Модели и методы машинного обучения для решения задач оптимизации и прогнозирования работы морских портов

Machine learning techniques have made significant advances and expanded application sphere over the past decade to include problems of port operations. This happened due to the growing amount of data available cargo ports. We review the literature on models and methods of machine learning and their application to optimization of port operations. A special attention is paid to the port planning and development a wide range of topics in port operations, including port planning and development, their safety and security, water and land port operations.За последнее десятилетие существенно улучшились методы машинного обучения и расширилась сфера их применения, которая дополнилась рядом операционных задач, возникающих в грузовых портах. Это связано с накоплением и возможностью использования имеющихся в грузовых портах больших объемов данных. Статья посвящена обзору литературы по моделям и методам машинного обучения и их применению к оптимизации портовых операций. Основное внимание уделено планированию и развитию портов, их безопасности и охране, водным и сухопутным портовым операциям

Early detection of vessel delays using combined historical and real-time information

In ocean transportation, detecting vessel delays in advance or in real time is important for fourth-party logistics (4PL) in order to fulfill the expectations of customers and to help customers reduce delay costs. However, the early detection of vessel delays faces the challenges of numerous uncertainties, including weather conditions, port congestion, booking issues, and route selection. Recently, 4PLs have adopted advanced tracking technologies such as satellite-based automatic identification systems (S-AISs) that produce a vast amount of real-time vessel tracking information, thus providing new opportunities to enhance the early detection of vessel delays. This paper proposes a data-driven method for the early detection of vessel delays: in our new framework of refined case-based reasoning (CBR), real-time S-AIS vessel tracking data are utilized in combination with historical shipping data. The proposed method also provides a process of analyzing the causes of delays by matching the tracking patterns of real-time shipments with those of historical shipping data. Real data examples from a logistics company demonstrate the effectiveness of the proposed method.ope

How do incumbent energy firms realise the potential for value creation from the adoption of smart meters? A study of organisational affordances, organisational capabilities, and generative mechanisms in the UK energy sector

Big data technologies are advanced technologies that enable data to be collected in real-time at large volume and at low cost. Anecdotal evidence suggests that insights derived from big data have the potential to transform business strategies and business models and thereby improve marketing, product and service development, human resources, operations, and other core business functions. As such, there has been significant academic and practitioner interest in studying big data and value creation in organisations. However, much of the previous research has focused on studying the relationship between big data resources and investments and their impact on firm performance, and therefore have overlooked the processes and mechanisms through which firms realise the value creation potential from big data technologies. Lack of knowledge and understanding on how organisations realise the value creation potential may explain why some organisations still fail to reach their strategic goals despite investing substantial resources into big data technologies. Against this backdrop, this thesis aims to address this research gap by seeking to understand how organisations realise the value creation potential from adopting a specific big data technology (i.e., smart meters) in the UK energy sector. To do so, I collect qualitative data (interviews, company documents, news articles, governments reports, and publicly available documents) in two case study organisations: BlueHouse and GreenWorks (anonymised names). Using affordance theory lens, I provide empirically grounded insights into the opportunities for value creation that smart meters provide (organisational affordances), explain how these value creation opportunities are realised (organisational capabilities and actualisation enablers), and explain what constrains these value creation opportunities from being realised (generative mechanisms). My findings extend affordance theory by empirically examining the role of organisational capabilities and generative mechanisms in the affordance actualisation process. They also provide meaningful insights on the process of realising the value creation potential from the adoption of a new technology within incumbent firms