Optimizing the Scheduling of Electrified Public Transport System in Malta

Author:

Sharma Satish1ORCID,Bhattacharya Somesh2ORCID,Kiran Deep3ORCID,Hu Bin4ORCID,Prandtstetter Matthias4ORCID,Azzopardi Brian56ORCID

Affiliation:

1. Department of Electrical Engineering, Malaviya National Institute of Technology Jaipur, Jaipur 302017, India

2. Department of Electrical Engineering, Faculty of Engineering, University of Malta, MSD 2080 Msida, Malta

3. Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India

4. Austrian Institute of Technology, 1210 Vienna, Austria

5. MCAST Energy Research Group, Institute of Engineering and Transport, Malta College of Arts, Science and Technology (MCAST), Main Campus, Corradino Hill, PLA 9032 Paola, Malta

6. The Foundation for Innovation and Research—Malta, 65 Design Centre Level 2, Tower Road, BKR 4012 Birkirkara, Malta

Abstract

In this paper, we describe a comparative analysis of a bus route scheduling problem as part of timetable trips. We consider the current uptake of electric buses as a viable public transportation option that will eventually phase out the diesel-engine-based buses. We note that, with the increasing number of electric buses, the complexity related to the scheduling also increases, especially stemming from the charging requirement and the dedicated infrastructure behind it. The aim of our comparative study is to highlight the brevity with which a multi-agent-system-based scheduling method can be helpful as compared to the classical mixed-integer linear-programming-based approach. The multi-agent approach we design is centralized with asymmetric communication between the master agent, the bus agent, and the depot agent, which makes it possible to solve the multi-depot scheduling problem in almost real time as opposed to the classical optimizer, which sees a multi-depot problem as a combinatorial heuristic NP-hard problem, which, for large system cases, can be computationally inefficient to solve. We test the efficacy of the multi-agent algorithm and also compare the same with the MILP objective designed in harmony with the multi-agent system. We test the comparisons first on a small network and then extend the scheduling application to real data extracted from the public transport of the Maltese Islands.

Funder

European Commission’s Horizon 2020 Twinning project, “Networking for Excellence in Electric Mobility Operations (NEEMO)”

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

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