Mixed-fleet single-terminal bus scheduling problem: Modelling, solution scheme and potential applications. (October 2020)

• Record Type: Journal Article
• Authors: Rinaldi, Marco; Picarelli, Erika; D'Ariano, Andrea; Viti, Francesco
• Is Part Of: Omega. Volume 96(2020)
• Publication Date: 2020-10
• Subjects: Public transport optimization -- Vehicle scheduling -- Mixed-fleet -- MILP -- Decomposition scheme; Management -- Periodicals; 658.4005
• DOI: 10.1016/j.omega.2019.05.006
• Languages: English
• ISSNs: 0305-0483

Abstract

Highlights: We extend our previous mixed-fleet scheduling model with optimized memory requirements, addressing real-size instances. We introduce an ad-hoc decomposition scheme to solve problems with an arbitrary number of trips, fleet size and composition. We validate the proposed MILP and solution methods through two test cases arising in the city of Luxembourg. We perform sensitivity analysis of the model's solutions to degree of decomposition, fleet size and fleet composition. We numerically assess the proposed MILP and solution method's scalability capabilities. Abstract: Reducing pollutant emissions and promoting sustainable mobility solutions, including Public Transport (PT), are increasingly becoming key objectives for policymakers worldwide. In this work we develop an optimal vehicle scheduling approach for next generation PT systems, considering the instance of mixed electric / hybrid fleet. Our objective is that of investigating to what extent electrification, coupled with optimal fleet management, can yield operational cost savings for PT operators. We propose a Mixed Integer Linear Program (MILP) to address the problem of optimal scheduling of a mixed fleet of electric and hybrid / non-electric buses, coupled with an ad-hoc decomposition scheme aimed at enhancing the scalability of the proposed MILP. Two case studies arising from the PT network of the city of Luxembourg are employed in order to validate the model; sensitivity analysis to fleet design parameters is performed, specifically in terms of fleet size and fleet composition. Conclusions point to the fact that careful modelling and handling of mixed-fleet conditions are necessary to achieve operational savings, and that marginal savings gradually reduce as more conventional buses are replaced by their electric counterparts. We believe the methodology proposed may be a key part of advanced decision support systems for policymakers and operators that are dealing with the on-going transition from conventional bus fleets towards greener transport solutions.