Digital twin (DT) development has created a significant demand for digital model generation. However, this rapid proliferation introduces a novel challenge: model silos across heterogeneous systems and lifecycle phases. Integrating increasingly complex, multidisciplinary models developed by various stakeholders remains difficult. Addressing interoperability issues is essential to ensure consistent communication and data exchange among heterogeneous digital models. This paper proposes an ontology-based methodology on a Semantic Hub architecture to enhance interoperability between system design and discrete event simulation models.The Semantic Hub integrates reference ontologies and semantic parsing to provide unified semantic representations across modeling tools and lifecycle contexts. The applicability of the proposed method is assessed through case studies. Results show that the approach enables automated generation of semantically consistent knowledge graphs and supports cross-tool model shift with reduced manual intervention. Accordingly, the framework preserves domain knowledge while ensuring consistent representation across design and simulation models. By bridging semantic gaps between these models, this study provides a semantic foundation for lifecycle-oriented interoperability in digital twin development.

In this study, we present an interpretable, control-aware framework for convergent urban flood–epidemic response, based on a coupled system of nonlinear ordinary differential equations (ODEs). The model links reduced-order hydrology, infrastructure degradation, displacement, contamination, epidemic dynamics, and operational capacity with bounded state domains that preserve physical meaning and enable robust simulation. On top of this nonlinear ODE core, we integrated transparent baselines, a one-step model predictive controller (MPC), a constraint-aware optimizer with explicit budget and safety criteria, a proximal policy optimization (PPO)-style reinforcement-learning (RL) policy, and hybrid/Pareto policy variants. To improve usability for non-technical stakeholders, we added a read-only, ground-truth-first LLM explanation layer: Python pre-computes per-phase causal premises (triggers, switch drivers, threshold exceedances, and policy mismatches) from the trajectory, and the LLM rewrites only the per-phase control narrative into human-readable prose. A four-backend evaluation — llama3.2:3b (raw-payload ablation), GPT-4o-mini and gemma4:e2b (premise layer), and llama3-ft (fine-tuned on premise–prose pairs) — identifies gemma4:e2b (7.2GB, free/local) as the best inference-time trade-off and llama3-ft (6GB, bf16) as a fine-tuned alternative achieving near-complete faithfulness without prompt engineering. Multi-seed stochastic evaluations confirm criterion-dependent non-dominance across four adaptive policies: no single policy is simultaneously optimal for mortality, contamination, shelter safety, reward, and budget, establishing the framework as a multi-criteria decision-support tool rather than a single-winner ranker.

Modern manufacturing systems operate in highly dynamic environments, where continuous innovation demands extensive monitoring and adaptation. Bottlenecks in these systems pose challenges for testing and implementing improvements without disrupting ongoing operations. Discrete Event Simulation (DES) offers a forward-looking methodology for assessing alternative scenarios within complex systems. However, the manual design of DES models is typically resource- and time-intensive, making it difficult to accommodate dynamic, rapidly changing conditions. The current approaches are largely conceptual and do not incorporate evidence-based DES model building. This limits their effectiveness and scalability, particularly in modern manufacturing environments characterized by high variability and constant evolution. This study targets the complexity of manually designing DES environments by proposing an automated framework—WEFTSIM—that extracts simulation models directly from manufacturing data. The goal is to enhance the accuracy, efficiency, and adaptability of DES models in representing real-world processes. The evaluation combines two approaches: (i) A real-world case-study-driven assessment of WEFTSIM’s applicability to the automatic, data-driven design of DES models through a systematic comparison of scenario performance. (ii) Validation assessment through a comparative performance analysis of the proposed methods against historical data to quantify their fidelity in representing the system behavior. WEFTSIM effectively derived DES models that closely mirror the actual manufacturing operations, attaining around 80% coverage at the activity level and achieving a high trace similarity of 90% when validated against the observed data. The automated approach reduced the manual and time-intensive conceptualization phase. The evaluation against existing benchmark methods shows WEFTSIM’s capability to automatically design DES models, detect bottlenecks, and rapidly identify an improvement scenario.

The increasing adoption and investment in Model-Based Systems Engineering (MBSE) and Digital Thread and Digital Twin (DT T) demand interoperability among models across the organization. Model interoperability is a critical challenge in the development of DT T due to the diversity of models and standards used in different phases of product or system design life cycle (PLC/SDLC). Moreover, this challenge is further complicated by the complexity of the differences among various organizations and enterprises. This paper proposes an ontology-driven approach to address the challenge of model interoperability and emphasizes the significance of intra-and cross-organizational Enterprise Integration (EI) and trustworthy decision-making in the development of DT T. The proposed approach investigates models’ interoperability, transformation, and portability and introduces potential research avenues to add explainability throughout the digital model’s lifecycle. Furthermore, the paper emphasizes the need for standard evaluation criteria or measurements to evaluate the maturity of EI and to compare different model translation and portability methodologies.

Artificial intelligence (AI) has become an essential tool for manufacturers seeking to optimize their production processes, reduce costs, and improve product quality. However, the complexity of the underlying mechanisms of AI systems can render it difficult for humans to understand and trust AI-driven decisions. Explainable AI (XAI) is a rapidly evolving field that addresses this challenge, providing human-understandable explanations of AI decisions. Based on a systematic literature survey, We explore the latest techniques and approaches that are helping manufacturers gain transparency in the decision-making processes of their AI systems. In this survey, we focus on two of the most exciting areas of XAI: ontology-based and semantic-based XAI (O-XAI, S-XAI, respectively), which provide human-readable explanations of AI decisions by exploiting semantic information. These latter types of explanations are presented in natural language and are designed to be easily understood by non-experts. Translating the decision paths taken by AI algorithms to meaningful explanations through semantics, O-XAI, and S-XAI enables humans to identify various cross-cutting concerns that influence the decisions made by the AI system. This information can be used to improve the performance of the AI system, identify potential biases in the system, and ensure that the decisions are aligned with the goals and values of the manufacturing organization. Additionally, we highlight the benefits and challenges of using O-XAI and S-XAI in manufacturing and discuss the potential for future research, aiming to provide valuable guidance for researchers and practitioners looking to leverage the power of ontologies and general semantics for XAI.

Thanks to the advent of robotics in shopfloor and warehouse environments, control rooms need to seamlessly exchange information regarding the dynamically changing 3D environment to facilitate tasks and path planning for the robots. Adding to the complexity, this type of environment is heterogeneous as it includes both free space and various types of rigid bodies (equipment, materials, humans etc.). At the same time, 3D environment-related information is also required by the virtual applications (e.g., VR techniques) for the behavioral study of CAD-based product models or simulation of CNC operations. In past research, information models for such heterogeneous 3D environments are often built without ensuring connection among different levels of abstractions required for different applications. For addressing such multiple points of view and modelling requirements for 3D objects and environments, this paper proposes an ontology model that integrates the contextual, topologic, and geometric information of both the rigid bodies and the free space. The ontology provides an evolvable knowledge model that can support simulated task-related information in general. This ontology aims to greatly improve interoperability as a path planning system (e.g., robot) and will be able to deal with different applications by simply updating the contextual semantics related to some targeted application while keeping the geometric and topological models intact by leveraging the semantic link among the models.

Urban mobility is a critical aspect of sustainable urban development, with significant environmental, social, and economic implications. Assessing the sustainability of urban mobility systems in order to create more carbon neutral, liveable, healthier, and sustainable cities and neighborhoods for the future requires a multidimensional approach that integrates diverse factors. However, the lack of a unified assessment framework poses challenges in comparing and evaluating different urban mobility projects. This article proposes an ontology for assessing the sustainability of urban mobility systems. This ontology is based on a multidimensional approach that integrates knowledge from experts in transportation engineering, urban planning, environmental science, and social sciences to incorporate existing sustainability indicators and frameworks, as well as domain-specific knowledge. A consensus approach based on Dempster–Shäfer (DS) and Analytic Hierarchy Process (AHP) methods is proposed to account for uncertainties and to allow for the consideration of preferences and ill judgment. Through a case study in Romania, the authors demonstrated the applicability of the proposal to provide a comprehensive and flexible framework for assessing urban mobility sustainability. The proposed ontology provides a valuable tool for policymakers, urban planners, and transportation engineers to make informed decisions towards sustainable urban mobility, and the sensitivity analysis is carried out to demonstrate the robustness of the proposed framework. It has potential for iterative validation and feedback from domain experts, and can serve as a foundation for future research.

Digital revolution produces massive, heterogeneous and isolated data. These latter remain underutilized, unsuitable for integrated querying and knowledge discovering. Hence the importance of this survey on data integration which identifies challenging issues and trends. First, an overview of the different generations and basics of data integration is given. Then, semantic data integration is focused, since it semantically links data allowing wider insights and decision-making. More than thirty works are reviewed. The goal is to help analysts to identify relevant criteria to compare then choose among semantic data integration approaches, focusing on the category (materialized, virtual or hybrid) and querying techniques.

Flexible manufacturing plays an important role in Industry 4.0 for developing the factory of the future and requires enhanced planning, scheduling, and control. The quick and effective adaptation in the production line in response to customers' requirements or face of unwanted situations will promote considerable flexibility in manufacturing. CHAIKMAT is a research project funded by the French National Agency of research that aims to add flexibility and transparency to manufacturing through trustful automatic decision-making. The project proposes a human-centric AI approach that investigates whether an available set of machines can perform a specific production process and then provides human experts with meaningful explanations of how the decision process is conducted. A hybrid predictive model, comprising of both semantic reasoning and machine learning system will help in real-time decision making through the automated analysis of two sources of information: a stream of machine-monitoring data describing the current state of the production line and a common-sense knowledge graph (MCSKG) that is modelled based on machine capability and process planning ontology model. Furthermore, this hybrid predictive model will also be able to explain its prediction so that the user can fully comprehend the rationale behind such a decision. In this paper, we will describe the architecture of the proposed system along with a detailed plan for verification. The paper also presents the state-of-the-art of AI applications in flexible manufacturing to establish how CHAIKMAT project aims to apply some of the novel AI methodologies to circumvent the existing gaps.

Cholera is a bacterial disease that is commonly transmitted through contaminated water, leading to severe diarrhea and rapid dehydration that can prove fatal if left untreated. The complexity of the disease spread arises from the convergence of several distinct and interrelated factors, which previous research has often failed to consider. A significant scientific limitation of the existing literature is the simplistic assumption of linear or logistic dynamics of the disease spread, thereby impeding a thorough assessment of the effectiveness of control strategies. Since environmental factors are the most influential determinant of Vibrio bacterial growth in nature and are responsible for the resurgence, propagation, and disappearance of cholera epidemics, we have proposed a S-I-R-S model that combines bacterial dynamics with the Allee effect. This model takes into account the environmental influence and allows for a better understanding of the disease dynamics. Our results have revealed the phenomenon of bi-stability, with backward and forward bifurcation. Furthermore, our findings have demonstrated that the Allee effect provides a robust framework for characterizing fluctuations in bacterial populations and the onset of cholera outbreaks. This framework can be used for assessing the effectiveness of control strategies, including regular environmental sanitation programs, adherence to hygiene protocols, and monitoring of unfavorable weather conditions.

Nowadays, due to urbanization growth, the need for mobility arises around the world. Some cities indeed are seeking for innovative solutions in order to meet the increasing users demand in connectivity, among which mega cities that have introduce air mobility. This latter will increase the mobility externalities and complexify its management. In the past decade, mobility as a service paradigm has been proven as the best approach to address such issues. But the current solutions are provided by autonomous mobility providers. In order to provide policy-makers in cities with a decision support tool allowing them to manage traffic regulation, environmental pollution, safety of the passengers, and services and infrastructures renewal, there is a need to address interoperability issue between the existing mobility systems. This paper is a preliminary study of interoperability concerns in the context of multidimensional urban mobility, which includes land and air modes. To that end, we present and discuss the building blocks of the underlying system and show which kinds of the interoperability occur and provide directions to solve them, within the frame of mobility as a service (MaaS).

Real-time management of hydraulic systems composed of multi-reservoir involves conflicting objectives. Its representation requires complex variables to consider all the systems dynamics. Interfacing simulation model with optimization algorithm permits to integrate flow routing into reservoir operation decisions and consists in solving separately hydraulic and operational constraints, but it requires that the water resource management model is based on an evolutionary algorithm. Considering channel routing in optimization algorithm can be done using conceptual models such as the Muskingum model. However, the structure of algorithms based on a network flow approach, inhibits the integration of the Muskingum model in the approach formulation. In this work, a flood routing model, corresponding to a singular form of the Muskingum model, constructed as a network flow is proposed and integrated into the water management optimization. A genetic algorithm is involved for the calibration of the model. The proposed flood routing model was applied on the standard Wilson test and on a 40 km reach of the Arrats river (southwest of France). The results were compared with the results of the Muskingum model. Finally, operational results for a water resource management system including this model are illustrated on a rainfall event.

In light of the complexity of unfolding disasters, the diversity of rapidly evolving events, the enormous amount of generated information, and the huge pool of casualties, emergency responders (ERs) may be overwhelmed and in consequence poor decisions may be made. In fact, the possibility of transporting the wounded victims to one of several hospitals and the dynamic changes in healthcare resource availability make the decision process more complex. To tackle this problem, we propose a multicriteria decision support service, based on the Analytic Hierarchy Process (AHP) method, that aims to avoid overcrowding and outpacing the capacity of a hospital to effectively provide the best care to victims by finding out the most appropriate hospital that meets the victims’ needs. The proposed approach searches for the most appropriate healthcare institution that can effectively deal with the victims’ needs by considering the availability of the needed resources in the hospital, the victim’s wait time to receive the healthcare, and the transfer time that represents the hospital proximity to the disaster site. The evaluation and validation results showed that the assignment of hospitals was done successfully considering the needs of each victim and without overwhelming any single hospital.

Ontologies are logical theories that are used in computer science for describing different items such as web services, agents in multi-agent systems, or domain knowledge. Many ontologies exist, expressing various domains of knowledge with different abstraction levels (domain ontologies, top-level ontologies, and task ontologies are the usual categories). The conceptualization of the knowledge contained in an ontology is subject to change, whether because the context of its use changes, because the domain evolves, or because an ontology needs to interoperate with other elements using other ontologies. Change in logical theories is a form of defeasible reasoning, in which some formulas need to be added or removed from a knowledge base. Adaptive Logics (AL) is a logic managing defeasible reasoning that we investigate in this paper for managing change in ontologies expressed with Description Logics (DL). The adaptation of AL for DL will help express the context in which formulas remain valid or can be added to a DL knowledge base, and ease the interoperability between ontologies.

This work deals with the simulation of complex manipulation tasks in virtual environments. Validating such complex tasks, possibly to be performed under strong geometric constraints, requires considering task and path planning jointly. The contribution of this work focuses on using task-related information at the path planning level. We propose an ontology-based approach to a) model the 3D environment where the simulated task is executed, based on an original multi-level environment model involving higher abstraction level data than the purely geometric models traditionally used, and b) automatically define path planning queries for the primitive actions of a task plan, together with task-related geometric constraints on these queries. This approach allows the improvement of the state of the art from two points of view. First, our joint task and path planning approach allows the improvement of path planning through better semantic control of the path planning process. Second, if compared to hard-coded geometric constraints, the proposed ontology-based approach introduces a more flexible way of defining geometric constraints through an inference process, and can be adapted to different applications of manipulation tasks.

In order to face an increasing economic competition, industrial manufacturers wish to reduce the time and cost of product development. Furthermore, up-to-date products are more and more integrated, and must be assembled, disassembled or maintained under potentially very strong geometric constraints. In the context of Industry 4.0, manufacturers are therefore expressing the desire to validate all the tasks related to their products lifecycles, from design stage on, by simulation using a digital mock-up, and before building the physical prototypes. A key issue is then to find a trajectory, a movement, to show the feasibility of the simulated scenarios. Automatic path planning algorithms, developed by the robotics community from the 1980s on, have been widely used for this purpose. In this paper, we intend to improve the relevance of the trajectories proposed by such algorithms and the associated computation times. To do so, we consider: a) the use of path planning algorithms or of combinations of these; b) the involvement for the environment modelling of data with a higher abstraction level than the purely geometric data traditionally used; and c) the representation of the knowledge related to the task to be performed by using ontologies. The approaches developed and associated improvements of the state of the art are validated experimentally through the simulation of highly geometrically constrained manipulation tasks.

Managing complex disaster situations is a challenging task because of the large number of actors involved and the critical nature of the events themselves. In particular, the different terminologies and technical vocabularies that are being exchanged among Emergency Responders (ERs) may lead to misunderstandings. Maintaining a shared semantics for exchanged data is a major challenge. To help to overcome these issues, we elaborate a modular suite of ontologies called POLARISCO that formalizes the complex knowledge of the ERs. Such a shared vocabulary resolves inconsistent terminologies and promotes semantic interoperability among ERs. In this work, we discuss developing POLARISCO as an extension of Basic Formal Ontology (BFO) and the Common Core Ontologies (CCO). We conclude by presenting a real use-case to check the efficiency and applicability of the proposed ontology.

The spread of new Web technologies has led to organizational transitions that are at the root of the digital revolution and then the generation of a big amount of heterogeneous data using different vocabularies and different conceptual schemas. Accordingly, data resides in many siloed systems and are mainly untapped for integrated operations, insights, and decisionmaking situations. To overcome the insufficient exploitation of data, a data integration system is crucial to break down data silos and create a common information space where data will be semantically linked. Data integration is at the heart of the data value chain. It allows to integrate the big amount of the heterogeneous acquired data and prepare it to exploitation phase. Specifically, semantic data integration provides a semantic meaningful enrichment to the integrated data that empowers the capabilities of data exploration through artificial intelligence algorithms. Semantic data integration will give data analysts a comprehensive toolkit for dataset exploration and for discovering the knowledge within integrated datasets. This paper provides a survey on the different generations (ETL, OBDA) of semantic data integration approaches and systems. Specifically, it reviews 29 works belonging to three categories of approaches; materialized, virtual and hybrid approaches. It aims to identify the most relevant aspects to consider in the development of a semantic data integration approach in order to support analysts and experts to select the most appropriate approach, depending on their needs.

In today’s competitive business environment, the cost of a product is one of the most important considerations for its sale. Businesses are heavily involved in research strategies to minimize the cost of elements that can impact on the final price of the product. Logistics is one such factor. Numerous products arrive from diverse locations to consumers in today’s digital era of online businesses. Clearly, the logistics sector faces several dilemmas from order attributes to environmental changes in this regard. This has specially been noted during the ongoing Covid-19 pandemic where the demands on online businesses have increased several fold. Consequently, the methodology to optimise delivery cost and its impact on environmental focus by reducing CO2 emissions has gained relevance. The resultant strategy of Shipment Consolidation that has evolved is an approach that combines one or more transport orders in the same vehicle for delivery. Shipment Consolidation has been categorized in three order scheduling approaches: Time based consolidation, Quantity based consolidation, and a Hybrid (Time-Quantity) based consolidation. In this paper, a new Hybrid Consolidation approach is presented. Using the Hybrid approach, it has been shown that order delivery can be facilitated by taking into account not only the order pick up time, but also the total order quantity. These results have shown that if a time window is available in respect of the order delivery time, then the order can be delayed from pickup to consolidate it with other orders for cost optimization. This hybrid approach is based on four consolidation principles, two of which work on fixed departure and two, on demand departure. Three of these rules have been implemented and tested here with an application case study. Statistical analysis of the results is illustrated with different planning evaluation indicators. The Result analyses indicate that consolidation of orders is increased with each implemented rule hence motivating us towards the implementation of the fourth rule. Testing with bigger data sets is required.