Bus Operations Scheduling Subject to Resource Constraints Using Evolutionary Optimization

Gkiotsalitis, Konstantinos; Kumar, R. Saravana

doi:10.3390/informatics5010009

Cited by 10 publications

(8 citation statements)

References 31 publications

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“…Several research works [167,171] have developed dynamic bus scheduling schemes to improve the transit experience of passengers, with the common objective being minimizing passenger wait times. However, many existing models estimate passenger wait time without considering the limited capacity of buses [168,219]. During peak hours particularly, certain passengers (depending on the queue length and their location in the queue) may need to wait for more than two buses before they get on board.…”

Section: Optimisation Formulationmentioning

confidence: 99%

Modelling and Optimisation of Resource Usage in an IoT Enabled Smart Campus

Sutjarittham¹

2021

Preprint

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University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilised efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organisations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory.I avail this opportunity to thank all my closed friends for making my time as a Ph.D. candidate an enjoyable one. Their delightful friendship and hospitality have always mitigated my stress during this hard time. Special thank goes to Amarin Siripanich, for his love and support. v Words cannot express my gratitude to my family, especially my parents and siblings, whose unconditional love and supports have given me strength to pursue my dream. Thank you. vi

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Section: Optimisation Formulationmentioning

confidence: 99%

Modelling and Optimisation of Resource Usage in an IoT Enabled Smart Campus

Sutjarittham¹

2021

Preprint

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“…Shen, Xu, and Li (2016) studied a transit-vehicle-scheduling problem with a novel probabilistic-delay-propagation model. Gkiotsalitis and Kumar (2018) is the convergence properties of their two-way-looking (upstream and downstream)…”

Section: Literature Reviewmentioning

confidence: 99%

A multiobjective single bus corridor scheduling using machine learning-based predictive models

Chen

Bai

et al. 2020

International Journal of Production Research

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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-Contact R.

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“…S CHEDULING the dispatching times of bus trips is a sub-problem of the tactical planning phase. This problem follows the stages of frequency settings and vehicle allocation [1]- [4]. After setting the dispatching times of trips, service operators may apply control strategies in real time such as holding, stop-skipping or dispatching time adjustments [5]- [11].…”

Section: Introductionmentioning

confidence: 99%