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.

Water scarcity and the low quality of wastewater produced in industrial applications present significant challenges, particularly in managing fresh water intake and reusing residual quantities. These issues affect various industries, compelling plant owners and managers to optimise water resources within their process networks. To address this cross-sector business requirement, we propose a Decision Support System (DSS) designed to capture key network components, such as inlet streams, processes, and outlet streams. Data provided to the DSS are exploited by an optimisation module, which supports both network design and operational decisions. This module is coupled with a generic mixed-integer nonlinear programming (MINLP) model, which is linearised into a compact mixed-integer linear programming (MILP) formulation capable of delivering fast optimal solutions across various network designs and input parameterisations. Additionally, a Constraint Programming (CP) approach is incorporated to handle nonlinear expressions through straightforward modeling. This state-of-the-art generalised framework enables broad applicability across a wide range of real-world scenarios, setting it apart from the conventional reliance on customised solutions designed for specific use cases. The proposed framework was tested on 500 synthetic data instances inspired by historical data from three case studies. The obtained results confirm the validity, computational competence and practical impact of our approach both among their operational and network design phases, demonstrating significant improvements over current practices. Notably, the proposed approach achieved a 17.6% reduction in freshwater intake in a chemical industry case and facilitated the reuse of nearly 90% of wastewater in an oil refinery case.

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.

Shipbrokers play a key role in maritime industry by acting as intermediates between shipping companies and the market. They undertake various chartering, buying or selling operations. In this paper, we propose a mathematical programming approach for the evaluation and selection of shipbrokers. Specifically, the score of each ship broker is a composite measure that is derived by aggregating a set of performance criteria, e.g., reputation, etc. The developed mathematical programming models enable the aggregation and weighting of the criteria. We employ three optimization models to explore the effect of different weighting schemes on the scores and ranking of the shipbrokers. The models that provide a common set of weights for all the shipbrokers establish the appropriate ground for comparisons among them. Also, our models facilitate the incorporation of user priorities over the criteria in the form of weight restrictions. The proposed approach is illustrated by assessing seven shipbroker offers for selling a dry-bulk ship using four criteria, namely revenue, brokerage fee, brokerage time and terms & conditions.

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/)

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.

In this paper we describe a scenario from the Shipping industry, that employs analytics, stream processing, monitoring, alerting and vessel route optimization over big data. This includes the business process modelling, infrastructure management and monitoring along with dimensioning and deployment of focused services requiring different stakeholders roles for their parameterization and enactment. Apart from analysing the domain requirements and user roles, we show how BigDataStack, i.e., a high-performance data-centric stack for big data applications and operations, incorporates, supports and facilitates all these requirements.

One of the key challenges in the maritime industry refers to minimizing the time a vessel cannot be utilized, which has multiple effects. The latter is addressed through maintenance approaches that however in many cases are not efficient in terms of cost and downtime. Predictive maintenance provides optimized maintenance scheduling offering extended vessel lifespan, coupled with reduced maintenance costs. As in several industries, including the maritime domain, an increasing amount of data is made available through the deployment and exploitation of data sources, such as on board sensors that provide real-time information. These data provide the required ground for analysis and thus support for various types of data-driven decision making. In the maritime domain, sensors are deployed on vessels to monitor their engines and data analysis tools are needed to assist engineers towards reduced operational risk through predictive maintenance solutions that are put in place. In this paper, we present an approach for anomaly detection on time-series data, utilizing machine learning on the vessels sensor data, in order to predict the condition of specific parts of the vessel's main engine and thus facilitate predictive maintenance. The novel characteristic of the proposed approach refers both to the inclusion of new innovative models to address the case of predictive maintenance in maritime and the combination of those different models, highlighting an improved result in terms of evaluation metrics.

The new data-driven industrial revolution highlights the need for big data technologies to unlock the potential in various application domains. In this context, emerging innovative solutions exploit several underlying infrastructure and cluster management systems. However, these systems have not been designed and implemented in a "big data context", and they rather emphasize and address the computational needs and aspects of applications and services to be deployed. In this paper we present the architecture of a complete stack (namely BigDataStack), based on a frontrunner infrastructure management system that drives decisions according to data aspects, thus being fully scalable, runtime adaptable and high-performant to address the needs of big data operations and data-intensive applications. Furthermore, the stack goes beyond purely infrastructure elements by introducing techniques for dimensioning big data applications, modelling and analyzing of processes as well as provisioning data-as-a-service by exploiting a seamless analytics framework.

Manufacturing companies are forced to become energy-aware under the pressure of energy costs, legislation and consumers' environmental awareness. Production scheduling remains a critical decision making process, although demanding in computational terms and sensitive on data availability and credibility. Hence the interest in incorporating energy-related aspects in production scheduling. We propose a decision support system (DSS), composed by an Iterated Local Search algorithm that offers hierarchical optimization over multiple scheduling objectives and is energy-aware in terms of both the constraints incorporated and the objectives to be optimized, plus a generic yet concise data model whose entities are extracted from the literature and actual user requirements. The use of this DSS by two textile manufacturers shows that it supports efficiently energy-aware scheduling decisions.

In this paper we investigate the problem of efficiently evaluating XPath queries over large XML data stored in a distributed manner. We propose a MapReduce algorithm based on a query decomposition which computes all expected answers in one MapReduce step. The algorithm can be applied over large XML data which is given either as a single distributed document or as a collection of small XML documents.

Environmental concerns, stricter legislation and inflated energy costs, together yield energy efficiency as an important pillar for virtually every industrial sector. Mindful of this challenge, ISs can act as enablers of energy-based management and intelligent decision support. Based on empirical evidence through two case studies combined with the design of a system prototype, this paper identifies those major functionalities that suffice to characterize an IS as ‘energy-aware’ in manufacturing. The functionalities are classified into two broad categories: (a) energy monitoring and (b) energy-aware analytics and are then combined into a high-level architecture. As a prerequisite for deploying such functionalities, this research presents also an approach integrating energy and operational information flows. Beyond that, the technologies that support the real-time and large-scale handling of energy data are provided. Our effort scales up to introduce a generic framework of a case-independent energy-aware IS.