Predictive maintenance of baggage handling conveyors using IoT

This article discusses issues related to the maintenance of airports’ baggage handling systems and assesses the feasibility of using predictive maintenance instead of periodic maintenance. The unique issues related to baggage handling systems are discussed — namely random noise captured by the IoT sensors due to the movement of the luggage and complex interconnected components that constitute the conveyors. The paper presents a scalable and economical maintenance 4.0 solution for such a system using data from sensors installed (on a live system in absence of historical data). Differentiating between anomaly detection and outlier detection the paper presents an algorithm that can be used to remove idle and noisy data from the datasets. Using integrated machine learning approaches, it tries to detect and diagnose incumbent defects in the early stage to avoid breakdowns. The paper proposes an automated machine-learning pipeline by processing unstructured industrial data. The performance of various machine learning algorithms on the collected data is compared. Finally, the paper discusses avenues for future research

Optimal allocation of near-expiry food in a retailer-foodbank supply network with economic and environmental considerations: an aggregator's perspective

Wastage of perishable food products is a severe concern to society and needs to be addressed to ensure food security for all. Moreover, the food waste when sent to landfills, decomposes to produce greenhouse gases like methane and carbon dioxide. The emergence of food banks and aggregators has abated the problem of food wastage to a certain extent. An aggregator, which connects the retailers to the food banks, plays a critical role in ensuring that the food reaches the food banks on time. However, to ensure food security and reduce wastage of food, it is essential that food aggregators remain profitable. The aggregator has to determine the number of heterogeneous vehicles to hire from the market and allocate them their route on a daily basis depending on donations committed by the retailers and also take into account potential environmental impact from the decomposition of food waste and carbon emitted from hired vehicles. Hence, we propose decision support for aggregators, using data from an aggregator based in Turkey, which can help in reducing food wastage by allocating the donated food items from retailers to food banks while maximizing the profitability of the aggregator and minimizing the environmental impact. We have also analyzed how the availability of different types of vehicles can impact the aggregator's profit. Furthermore, the effect of various model parameters such as transportation cost, and percentage of retailers' gain paid to the aggregator on the total profit along with the impact of distances on types of vehicles hired is also analyzed. We have compared two strategies that the aggregator could possibly employ and generate managerial insights