Maritime transport emits approximately 940 million tonnes of CO₂ annually, a figure expected to rise significantly if effective mitigation measures are not implemented. To address this challenge, various technologies have been proposed to reduce emissions or improve vessel energy efficiency. Among them, Optimal Weather Routing (OWR) algorithms based on accurate Fuel-Oil Consumption (FOC) prediction models have gained increasing attention, as they can substantially reduce fuel usage and associated emissions.Despite their potential, the adoption of such algorithms remains limited. Many maritime operators struggle to develop reliable FOC prediction models due to the scarcity of granular operational data. This issue is particularly pronounced for older vessels, where installing advanced sensing and data-collection systems entails considerable cost and operational complexity, creating a significant barrier to adoption.In this work, we address this challenge by exploiting a reduced and readily obtainable feature set that requires no additional onboard installations. The proposed framework enables the prediction of the vessel’s Main Engine rotational speed (RPM) using only minimal monitoring information, such as that available from noon reports and AIS-derived data. To this end, we combine an analytical RPM estimation method with a Deep Learning (DL) architecture, resulting in a geometry-augmented, data-driven RPM prediction scheme that remains robust under data-limited conditions.The predicted RPM values are subsequently used to estimate FOC and to drive a computationally efficient OWR algorithm. Evaluation on real Trans-Atlantic and Trans-Pacific voyages demonstrates improved fuel efficiency and a significant reduction in computational time, particularly when adverse weather renders shortest-path routing infeasible.

As industrial water demand continues to grow, effective monitoring and management become critical for sustainable resource use. This paper explores how Digital Twins can enhance industrial water management by integrating insights from existing research with findings from five real-world case studies. We derive common functional and non-functional requirements, as well as relevant Key Performance Indicators, based on industry needs. Guided by the ISO 23247 standard, we propose a Digital Twin architecture and a supporting data model designed for water-related applications. The resulting framework helps bridge theoretical approaches with practical challenges, offering a foundation for implementing "Digital Water" solutions. Industry stakeholders and analysts can also use the identified use cases and Key Performance Indicators to align Digital Twin implementations with strategic objectives.

This paper presents optEngine, a service-oriented shell for delivering Optimization-as-a-Service (OaaS) in modern manufacturing. Designed to address the limitations of tightly coupled, solver-specific optimization systems, opt Eng i ne introduces a novel middleware architecture that supports asynchronous and synchronous optimization workflows. The optEngine system offers a generalizable and cloud-ready optimization interface that aligns with Industry 4.0 principles, i.e., scalability, interoperability, and responsiveness. It is agnostic to optimization data schemas, allowing seamless integration with heterogeneous backend engines without requiring structural modifications. Its architecture combines a web-based API, durable message queues, and a schema-flexible data model to ensure scalability, robustness, and ease of deployment. A real-world case study from the automotive industry illustrates opt Engine's application in optimizing robotic operations within a kitting system, demonstrating both responsiveness and extensibility.

This paper explores the integration of Digital Twins and smart services within Reconfigurable Manufacturing Systems to enhance adaptability, efficiency, and decision-making in modular production environments. Through four industrial case studies, we derive business and system requirements, leading to the development of a conceptual architecture that supports real-time monitoring, simulation, and optimization. The results demonstrate significant improvements in lead time, production efficiency, and operational costs, while also identifying challenges such as DT configuration complexity and interoperability issues. The proposed conceptual architecture introduces a scalable, microservices-based approach that enables distributed intelligence, seamless reconfiguration, and enhanced interoperability between production modules, marking a step forward in the evolution of flexible and data-driven manufacturing ecosystems.

Robotic arms are extensively used in production environments to undertake tasks such as welding, hemming, etc. Minimizing energy consumption of robotic systems poses a critical challenge for sustainable manufacturing. We propose using the robot's Digital Twin to obtain the energy consumption for each movement of a production cycle under different operational scenarios, i.e., different configurations for attributes such as velocity, acceleration, jerk and trajectory. Further, we develop an Integer Programming (IP) model that incorporates these scenarios and selects the ones that minimize total energy consumption. To facilitate applicability, we present a preprocessing filter that uses Pareto dominance to remove suboptimal scenarios, reducing the IP's solution space and thus vastly improving computational efficiency, as also shown in our computational experiments. Moreover, we present how we seamlessly apply our approach within the design process of robotic cells. Copyright (c) 2025 The Authors.

Liquified Petroleum Gas (LPG) is an oil refinery product that must adhere to quality specifications with respect to certain impurities. Refineries apply an LPG purification process that consists of a flow network with several process units (PUs). Current methods focus on optimising the performance of each PU separately; there exists no known approach for identifying the whole process optimum. In this paper, we present an approach for optimising the LPG purification process as a whole. We utilise operational scenarios to model the non-linear transformations of each PU. These scenarios enable us to devise a Mixed Integer Linear Program (MILP) that minimises energy consumption. The obtained solution is an approximation of the optimum but offers actionable support to refinery engineers. To enable the applicability of our approach at large scale, we propose two filters based on Pareto dominance and Data Envelopment Analysis (DEA) to identify the Pareto optimal and the Best Practice set of scenarios, respectively. By filtering out the rest, we reduce the solution space of the corresponding MILP. Further, we provide computational evidence that the application of these filters vastly improves solution time and enables applicability under real production conditions.

Reconfigurable Manufacturing Systems (RMSs) have been proposed to bolster resilience and to efficiently address rapid changes in customer needs. However, notable obstacles to their adoption still persist, such as insufficient integration into operations and inadequate incorporation of distributed control. In this paper, we present a 2-pillar conceptual architecture for enabling the operational integration of RMSs through interoperable Digital Twins (DTs) and propose avenues to enhance their capabilities with distributed and global intelligence technologies. To this end, utilizing DTs based on industrial standards promotes modularity and integrability. Moreover, it enables leveraging decision support methods and tools that may consider economic, energy and environmental aspects at different production levels (from machine level to the whole production system), thus offering a comprehensive framework to enhance adaptability, resilience, and sustainability in RMS. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

We investigate a market without money in which every agent offers indivisible goods in multiple copies, in exchange for goods of other agents. The exchange must be balanced in the sense that each agent should receive a quantity of good(s) equal to the one she transfers to others. We describe the market in graph-theoretic terms hence we use the notion of circulations to describe a balanced exchange of goods. Each agent has strict preferences over the agents from which she will receive goods and an upper bound on the quantity of each transaction, while a positive integer weight reflects the social importance of each unit exchanged. In this paper, we propose a simple variant of the Top Trading Cycles mechanism that finds a Pareto optimal circulation. We then offer necessary and sufficient conditions for a circulation to be Pareto optimal and, as a consequence, a easy recognition procedure. Last, we show that finding a maximum weight Pareto optimal circulation is NP-hard but becomes polynomial if weights are concordant with preferences.

The maritime sector is required to adhere to the IMO 2020 - mandated reduction of emissions. This reduction can be conducted by either using a compliant fuel with lower sulfur content, an alternative fuel (e.g. LNG, methanol), or clean its exhaust gasses with a "scrubber" technology to reduce the output of CO2 , NOx and SOx emissions. The objective of this paper is to present a holistic approach to continuously monitor and estimate the emissions of a vessel as well as to assess and improve the efficiency of scrubbers. Furthermore the deployment of a cutting-edge, integrated framework, incorporating the latest technological advances, that can of er the ability to capture, process and analyze vessels’ operational data in order to improve efficiency, sustainability, and rule compliance is presented. Particularly the conceptualization and materialization of a big data application suite that exploits the IoT (Internet of Things) and AI (Artificial Intelligence) advancements and technologies, to employ a “digital replica” of the en-route vessel is demonstrated. By collecting a multitude of features from on-board sensor installments, we present how we can effectively utilize these features, harvested in real time, in order to accurately assess and estimate the environmental footprint of the vessel by employing robust Fuel Oil Consumption (FOC) predictors. Then we describe in detail the streamlined procedure from data acquisition to model deployment, utilizing the proposed big data framework, in order to assess and estimate the emissions during the operational state of the vessel. Finally, we demonstrate experimental results by deploying comparative analysis utilizing operational data from one containership-centric Living Lab (LL) in order to validate and confirm our approaches in terms of accuracy and performance in a real world setting.

Starting from a critical problem in oil refineries, namely on-specs LPG production, we propose a generic mathematical programming approach that incorporates flow and blending constraints for process industries in which impurities must adhere to certain specifications. Moreover, we extend our approach to accommodate the uncertainty that may arise from the level of impurities in the input feed.

In this paper, we propose the application of matching mechanisms to handle horizontal collaboration in logistics. Based on the requirements of a real-life setting, we introduce a new variation of matching under preferences. We provide a Random Serial Dictatorship (RSD) mechanism for finding a solution that incorporates Pareto optimality, incentive compatibility and fairness, which are desired properties for enabling collaboration between antagonistic participants. Further, we present an extensive experimental analysis that is twofold. We compare the proposed RSD mechanism to two well-known matching mechanisms, namely Maximising Cardinal Utilities (MAXCU) and Bundled Probabilistic Serial (BPS). We compare these mechanisms with respect to cardinal efficiency and envy, and highlight the complementarity between them for handling different settings. Most importantly, we show that RSD is the most appropriate choice for settings similar to the one motivating our study. Moreover, we examine the effect of randomisation of RSD to fairness. Finally, we discuss how our matching mechanism was applied in the real-life context of four 3rd Party Logistics providers. Results show that the proposed approach creates significant potential for synergies and may be used to support horizontal collaboration in logistics. (c) 2022 Elsevier Ltd. All rights reserved.

The operation of any vessel includes risks, such as mechanical failure, collision, property loss, cargo loss, or damage. For modern container ships, safe navigation is challenging as the rate of innovation regarding design, speed profiles, and carrying capacity has experienced exponential growth over the past few years. Prevention of cargo loss in container ship liners is of high importance for the Maritime industry and the waterborne sector as it can lead to potentially disastrous, harmful, or even life-threatening outcomes for the crew, the shipping company, the marine environment, and aqua-culture. With the installment of onboard decision support system(s) (DSS) that will provide the required operational guidance to the vessel’s master, we aim to prevent and overcome such events. This paper explores cargo losses in container ships by employing a novel weather routing optimization DS framework that aims to identify excessive motions and accelerations caused by bad weather at specific times and locations; it also suggests alternative routes and, thus, ultimately prevents cargo loss and damage.

Route optimization has been a research topic for many years in the maritime industry and it constitutes one of the key components to improving energy efficiency and sustainability in ship operations. This paper deals with the challenge of estimating Fuel Oil Consumption (FOC) in the context of Weather Routing (WR). Given a plethora of features collected from the vessel’s Automatic Identification System (AIS) or on-board sensor installations, we examine how a predictive FOC scheme can be coupled with WR optimization algorithms in order to reduce the vessel’s FOC, emissions, and the overall cost of a voyage. In order to handle the amount of data required for FOC prediction, we employ a streaming pipeline that harvests data in real-time from different sources and processes them appropriately for visualization, causal analysis, and forecasting purposes. In this direction, we first conduct an exploratory analysis to examine and unveil the importance and inter-association between the various variables related to sea-keeping and weather features, in order to utilize them effectively in the context of a FOC predictive scheme. Furthermore, we introduce a novel recurrent neural network architecture that approximates ideally the underlying function describing the features and the vessel’s FOC by taking into account historical data, and we showcase the results. Finally, we demonstrate how the FOC prediction model can be coupled with a WR algorithm to propose the optimal route for a vessel in terms of FOC efficiency.

Digital Twins (DTs) are a core enabler of Industry 4.0 in manufacturing. Cognitive Digital Twins (CDTs), as an evolution, utilize services and tools towards enabling human-like cognitive capabilities in DTs. This paper proposes a conceptual framework for implementing CDTs to support resilience in production, i.e., to enable manufacturing systems to identify and handle anomalies and disruptive events in production processes and to support decisions to alleviate their consequences. Through analyzing five real-life production cases in different industries, similarities and differences in their corresponding needs are identified. Moreover, a connection between resilience and cognition is established. Further, a conceptual architecture is proposed that maps the tools materializing cognition within the DT core together with a cognitive process that enables resilience in production by utilizing CDTs.

We investigate a market without money in which agents can offer certain goods (or multiple copies of an agent-specific good) in exchange for goods of other agents. The exchange must be balanced in the sense that each agent should receive a quantity of good(s) equal to the one she transfers to others. In addition, each agent has strict preferences over the agents from which she will receive goods, and there is an upper bound on the volume of each transaction and a weight reflecting its social importance or its cardinal utility for the two agents. We propose a simple variant of the Top Trading Cycles mechanism that finds a Pareto optimal balanced exchange. We then offer necessary and sufficient conditions for a balanced exchange to be Pareto optimal and exploit these to obtain a recognition procedure. This procedure can detect whether a given exchange is Pareto optimal and, if not, improve it to become Pareto optimal in polynomial time. Last, we show how to obtain a Pareto optimal balanced exchange of maximum weight in two special cases.

Manufacturing systems are often prone to disruptions that break the continuity of operations and prevent them from reaching their planned performance. This paper presents a Situation-Aware Manufacturing System framework that is applied to identify and predict disruptions, to evaluate their impact and to react timely to repair the affected processes, by coupling the capabilities of contemporary Industry 4.0 technologies, predictive analytics, simulation, and optimisation tools. The proposed framework is built upon the decision-making model based on situation awareness and includes perceiving and comprehending the current state of production elements and of the production system, projecting its future situation, deciding on corrective actions and implementing them. To establish both coherence and transferability, a design science research approach has been utilised to frame the scope of disruption identification and handling, categorise the user requirements, and validate the suggested design in the actual field. In this regard, the paper reports results from two real-life manufacturing instantiations that enable the evaluation of the proposed framework in both quantitative and qualitative terms.

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables the provision of equipment state monitoring services and the generation of decision-making for equipment operations. In this paper, we propose two machine learning models: (1) to forecast the amount of pentane (C5) content in the final product mixture; (2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.

In the era of Industry 4.0, Digital Twins (DTs) pave the way for the creation of the Cognitive Factory. By virtualizing and twinning information stemming from the real and the digital world, it is now possible to connect all parts of the production process by having virtual copies of physical elements interacting with each other in the digital and physical realms. However, this alone does not imply cognition. Cognition requires modelling not only the physical characteristics but also the behavior of production elements and processes. The latter can be founded upon data-driven models produced via Data Analytics and Machine Learning techniques, giving rise to the so-called Cognitive (Digital) Twin. To further enable the Cognitive Factory, a novel concept, dubbed as Enhanced Cognitive Twin (ECT), is proposed in this paper as a way to introduce advanced cognitive capabilities to the DT artefact that enable supporting decisions, with the end goal to enable DTs to react to inner or outer stimuli. The Enhanced Cognitive Twin can be deployed at different hierarchical levels of the production process, i.e., at sensor-, machine-, process-, employee- or even factory-level, aggregated to allow both horizontal and vertical interplay. The ECT notion is proposed in the context of process industries, where cognition is particularly important due to the continuous, non-linear, and varied nature of the respective production processes.

An upper bound on the diameter of the Stable Matching (Stable Marriage) polytope is known to be ⌊n2⌋ where n is the number of men (or women) involved in the matching. The current work complements that result by providing a lower bound and an algorithm computing it. It also presents a class of Stable Matching instances for which the lower bound coincides with the above-mentioned upper bound.