IoT Analytics and Agile Optimization for Solving Dynamic Team Orienteering Problems with Mandatory Visits

Li, Yuda; Peyman, Mohammad; Panadero, Javier; Juan, Angel A.; Xhafa, Fatos · 2022 · DOAJ

DOI: 10.3390/math10060982

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Summary

This paper addresses the challenge of integrating Internet of Things (IoT) analytics into agile optimization frameworks to solve real-time routing problems in smart cities. The authors focus on the Dynamic Team Orienteering Problem with Mandatory Visits (DTOP-MV), using waste collection as a realistic case study. Traditional optimization approaches often assume static inputs, such as fixed travel times and pre-selected nodes, which fails to account for the dynamic nature of urban environments. The research is motivated by the need for efficient, sustainable transport and mobility systems that can leverage continuous data streams from IoT devices—such as sensors monitoring bin saturation levels and traffic cameras—to adapt routing decisions in real time. The methodology involves modeling the waste collection process in Barcelona as a DTOP-MV, where a fleet of vehicles must visit mandatory nodes (bins requiring collection) while maximizing rewards from optional visits within a time or distance limit. The authors propose a dynamic solving approach that combines a planning horizon with a trigger mechanism. This system periodically re-computes vehicle routes as new data becomes available from open city repositories. The optimization is performed using an agile biased-randomized heuristic, which processes updated traffic and bin status information in milliseconds. This dynamic approach is contrasted with a traditional static method, where routes are calculated only once at the beginning of the operation without subsequent adjustments. Computational experiments demonstrate that the proposed dynamic approach significantly outperforms the static method in terms of accumulated rewards. By incorporating real-time IoT analytics, the system effectively adapts to changing traffic conditions and bin statuses, leading to more efficient routing. The results highlight the effectiveness of using agile optimization algorithms fed by continuous data streams to handle uncertainty and dynamics in urban logistics. The study confirms that re-optimizing routes periodically based on live data yields better operational outcomes than relying on initial static plans. The significance of this work lies in its contribution to the field of smart city logistics and IoT-driven optimization. It provides a practical framework for integrating cognitive, descriptive, predictive, and prescriptive analytics into vehicle routing problems. The findings suggest that leveraging open data and IoT platforms can reduce pollution, energy consumption, and operational costs in municipal services like waste management. Furthermore, the paper establishes a foundation for applying similar agile optimization techniques to other dynamic urban challenges, such as ride-sharing and electric vehicle charging, thereby supporting the development of more sustainable and responsive smart city infrastructures.

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