Smart Computing and Systems Engineering - 2021 (SCSE 2021)

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/25343

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Has files: YesSubject: search.filters.subject.indoor positioning, Internet of Things (IoT), Supervised Learning

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    Solution approach to incompatibility of products in a multi-product and heterogeneous vehicle routing problem: An application in the 3PL industry
    (Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Weerakkody, H. D. W.; Niwunhella, D. H. H.; Wijayanayake, A. N.
    Vehicle Routing Problem (VRP) is an extensively discussed area under supply chain literature, though it has variety of applications. Multi-product related VRP considers about optimizing the routes of vehicles distributing multiple commodities. Domestic distribution of goods of multiple clients from a third-party logistics distribution center (DC) is one example of such an application. Compatibility of products is a major factor taken into consideration when consolidating and distributing multiple products in the same vehicle. From the literature, it was identified that, though compatibility is a major consideration, it has not been considered in the literature when developing vehicle routing models. Therefore, this study has been carried out with the objective of minimizing the cost of distribution in the multi-product VRP while considering the compatibility of the products distributed, using heterogeneous vehicle types. The extended mathematical model proposed has been validated using data obtained from a leading 3PL firm in Sri Lanka which has been simulated using the Supply Chain Guru software. The numerical results showcase that cost has been reduced when consolidating shipments in a 3PL DC. The study will contribute to literature with the finding that the compatibility factor of products can be considered when developing vehicle routing models for the multi-product related VRP.
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    Comparison of supervised learning-based indoor localization techniques for smart building applications
    (Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Maduraga, M. W. P.; Abeysekara, Ruvan
    Smart buildings involve modern applications of the Internet of Things (IoT). Intelligent buildings could include applications based on indoor localization, such as tracking the real-time location of humans inside the building using sensors. Mobile sensor nodes can emit electromagnetic signals in an ambient sensor network, and fixed sensors in the same network can detect the Received Signal Strength (RSS) from its mobile sensor nodes. However, many works exist for RSS-based indoor localization that use deterministic algorithms. It's complicated to suggest a generated mechanism for any indoor localization application due to the fluctuation of RSSI values. This paper has investigated supervised machine learning algorithms to obtain the accurate location of an object with the aid of Received Signal Strengths Indicator (RSSI) values measured through sensors. An available RSSI data set was trained using multiple supervised learning algorithms to predict the location and their average algorithm errors were compared.