The coping of demand oscillation is an important challenge in dynamic transport planning. A reliable request fulfillment must be provided even if the number of incoming requests temporarily climbs over the expected demand and resource scarceness appears. The aim of this paper is to propose an innovative planning approach that enables a transportation fleet to maintain a sufficiently high percentage of timely‐fulfilled customer requests even in demand peak situations.
The effectiveness of the new approach is verified in computational simulation experiments. Quantifications for the system's responsiveness are proposed. Then, the quantified knowledge about the intermediate responsiveness is exploited to adjust the decision model representing the next schedule update task in a rolling horizon re‐planning.
The observed simulation results suggest the suitability of the proposed approach. An adjustment of the plan update model supports the maintenance of a high percentage of timely completed requests during and after the demand peak.
The generic approach presented and evaluated here motivates an adaptation to other more practical problem settings, in order to show its general applicability.
The proposed methodology contributes to the current demand for computational support for increasing the responsiveness of logistic systems.
The original contribution of this paper is the autonomous feedback‐controlled adjustment of decision preferences which enables a rolling horizon re‐planning framework to maintain a stable output performance even if the input oscillates significantly over time.
