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Integrated Planning of IoT-Based Smart Bin Allocation and Vehicle Routing in Solid Waste Management

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Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems (APMS 2021)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 632))

Abstract

The internet of things (IoT) is a prominent modern technology that offers robust solutions to modernizing consecutive systems. It accords controlled and calibrated outcomes to streamline smart cities, smart homes, smart industries, and smart environments. In this study, an ultrasonic sensor-based waste filling level is considered on IoT-based waste bins to optimize dynamic routes instead of fixed routes, such that the efficiency of waste collection and transportation can be improved. This article illustrates the time-dependent penalty concept to waste management authorities if these smart bins are not emptied in time after becoming full. This article presents a smart waste management model for smart cities that takes into account both bin allocation costs and routing costs. An innovative meta-heuristic neighborhood search technique is developed to solve the above model. The proposed model is illustrated with some numerical data, and a sensitivity analysis is performed with some parameters. After the waste from smart waste bins is collected, some waste products are recycled and reused through application of the game-theoretic concept involving the South Korean aspect of waste management.

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Acknowledgement

This research is supported by the Brain Pool Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT, and Future Planning [Grant No. NRF-2020H1D3A2A01085443].

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Correspondence to Ilkyeong Moon .

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Roy, A., Manna, A., Kim, J., Moon, I. (2021). Integrated Planning of IoT-Based Smart Bin Allocation and Vehicle Routing in Solid Waste Management. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 632. Springer, Cham. https://doi.org/10.1007/978-3-030-85906-0_55

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  • DOI: https://doi.org/10.1007/978-3-030-85906-0_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85905-3

  • Online ISBN: 978-3-030-85906-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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