Abstract

The aim of the bi-objective multimodal car-sharing problem (BiO-MMCP) is to determine the optimal mode of transport assignment for trips and to schedule the routes of available cars and users whilst minimizing cost and maximizing user satisfaction. We investigate the BiO-MMCP from a user-centred point of view. As user satisfaction is a crucial aspect in shared mobility systems, we consider user preferences in a second objective. Users may choose and rank their preferred modes of transport for different times of the day. In this way, we account for, e.g., different traffic conditions throughout the planning horizon. We study different variants of the problem. In the base problem, the sequence of tasks a user has to fulfil is fixed in advance and travel times as well as preferences are constant over the planning horizon. In variant 2, time-dependent travel times and preferences are introduced. In variant 3, we examine the challenges when allowing additional routing decisions. Variant 4 integrates variants 2 and 3. For this last variant, we develop a branch-and-cut algorithm which is embedded in two bi-objective frameworks, namely the epsilon -constraint method and a weighting binary search method. Computational experiments show that the branch-and cut algorithm outperforms the MIP formulation and we discuss changing solutions along the Pareto frontier.

Highlights

  • Today, most of the world’s population lives in urban environments and cities continue to grow (United Nations-Department of Economic and Social Affairs 2018)

  • We observe that only dissolving the fixed sequence does not come with high improvements, but in combination with time dependencies a greater amount of solutions as well as lower cost and better user satisfaction are obtained

  • Inspired by the change in mobility patterns, we study the bi-objective multimodal car-sharing problem where we assign modes of transport to trips as well as cars and user routes

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Summary

Introduction

Most of the world’s population lives in urban environments and cities continue to grow (United Nations-Department of Economic and Social Affairs 2018). By using car-sharing, resources can be employed more efficiently, it is more environmentally friendly, and newly available space can be gained as, e.g., green space in urban areas (VCÖ - Mobilität der Zukunft 2020). The target is to reduce a one-to-one assignment of company cars, employ more environmentally friendly MOTs and strive for shared mobility where each employee gets her preference. This goes hand in hand with companies aiming for a greener carbon footprint and enhancing employee satisfaction (SEAMLESS 2020). We study the bi-objective multimodal car-sharing problem where we assign MOTs to trips and find car and, depending on the variant, user routes throughout a day.

Related work
Problem description
Formal description
Solution approach
Valid inequalities
Branch‐and‐cut for m4b
Separation of infeasible user routes
Separation of infeasible car routes
Synchronization of routes
Strengthened infeasible path constraints
The ‐constraint method
A weighting binary search method
Computational study
Test instances
Enhancements and preprocessing
Algorithmic tests
Introducing valid inequalities for model m3
Solving model m4
Findings
Conclusion and future work
Full Text
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