Abstract: This paper describes the design, implementation, visualizations, results and lessons learned of a novel real-world socio-technical research system for the purpose of rescheduling train drivers in
the event of disruptions. The research system is structured according to the Actor-Agent paradigm: here agents assist in rescheduling tasks of train drivers. Coordination between agents is based on a team formation process in which possible rescheduling alternatives can be evaluated, based on constraints and preferences of involved human train drivers and dispatchers. The research system is the result of cooperation on decentralised multi-agent crew rescheduling between Netherlands Railways (NS) and the D-CIS Lab. The implementation is realized using the Cougaar framework and includes actual timetable and rolling stock schedule data and driver duty data.
Abstract: This paper describes the design of a socio-technical research system for the purpose of rescheduling train drivers in the event of disruptions. The research system is structured according to the Actor-Agent paradigm: Here agents assist in rescheduling tasks of train drivers. Coordination between agents is based on a team formation process in which possible rescheduling alternatives can be evaluated, based on constraints and preferences of involved human train drivers and dispatchers. The research aim is to explore
the effectiveness of a decentralized, flexible actor-agent based approach to crew rescheduling. The
research system is realized using the Cougaar framework and includes actual rolling stock schedule data and driver duty data. The current reduced-scale version shows promising results for the full-scale version end 2008.
Abstract: Passenger railway operations are based on an extensive planning process for generating the timetable, the rolling stock circulation, and the crew duties for train drivers and conductors. In particular, crew scheduling is a complex process.
After the planning process has been completed, the plans are carried out in the real-time operations. Preferably, the plans are carried out as scheduled. However, in case of delays of trains or large disruptions of the railway system, the timetable, the rolling stock circulation and the crew duties may not be feasible anymore and must be rescheduled.
This paper presents a method based on multi-agent techniques to solve the train driver rescheduling problem in case of a large disruption. It assumes that the timetable and the rolling stock have been rescheduled already based on an incident scenario. In the crew rescheduling model, each train driver is represented by a driver-agent. A driver-agent whose duty has become infeasible by the disruption starts a recursive task exchange process with the other driver-agents in order to solve this infeasibility. The task exchange process is supported by a route-analyzer-agent, which determines whether a proposed task exchange is feasible, conditionally feasible, or not feasible. The task exchange process is guided by several cost parameters, and the aim is to find a feasible set of duties at minimal total cost.
The train driver rescheduling method was tested on several realistic disruption instances of Netherlands Railways (NS), the main operator of passenger trains in the Netherlands. In general the rescheduling method finds an appropriate set of rescheduled duties in a short amount of time. This research was carried out in close cooperation by NS and the D-CIS Lab.
Abstract: Crew rescheduling in response to disruptions is a difficult problem, due to the additional (social) constraints imposed on human workforce. In the real-world domain of train driver rescheduling in the Netherlands, an actor-agent based approach is taken to (a) support human dispatchers and (b) accommodate individual train drivers’ preferences. This paper outlines the task-exchange team-configuration process including the role of the
various rescheduling constraints. The rescheduling approach is designed for operation in a real world environment: to this end, a number of heuristics are discussed that are currently being examined to optimize the solution finding process with respect to
three dimensions: performance, quality and clarity. The heuristics have been implemented in a research system, supporting the full driver-agent population, working on real world data. This effort is an ongoing study on novel multi-agent approaches to crew
rescheduling, and is the result of cooperation between Netherlands Railways and D-CIS Lab.