Journal
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Volume 61, Issue 1, Pages 131-145Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2020.1766716
Keywords
bus scheduling; multi-objective optimisation; combinatorial optimisation; machine learning
Ask authors/readers for more resources
This paper discusses the current situation and limitations of current methods for addressing uncertainties in real-life optimization problems. The authors propose a novel framework that combines mathematical models and machine learning modules to overcome these limitations and demonstrate its practicality and feasibility through real-life and artificial bus scheduling instances. The proposed framework represents the first multi-objective bus-headway-optimisation method for non-timetabled bus schedules with major practical constraints being considered.
Many real-life optimisation problems, including those in production and logistics, have uncertainties that pose considerable challenges for practitioners. In spite of considerable efforts, the current methods are still not satisfactory. This is primarily caused by a lack of effective methods to deal with various uncertainties. Existing literature comes from two isolated research communities, namely the operations research community and the machine learning community. In the operations research community, uncertainties are often modelled and solved through techniques like stochastic programming or robust optimisation, which are often criticised for their over conservativeness. In the machine learning community, the problem is formulated as a dynamic control problem and solved through techniques like supervised learning and/or reinforcement learning, which could suffer from being myopic and unstable. In this paper, we aim to fill this research gap and develop a novel framework that takes advantages of both short-term accuracy from mathematical models and high-quality future forecasts from machine learning modules. We demonstrate the practicality and feasibility of our approach for a real-life bus scheduling problem and two controlled bus scheduling instances that are generated artificially. To our knowledge, the proposed framework represents the first multi-objective bus-headway-optimisation method for non-timetabled bus schedule with major practical constraints being considered. The advantages of our proposed methods are also discussed, along with factors that need to be carefully considered for practical applications.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available