Machine Learning for Variable Cost and Size Bin Packing Problem

Nowadays, third-party logistics is an essential component of efficient delivery, enabling companies to purchase carrier services instead of keeping an expensive fleet of vehicles. However, the contracts with the carriers usually have to be booked beforehand when the delivery demand is unknown. This led to the managerial task of choosing an appropriate set of bins (fleet contracts) under uncertainty. Such a decision problem is defined as the Variable Cost and Size Bin Packing Problem with Stochastic Items [1]. It consists of packing the set of items (goods) with uncertain volumes and quantity into containers (bins) of different fixed costs and capacities. Since this problem cannot be solved for large realistic instances by means of exact solvers, this paper introduces a Machine Learning heuristic to approximate the first stage decision variables. Several numerical experiments are outlined to show the effectiveness of the proposed approach to deal with realistic instances of up to 3000 items. Moreover, different classification approaches are compared to gain insight into heuristic performance to deal with the outlined problem. [1] Crainic, T. G., Gobbato, L., Perboli, G., Rei, W., Watson, J. P., & Woodruff, D. L. (2014). Bin packing problems with uncertainty on item characteristics: An application to capacity planning in logistics. Procedia-Social and Behavioral Sciences, 111, 654-66

The European Concept of Smart City: A Taxonomic Analysis

The concept of “Smart City” became widely debated, including different components for building a truly sustainable urban environment. In the literature, there is a huge number of contributions inherent to the definition of a smart city, however, a broad view of the field is still missing. The aim of this paper is twofold. Firstly, to provide a repeatable and scalable methodology that can be applied to unstructured documents on smart cities projects considering all the multi-facet aspects of a smart city (e.g., business model, technology). Secondly, to propose an analysis carried out with a taxonomy to a database of 25 outstanding smart city projects in Europe, to discuss the current direction in which they are moving, identifying success factors and analyzing new trends and future paths

Machine Learning heuristic for Variable Cost and Size Bin Packing Problem with Stochastic Items

Third-party logistics becomes an essential component of efficient delivery, enabling companies to rent transportation services instead of keeping an expensive fleet of vehicles. However, the contracts with the carriers usually have to be booked beforehand when the delivery demand is unknown. This decision process is strongly affected by uncertainty, provided with a long (tactical) planning horizon, and can be expressed as choosing an appropriate set of bins (fleet contracts). Formally, it can be modeled as the Variable Cost and Size Bin Packing Problem with Stochastic Items [1]. It consists of packing the set of items (goods) with uncertain volumes and quantities into containers (bins) of different fixed costs and capacities. This problem is described via a two-stage stochastic programming approach, where the cost of the bins of the second stage is significantly higher. Since it cannot be solved for large realistic instances by means of exact solvers for a reasonable time and memory consumption, this paper introduces a Machine Learning heuristic to approximate the first stage decision variables. Several numerical experiments are outlined to show the effectiveness of the proposed approach to deal with realistic instances of up to 3000 items. Further, the proposed heuristic is compared to the recent Progressive Hedging-based heuristic and showed a significant computational time reduction. Finally, different classification approaches are compared, and the feature selection process is explained to gain insight into heuristic performance to deal with the outlined problem. [1] Crainic, T. G., Gobbato, L., Perboli, G., Rei, W., Watson, J. P., & Woodruff, D. L. (2014). Bin packing problems with uncertainty on item characteristics: An application to capacity planning in logistics. ProcediaSocial and Behavioral Sciences, 111, 654-662

Efficient Kernel-Based Subsequence Search for Enabling Health Monitoring Services in IoT-Based Home Setting

This paper presents an efficient approach for subsequence search in data streams. The problem consists of identifying coherent repetitions of a given reference time-series, also in the multivariate case, within a longer data stream. The most widely adopted metric to address this problem is Dynamic Time Warping (DTW), but its computational complexity is a well-known issue. In this paper, we present an approach aimed at learning a kernel approximating DTW for efficiently analyzing streaming data collected from wearable sensors, while reducing the burden of DTW computation. Contrary to kernel, DTW allows for comparing two time-series with different length. To enable the use of kernel for comparing two time-series with different length, a feature embedding is required in order to obtain a fixed length vector representation. Each vector component is the DTW between the given time-series and a set of "basis" series, randomly chosen. The approach has been validated on two benchmark datasets and on a real-life application for supporting self-rehabilitation in elderly subjects has been addressed. A comparison with traditional DTW implementations and other state-of-the-art algorithms is provided: results show a slight decrease in accuracy, which is counterbalanced by a significant reduction in computational costs

Моделювання тренувань силової спрямованості для вдосконалення процесу ударної підготовки в хортингу

Мета роботи – виявити перспективність використання ефективних для силових видів спорту основних видів моделей тренувальних занять для підвищення рівня спеціальної ударної підготовки спортсменів із хор- тингу. Методи. Контингент обстежених складався із 60 спортсменів. Для кожної з трьох створених груп запропоновано окрему модель занять. Відмінність між моделями тренувань полягала у використанні різного варіанта поєднання вправ на тренажерах чи з вільною вагою обтяжень. При цьому використовувалася різна послідовність виконання базових й ізолюючих вправ. Контроль за змінами показників ударної підготовки (кількість влучно виконаних ударів ногами за 15 с із максимальною силою) відбувався на початку дослідження та протягом наступних трьох місяців із періодичністю в 30 днів. Результати. Установлено, що за три місяці досліджень найбільше підвищення рівня ударної підготовки на 28,8 % виявлено під час контрольної вправи «кількість влучних прямих ударів коліном задньої ноги з однобічної бойової з максимальною силою за 15 с» у спортсменів другої групи. Відсутність кількісних змін досліджуваних показників простежено лише в спорт- сменів першої групи під час виконання таких вправ, як удар ногою знизу підйомом ступні в голову, короткий задній удар ногою. У спортсменів третьої групи, які використовували комбіновану модель тренувань, у порів- нянні з представниками інших груп, досліджувані показники підвищились у середньому на 11,2 % у зіставленні з вихідними даними. Висновки. Модель силової спрямованості, в основі якої використовуються комплекси вправ зі штангою та гантелями в умовах анаеробно-алактатного режиму енергозабезпечення та навантаженнями 85 % від 1RМ, згідно з результатами проведеного дослідження, є найбільш ефективною

A machine learning optimization approach for last-mile delivery and third-party logistics

Third-party logistics is now an essential component of efficient delivery systems, enabling companies to purchase carrier services instead of an expensive fleet of vehicles. However, carrier contracts have to be booked in advance without exact knowledge of what orders will be available for dispatch. The model describing this problem is the variable cost and size bin packing problem with stochastic items. Since it cannot be solved for realistic instances by means of exact solvers, in this paper, we present a new heuristic algorithm able to do so based on machine learning techniques. Several numerical experiments show that the proposed heuristics achieve good performance in a short computational time, thus enabling its real-world usage. Moreover, the comparison against a new and efficient version of progressive hedging proves that the proposed heuristic achieves better results. Finally, we present managerial insights for a case study on parcel delivery in Turin, Italy

Natural Language Processing for the Identification of Human Factors in Aviation Accidents Causes: An Application to the SHEL Methodology

Accidents in aviation are rare events. From them, aviation safety management systems take fast and effective remedy actions by performing the analysis of the root causes of accidents, most of them are proved to be human factors. Since the current standard relies on the manual classification performed by trained staff, there are no technical standards already defined for automated human factors identification. This paper considers this issue, proposing machine learning techniques by leveraging on the state-of-the-art technologies of Natural Language Processing. The techniques are then adapted to the SHEL standard accident causality model and tested on a set of real accidents. Computational results show the accuracy and effectiveness of the proposed methodology, which leads to a possible reduction of time and costs up to 30%