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AuDaLa is Turing Complete
No full textPart 3: Short PapersInternational audienceAuDaLa is a recently introduced programming language that follows the new data autonomous paradigm. In this paradigm, small pieces of data execute functions autonomously. Considering the paradigm and the design choices of AuDaLa, it is interesting to determine the expressiveness of the language and to create verification methods for it. In this paper, we take our first steps to such a verification method by implementing Turing machines in AuDaLa and proving that implementation correct. This also proves that AuDaLa is Turing complete
Adaptable Configuration of Decentralized Monitors
No full textPart 2: Full Papers with ArtefactInternational audienceProminent challenges in runtime verification of a distributed system are the correct placement, configuration, and coordination of the monitoring nodes. This work considers state-of-the-art decentralized monitoring practices and proposes a framework to recommend efficient configurations of the monitoring system depending on the target specification. Our approach aims to optimize communication over several features (e.g., minimizing the number of messages exchanged, the number of computations happening overall, etc.) in contexts where finding an efficient communication strategy requires slow simulations. We optimize by training multiple machine learning models from simulations combining traces, formulae, and systems of different sizes. The experimental results show that the developed model can reliably suggest the best configuration strategy in a few nanoseconds, contrary to the minutes or possibly hours required by direct simulations that would be impractical at runtime
Integrating Uncertainty into a Supply Chain Network for Adaptive S&OP Process
No full textPart 6: Uncertainty and Collaboration in Supply ChainInternational audienceInstability is the new normal for supply chain networks. In this context, the Adaptive Sales and Operations Planning (AS&OP) process proposed by the Demand-Driven Adaptive-Enterprise model aims to improve the way these supply chain networks manage volatile, uncertain, complex and ambiguous environments. However, the process, as originally defined, reliesmainly on a deterministic maximum likelihood approach, thus limiting its scope. The present research work examines existing strategies for integrating uncertainty into the strategic planning and ultimately proposes an original Decision Support System that integratesuncertainty via interval data and scenario-based planning. An illustrative case study validates this proposal and discusses its limitations
Sustaining relevant digital inclusive education for young people (5-18 years of age): Northern Ireland Case Study: Northern Ireland Case Study
No full textA Proposal for Automatic Demand Forecast Model Selection
No full textInternational audienceDemand forecasting is critical within collaborative networks, enabling suppliers,manufacturers, and retailers to synchronize their operations and achieve enhanced supply chain efficiency. Despite a wealth of research on time series forecast model selection and the availability of numerous forecast models, selecting the most appropriate model for a specific time series remains a challenging task. In this study, an automatic demand forecast model selection procedure is proposed that includes a wide range of statistical andmachine learning forecast models. The optimization of the hyperparameters is performed on all the models. The study is validated on M3 monthly data, outperforming all submitted methods and demonstrating significant improvements in terms of accuracy. The approach was also applied to a collaborative network company
Leaf-First Zipper Semantics
No full textInternational audienceBiernacka et al. recently proposed zipper semantics, a semantics format from which sound and complete abstract machines for non-deterministic languages can be automatically derived. We present a new style of zipper semantics, called leaf-first, in which we express the semantics of two extensions of HOπ, a higher-order version of the π-calculus: one with passivation and the other with join patterns. The leaf-first style is better suited than the original one to express phenomena occurring in process calculi semantics such as scope extrusion, which is observable with passivation and complex with join patterns
Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments: 43rd IFIP WG 5.7 International Conference, APMS 2024 Chemnitz, Germany, September 8–12, 2024 Proceedings, Part IV
No full textInternational audienceThe IFIP AICT series publishes state-of-the-art results in the sciences and technologies of information and communication. The scope of the series includes: foundations of computer science; software theory and practice;</div
Dynamic Pricing for Fashion Supply Chain with Blockchain Supported Value Authentication
No full textPart 2: New Horizons for Intelligent Manufacturing Systems with IoT, AI, and Digital TwinsInternational audienceThe fashion industry is facing a challenge in pricing seasonal fashion products. Our study explores the dynamic pricing strategy for fashion supply chain that blockchain technology is employed to enhance fashionably designed value perceived by customers and identify heterogeneous fashion-sensitive customers. We propose pricing models on game theory in the presence of blockchain supported value authentication. Our research indicates that blockchain adoption could enhance the perceived fashion value of customers and further increase the customers’ willingness to buy and consequently profit. We compared basic priding (SF) and blockchain common pricing (CP) strategies that heterogeneous fashion-sensitive customers are differentiated by blockchain driven fashion value authentication data. Our results demonstrate that the equilibrium price in blockchain pricing model CP is higher than basic SF. And, the blockchain could increase the expected profit of fashion supply chains
Digitalizing Smallholder Farmer Agri-Food Supply Chains: A Case Study from a Developing Economy
No full textPart 1: Digital Transformation Approaches in Production and ManagementInternational audienceSmallholder farming is critical to ensuring food security and alleviating rural poverty. Poor agricultural practices, supply chain inefficiencies, weather challenges, and market disruptions all diminish productivity in this sector. As modern technology and digitalization reshape agriculture, there is a significant augmentation of stakeholder connectivity within smallholder farmer Agri-Food Supply Chains (AFSCs). The progress of technology allows smallholder farmers to gain access to high-quality farming inputs while expanding their market reach. While there are proven benefits of digitally transforming smallholder farmer AFSCs, there is still a significant knowledge gap in effectively assessing the potential of digital technologies from a supply chain perspective. As the overall approach in this paper, we used the case study research method along with inductive reasoning. We combined the AHP and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods to include both industry practitioners and academic perspectives in the decision-making process. The process involved using AHP to analyze supply chain inefficiencies, with a focus on their impact on yield, harvest quality, and farmer livelihood, and then using the TOPSIS method to prioritize digital solutions for the chosen case study. The case study revealed that 61% of inefficiencies arose in the early supply chain stages, notably in regulation (28.26%) and farm input supply (33.03%), emphasizing the critical need for prioritizing digital farm record-keeping and registration for improved efficiency. This study emphasizes practical digital solutions for smallholder farming supply chains while integrating industry and academic perspectives, offering a systematic approach to prioritizing interventions
Literature Review on the Current State-of-the-Art in Research and Technological Advancements in the Field of Machine Learning Applied to Predictive Maintenance
No full textPart 1: Digital Transformation Approaches in Production and ManagementInternational audienceThis literature review examines the technologies of Machine Learning (ML) in Predictive Maintenance (PdM), highlighting the necessity for industries to boost production efficiency for competitive advantage amid growing global demands. It underscores the pivotal roles of technological advancements, especially the Internet of Things (IoT) and Big Data, in enabling smarter automation and intelligent production processes. By leveraging equipment sensor data, Predictive Maintenance serves as a crucial proactive maintenance strategy, enhancing reliability and accuracy through its diagnostic and prognostic stages. However, challenges in financial, organizational, data, and repair complexities hinder its full-scale implementation, necessitating a deeper understanding of ML techniques in PdM. The review further discusses AI/ML’s role in enhancing predictive maintenance, detailing applications of AI, ML, and Deep Learning (DL) in predictive analytics and identifying emerging trends like transformers and self-supervised learning, which promise to improve PdM outcomes. Through a structured analysis, this report underscores the evolving landscape of ML applications in PdM, highlighting both challenges and opportunities, and suggesting further exploration of ML algorithms in this field