Jv
J.A.M. van Raaij
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Structural changes in the travel behaviour of rail passengers following the COVID-19 pandemic have led to a strong weekday asymmetry in passenger demand. Demand is becoming more concentrated on the Tuesdays and Thursdays while Monday, Wednesday and Friday have seen a decline in passenger demand. In response a new policy was introduced at the Netherlands Railways (NS) called weekday differentiation. This policy reallocates unplanned servicing activities to low-demand weekdays allowing a higher availability of rolling stock on Tuesday and Thursday. Its implementation is however troubled with low operational performance. Meaning that due to operational constrains only part of the plan is successfully performed. This thesis researches how the robustness of weekday differentiation can be improved by optimising the allocation of rolling stock types to weekday differentiation over a week. The robustness is measured by the ability of the model to fill the weekday differentiation moments, also improving buffers at the weekday differentiation locations and minimizing deadheading. A MILP model is developed to represent the rolling stock flows. The model is an assignment model that assigns rolling stock to one of several states including passenger service and weekday differentiation opportunities. The model captures the trade-off between the passenger service that is rigid and the limited operating room to assign rolling stock units that are not active to the proper tasks. Some computational experiments are conducted in this thesis to analyse the model workings and the outcome of different variants of the model. The results demonstrate that targeted policy interventions can increase the number of successfully allocated rolling stock units while always maintaining careful consideration for the timetable. The findings provide support for refining the current weekday differentiation policy and offer insights for the NS to improve weekday differentiation productivity.
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Structural changes in the travel behaviour of rail passengers following the COVID-19 pandemic have led to a strong weekday asymmetry in passenger demand. Demand is becoming more concentrated on the Tuesdays and Thursdays while Monday, Wednesday and Friday have seen a decline in passenger demand. In response a new policy was introduced at the Netherlands Railways (NS) called weekday differentiation. This policy reallocates unplanned servicing activities to low-demand weekdays allowing a higher availability of rolling stock on Tuesday and Thursday. Its implementation is however troubled with low operational performance. Meaning that due to operational constrains only part of the plan is successfully performed. This thesis researches how the robustness of weekday differentiation can be improved by optimising the allocation of rolling stock types to weekday differentiation over a week. The robustness is measured by the ability of the model to fill the weekday differentiation moments, also improving buffers at the weekday differentiation locations and minimizing deadheading. A MILP model is developed to represent the rolling stock flows. The model is an assignment model that assigns rolling stock to one of several states including passenger service and weekday differentiation opportunities. The model captures the trade-off between the passenger service that is rigid and the limited operating room to assign rolling stock units that are not active to the proper tasks. Some computational experiments are conducted in this thesis to analyse the model workings and the outcome of different variants of the model. The results demonstrate that targeted policy interventions can increase the number of successfully allocated rolling stock units while always maintaining careful consideration for the timetable. The findings provide support for refining the current weekday differentiation policy and offer insights for the NS to improve weekday differentiation productivity.