Automated Bus Crew Rescheduling for Late for Sign-On (LFSO) Event using Multi-Agent System

Unpredictable events (UE) are major factors that cause crew rescheduling to be performed. One of the UE is when a crew is late for duty. In this research, it is termed as Late for Sign-On (LFSO). When LFSO occurred, the reschedule is needed to make sure available crew take the duty. Currently, there is no automated mechanism to handle the LFSO. Real time rescheduling approaches mostly are not supported due to static schedules constraint. Mathematical approaches require extensive computational power therefore delayed the real-time results. Meanwhile, manual rescheduling is prone to error and not optimum. This research objective is to develop a new approach in automating the crew rescheduling process using multiagent system. The agents dynamically adapt their behaviour to changing environments quickly and find solutions via negotiations and cooperation between them. Experiment is conducted using AgentPower simulation tool. The result concluded that the proposed technique is capable to reschedule quickly. The distribution of a duty also plays a major role in achieving rescheduling success

Application of an iterative framework for real-time railway rescheduling

Since disruptions in railway networks are inevitable, railway operators and infrastructure managers need reliable measures and tools for disruption management. Current literature on railway disruption management focuses most of the time on rescheduling one resource (timetable, rolling stock or crew) at the time. In this research, we describe the application of an iterative framework in which all these three resources are considered. The framework applies existing models and algorithms for rescheduling the individual resources. We extensively test our framework on instances from Netherlands Railways and show that schedules which are feasible for all three resources can be obtained within short computation times. This case study shows that the framework and the existing rescheduling approaches can be of great value in practice

A Next Step in Disruption Management: Combining Operations Research and Complexity Science

Railway systems occasionally get into a state of out-of-control, meaning that there is barely any train is running, even though the required resources (infrastructure, rolling stock and crew) are available. These situations can either be caused by large disruptions or unexpected propagation and accumulation of delays. Because of the large number of aected resources and the absence of detailed, timely and accurate information, currently existing methods cannot be applied in out-of-control situations. Most of the contemporary approaches assume that there is only one single disruption with a known duration, that all information about the resources is available, and that all stakeholders in the operations act as expected. Another limitation is the lack of knowledge about why and how disruptions accumulate and whether this process can be predicted. To tackle these problems, we develop a multidisciplinary framework aiming at reducing the impact of these situations and - if possible - avoiding them. The key elements of this framework are (i) the generation of early warning signals for out-of-control situations using tools from complexity science and (ii) a set of rescheduling measures robust against the features of out-of-control situations, using tools from operations research

A next step in disruption management : combining operations research and complexity science

Railway systems occasionally get into a state of being out-of-control, meaning that barely any train is running, even though the required resources (infrastructure, rolling stock and crew) are available. Because of the large number of affected resources and the absence of detailed, timely and accurate information, currently existing disruption management techniques cannot be applied in out-of-control situations. Most of the contemporary approaches assume that there is only one single disruption with a known duration, that all information about the resources is available, and that all stakeholders in the operations act as expected. Another limitation is the lack of knowledge about why and how disruptions accumulate and whether this process can be predicted. To tackle these problems, we develop a multidisciplinary framework combining techniques from complexity science and operations research, aiming at reducing the impact of these situations and—if possible—avoiding them. The key elements of this framework are (i) the generation of early warning signals for out-of-control situations, (ii) isolating a specific region such that delay stops propagating, and (iii) the application of decentralized decision making, more suited for information-sparse out-of-control situations

Determining and Evaluating Alternative Line Plans in (Near) Out-of-Control Situations

From time to time, large disruptions cause heavily utilized railway networks to get in a state of (near) out-of-control, in which hardly any trains are able to run as the result of a lack of accurate and up-to-date information available to dispatchers. In this paper, we develop and test disruption management strategies for dealing with these situations. First, we propose an algorithm that finds an alternative line plan that can be operated in the affected part of the railway network. As the line plan should be feasible with respect to infrastructural and resource restrictions, we integrate these aspects in the algorithm in a Benders'-like fashion. Second, to operate the railway system within the disrupted region, we propose several local train dispatching strategies requiring varying degrees of exibility and coordination. Computational experiments based on disruptions in the Dutch railway network indicate that the algorithm performs well, finding workable and passenger oriented line plans within a couple of minutes. Moreover, we also demonstrate in a simulation study that the produced line plans can be operated smoothly without depending on central coordination

A Column Generation Approach for the Integrated Crew Re-Planning Problem

In this paper, we propose a column generation approach for crew re-planning, i.e., the construction of new duties and rosters for the employees, given changes in the timetable and rolling stock schedule. In the current practice, the feasibility of the new rosters is `assured' by allowing the new duties to deviate only slightly from the original ones. In the Integrated Crew Re-Planning Problem (ICRPP), we loosen this requirement and allow more exibility: The ICRPP considers the re-scheduling of crew for multiple days simultaneously, thereby explicitly taking the feasibility of the rosters into account, and hence allowing arbitrary deviations from the original duties. We propose a mathematical formulation for the ICRPP and develop a column generation approach to solve the problem. We apply our solution approach to practical instances from NS, and show the benefit of integrating the re-scheduling process