responsible for longer travel times and much discomfort for the passengers. Since disruptions are inevitable, the railway system should be prepared to deal with them effectively. This paper explains that, in case of a disruption, rescheduling the timetable, the rolling stock circulation, and the crew duties is so complex that solving them manually is too time consuming in a time critical situation where every minute counts. Therefore, algorithmic support is badly needed. To that end, we describe models and algorithms for real-time rolling stock rescheduling and real-time crew rescheduling that are currently being developed and that are to be used as the kernel of decision support tools for disruption management. Furthermore, this paper argues that a stronger passenger orientation, facilitated by powerful algorithmic support, will allow to mitigate the adverse effects of the disruptions for the passengers. The latter will contribute to an increased service quality provided by the railway system. This will be instrumental in increasing the market share of the public transport system in the mobility market.

Minimizing airport peaks problem by improving airline operations performance through an agent based system

Airports are important infra-structures for the air transportationbusiness. One of the major operational constraints is the peak of passengers inspecific periods of time. Airline companies take into consideration the airportcapacity when building the airline schedule and, because of that, the executionof the airline operational plan can contribute to improve or avoid airport peakproblems. The Airline Operations Control Center (AOCC) tries to solveunexpected problems that might occur during the airline operation. Problemsrelated to aircrafts, crewmembers and passengers are common and the actionstowards the solution of these problems are usually known as operationsrecovery. In this paper we propose a way of measuring the AOCC performancethat takes into consideration the relation that exists between airline scheduleand airport peaks. The implementation of a Distributed Multi-Agent System(MAS) representing the existing roles in an AOCC, is presented. We show thatthe MAS contributes to minimize airport peaks without increasing theoperational costs of the airlines

Airline disruption management with aircraft swapping and reinforcement learning

Managing fleet disruption is essential for an airline to control delay costs. Delays emerging from these disruptions can be manipulated through fleet operations like aircraft swapping. This paper applies machine learning techniques to the disruption problem. While airlines might do this process manually or using basic predefined rules, the complexity of the problem makes it well suited for a computed approach. The paper describes the principles of reinforced learning and the model used for testing them. Two representations of decision states are considered and applied to a set of historical schedules for an airline. The performance obtained by swapping aircraft using the reinforced learning is finally compared to the idle option, i.e., do not swap any flight. The comparison evinces that while the algorithm is far from being optimal, the agent takes relevant decisions as it performs better than the idle behaviour in heavily disrupted simulations

Solving airline operations problems using specialized agents in a distributed multi-agent system

An airline schedule very rarely operates as planned. Problems related with aircrafts, crew members and passengers are common and the actions towards the solution of these problems are usually known as operations recovery. The Airline Operations Control Center (AOCC) tries to solve these problems with the minimum cost and satisfying all the required rules. In this paper we present the implementation of a Distributed Multi-Agent System (MAS) representing the existing roles in an AOCC, This MAS deals with several operational bases and for each type of operation problems it has several specialized software agents that implement different algorithms (heuristic, AI, OR, etc.), competing to find the best solution for each problem. We present a real case study taken from an AOCC where a crew recovery problem is solved. Computational results using a real airline schedule are presented, including a comparison with a solution for the same problem found by the human operators in the AOCC. We show that, even in simple problems and when comparing with solutions found by human operators, it is possible to find valid solutions, in less time and with a smaller cost

Priority based technique for rescheduling trains

No AbstractKeywords: rescheduling; mathematical modelling; service disruptions; priorit