Coalition based approach for shop floor agility – a multiagent approach

Dissertation submitted for a PhD degree in Electrical Engineering, speciality of Robotics and Integrated Manufacturing from the Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaThis thesis addresses the problem of shop floor agility. In order to cope with the disturbances and uncertainties that characterise the current business scenarios faced by manufacturing companies, the capability of their shop floors needs to be improved quickly, such that these shop floors may be adapted, changed or become easily modifiable (shop floor reengineering). One of the critical elements in any shop floor reengineering process is the way the control/supervision architecture is changed or modified to accommodate for the new processes and equipment. This thesis, therefore, proposes an architecture to support the fast adaptation or changes in the control/supervision architecture. This architecture postulates that manufacturing systems are no more than compositions of modularised manufacturing components whose interactions when aggregated are governed by contractual mechanisms that favour configuration over reprogramming. A multiagent based reference architecture called Coalition Based Approach for Shop floor Agility – CoBASA, was created to support fast adaptation and changes of shop floor control architectures with minimal effort. The coalitions are composed of agentified manufacturing components (modules), whose relationships within the coalitions are governed by contracts that are configured whenever a coalition is established. Creating and changing a coalition do not involve programming effort because it only requires changes to the contract that regulates it

A holonic multi-agent methodology to design sustainable intelligent manufacturing control systems

[EN] The urgent need for sustainable development is imposing radical changes in the way manufacturing systems are designed and implemented. The overall sustainability in industrial activities of manufacturing companies must be achieved at the same time that they face unprecedented levels of global competition. Therefore, there is a well-known need for tools and methods that can support the design and implementation of these systems in an effective way. This paper proposes an engineering method that helps researchers to design sustainable intelligent manufacturing systems. The approach is focused on the identification of the manufacturing components and the design and integration of sustainability-oriented mechanisms in the system specification, providing specific development guidelines and tools with built-in support for sustainable features. Besides, a set of case studies is presented in order to assess the proposed method.This research was supported by research projects TIN2015-65515-C4-1-R and TIN2016-80856-R from the Spanish government. The authors would like to acknowledge T. Bonte for her contribution to the NetLogo simulator of the AIP PRIMECA cell.Giret Boggino, AS.; Trentesaux, D.; Salido Gregorio, MÁ.; Garcia, E.; Adam, E. (2017). A holonic multi-agent methodology to design sustainable intelligent manufacturing control systems. Journal of Cleaner Production. 167(1):1370-1386. https://doi.org/10.1016/j.jclepro.2017.03.079S13701386167

Simulation Game Concept For AI-Enhanced Teaching Of Advanced Value Stream Analysis and Design

Value stream analysis and design is employed globally by improvement teams within industrial settings to maximize value creation and eliminate waste. For ending methodical time-centricity, research expanded the methodology to incorporate diverse facets like material flow cost accounting, information logistics, and external influence factors. These enhancements, along with increasing data volumes, are prompting a re-evaluation of how professional improvement teams should think and operate. Consequently, a transformation of the pedagogical approach used for educating students and professionals necessitates novel solutions. Conventional teaching methods such as expository lectures are widely considered inadequate in promoting knowledge retention and engagement. So far, existing research has not yet resulted in a solution that can effectively impart the methodological complexity of advanced value stream analysis and design in a motivating and vivid fashion. To address this gap, this paper applies a tailored CRISP gamification framework to develop a simulation game concept. These concept enables AI-enhanced teaching of advanced value stream analysis and design focusing on identification of multi-stage resource-efficient optimization strategies. Through integration of game-based learning with AI a trained reinforcement learning agent can act either competitively or cooperatively, creating a unique form of teaching accounting the aspects personalization, adaptive feedback, content creation, and analysis and assessment

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Supply chains : ago-antagonistic systems through co-opetition game theory lens

Supply chain configurations, as hybrid governance structures, allow companies to be sufficiently integrated while keeping a certain level of flexibility. This enables them, on one hand, to converge towards common interests through the development of cooperation; and on the other hand, to diverge on their own interests by remaining in competition. This dynamics generates an ago-antagonistic system where both of these two concepts, namely cooperation and competition, simultaneously drive the supply chain. In the present article, this system is analyzed by using the co-opetition game theory developed by Brandenburger and Nalebuff (1996) in order to highlight the importance of such an apprehension of the supply chain approach.Supply chain; cooperation; competition; ago-antagonistic approach; co-opetition game theory

A general outline of a sustainable supply chain 4.0

[EN] This article presents a literature review to identify the current knowledge of supply chains 4.0 from the sustainability perspective. Reviewed papers were classified in terms of objectives, results, and sustainability approaches. Additionally, a critical discussion with the main results and recommendations for further research was carried out. Manufacturing supply chains have been contemplated but agri-food supply chains and chains related to diversified cropping systems have been also considered. In this way, 54 articles were identified and revised, and were classified according to the three main aspects of sustainability: economic, social, and environmental. The classification of articles indicated that more attention has been paid to the environmental aspect in the industry 4.0 (I4.0) context in the literature, while the social aspect has been paid less attention. Finally, reference frameworks were identified, along with the I4.0 models, algorithms, heuristics, metaheuristics, and technologies, which have enabled sustainability in supply chains.This research was supported by the European Commission Horizon 2020 project entitled 'Crop diversification and low-input farming cross Europe: From practitioners' engagement and ecosystems services to increased revenues and value chain organisation' (Diverfarming), grant agreement 728003; and the Spanish Ministry of Science, Innovation, and Universities project entitled 'Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)' (RTI2018-101344-B-I00).Cañas, H.; Mula, J.; Campuzano-Bolarín, F. (2020). A general outline of a sustainable supply chain 4.0. Sustainability. 12(19):1-17. https://doi.org/10.3390/su121979781171219Design Principles for Industrie 4.0 Scenarios https://ieeexplore.ieee.org/document/7427673Liao, Y., Deschamps, F., Loures, E. de F. R., & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), 3609-3629. doi:10.1080/00207543.2017.1308576Tseng, M.-L., Zhu, Q., Sarkis, J., & Chiu, A. S. F. (2018). Responsible consumption and production (RCP) in corporate decision-making models using soft computation. Industrial Management & Data Systems, 118(2), 322-329. doi:10.1108/imds-11-2017-0507Ghadimi, P., Wang, C., Lim, M. K., & Heavey, C. (2019). Intelligent sustainable supplier selection using multi-agent technology: Theory and application for Industry 4.0 supply chains. Computers & Industrial Engineering, 127, 588-600. doi:10.1016/j.cie.2018.10.050Wang, C., Ghadimi, P., Lim, M. K., & Tseng, M.-L. (2019). A literature review of sustainable consumption and production: A comparative analysis in developed and developing economies. Journal of Cleaner Production, 206, 741-754. doi:10.1016/j.jclepro.2018.09.172Exploring Linkages between Lean and Green Supply Chain and the Industry 4.0 https://link.springer.com/chapter/10.1007/978-3-319-59280-0_103Luthra, S., & Mangla, S. K. (2018). Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection, 117, 168-179. doi:10.1016/j.psep.2018.04.018Lin, K., Shyu, J., & Ding, K. (2017). A Cross-Strait Comparison of Innovation Policy under Industry 4.0 and Sustainability Development Transition. Sustainability, 9(5), 786. doi:10.3390/su9050786Man, J. C. de, & Strandhagen, J. O. (2017). An Industry 4.0 Research Agenda for Sustainable Business Models. Procedia CIRP, 63, 721-726. doi:10.1016/j.procir.2017.03.315KIEL, D., MÜLLER, J. M., ARNOLD, C., & VOIGT, K.-I. (2017). SUSTAINABLE INDUSTRIAL VALUE CREATION: BENEFITS AND CHALLENGES OF INDUSTRY 4.0. International Journal of Innovation Management, 21(08), 1740015. doi:10.1142/s1363919617400151Waibel, M. W., Steenkamp, L. P., Moloko, N., & Oosthuizen, G. A. (2017). Investigating the Effects of Smart Production Systems on Sustainability Elements. Procedia Manufacturing, 8, 731-737. doi:10.1016/j.promfg.2017.02.094Manavalan, E., & Jayakrishna, K. (2019). A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements. Computers & Industrial Engineering, 127, 925-953. doi:10.1016/j.cie.2018.11.030Ding, B. (2018). Pharma Industry 4.0: Literature review and research opportunities in sustainable pharmaceutical supply chains. Process Safety and Environmental Protection, 119, 115-130. doi:10.1016/j.psep.2018.06.031Bag, S., Telukdarie, A., Pretorius, J. H. C., & Gupta, S. (2018). Industry 4.0 and supply chain sustainability: framework and future research directions. Benchmarking: An International Journal. doi:10.1108/bij-03-2018-0056Ghafoorpoor Yazdi, P., Azizi, A., & Hashemipour, M. (2018). An Empirical Investigation of the Relationship between Overall Equipment Efficiency (OEE) and Manufacturing Sustainability in Industry 4.0 with Time Study Approach. Sustainability, 10(9), 3031. doi:10.3390/su10093031Braccini, A., & Margherita, E. (2018). Exploring Organizational Sustainability of Industry 4.0 under the Triple Bottom Line: The Case of a Manufacturing Company. Sustainability, 11(1), 36. doi:10.3390/su11010036Moghaddam, M., Cadavid, M. N., Kenley, C. R., & Deshmukh, A. V. (2018). Reference architectures for smart manufacturing: A critical review. Journal of Manufacturing Systems, 49, 215-225. doi:10.1016/j.jmsy.2018.10.006Paravizo, E., Chaim, O. C., Braatz, D., Muschard, B., & Rozenfeld, H. (2018). Exploring gamification to support manufacturing education on industry 4.0 as an enabler for innovation and sustainability. Procedia Manufacturing, 21, 438-445. doi:10.1016/j.promfg.2018.02.142Müller, J. M., Kiel, D., & Voigt, K.-I. (2018). What Drives the Implementation of Industry 4.0? The Role of Opportunities and Challenges in the Context of Sustainability. Sustainability, 10(1), 247. doi:10.3390/su10010247Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018). Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 117, 408-425. doi:10.1016/j.psep.2018.05.009Hidayatno, A., Destyanto, A. R., & Hulu, C. A. (2019). Industry 4.0 Technology Implementation Impact to Industrial Sustainable Energy in Indonesia: A Model Conceptualization. Energy Procedia, 156, 227-233. doi:10.1016/j.egypro.2018.11.133Sustainable Value Stream Mapping and Technologies of Industry 4.0 in Manufacturing Process Reconfiguration: A Case Study in an Apparel Company https://ieeexplore.ieee.org/document/8476750Kumar, R., Singh, S. P., & Lamba, K. (2018). Sustainable robust layout using Big Data approach: A key towards industry 4.0. Journal of Cleaner Production, 204, 643-659. doi:10.1016/j.jclepro.2018.08.327Wiśniewska-Sałek, A. (2018). Sustainable Development in Accordance With the Concept of Industry 4.0 on the Example of the Furniture Industry. MATEC Web of Conferences, 183, 04005. doi:10.1051/matecconf/201818304005Müller, J. M., & Voigt, K.-I. (2018). Sustainable Industrial Value Creation in SMEs: A Comparison between Industry 4.0 and Made in China 2025. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(5), 659-670. doi:10.1007/s40684-018-0056-zTsai, W.-H., & Lu, Y.-H. (2018). A Framework of Production Planning and Control with Carbon Tax under Industry 4.0. Sustainability, 10(9), 3221. doi:10.3390/su10093221Birkel, H., Veile, J., Müller, J., Hartmann, E., & Voigt, K.-I. (2019). Development of a Risk Framework for Industry 4.0 in the Context of Sustainability for Established Manufacturers. Sustainability, 11(2), 384. doi:10.3390/su11020384Roda-Sanchez, L., Garrido-Hidalgo, C., Hortelano, D., Olivares, T., & Ruiz, M. C. (2018). OperaBLE: An IoT-Based Wearable to Improve Efficiency and Smart Worker Care Services in Industry 4.0. Journal of Sensors, 2018, 1-12. doi:10.1155/2018/6272793Ardanza, A., Moreno, A., Segura, Á., de la Cruz, M., & Aguinaga, D. (2019). Sustainable and flexible industrial human machine interfaces to support adaptable applications in the Industry 4.0 paradigm. International Journal of Production Research, 57(12), 4045-4059. doi:10.1080/00207543.2019.1572932Zambon, I., Cecchini, M., Egidi, G., Saporito, M. G., & Colantoni, A. (2019). Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs. Processes, 7(1), 36. doi:10.3390/pr7010036Belaud, J.-P., Prioux, N., Vialle, C., & Sablayrolles, C. (2019). Big data for agri-food 4.0: Application to sustainability management for by-products supply chain. Computers in Industry, 111, 41-50. doi:10.1016/j.compind.2019.06.006Trivelli, L., Apicella, A., Chiarello, F., Rana, R., Fantoni, G., & Tarabella, A. (2019). From precision agriculture to Industry 4.0. British Food Journal, 121(8), 1730-1743. doi:10.1108/bfj-11-2018-0747Miranda, J., Ponce, P., Molina, A., & Wright, P. (2019). Sensing, smart and sustainable technologies for Agri-Food 4.0. Computers in Industry, 108, 21-36. doi:10.1016/j.compind.2019.02.002Stock, T., Obenaus, M., Kunz, S., & Kohl, H. (2018). Industry 4.0 as enabler for a sustainable development: A qualitative assessment of its ecological and social potential. Process Safety and Environmental Protection, 118, 254-267. doi:10.1016/j.psep.2018.06.026Chaim, O., Muschard, B., Cazarini, E., & Rozenfeld, H. (2018). Insertion of sustainability performance indicators in an industry 4.0 virtual learning environment. Procedia Manufacturing, 21, 446-453. doi:10.1016/j.promfg.2018.02.143Smart Factories in Industry 4.0: A Review of the Concept and of Energy Management Approached in Production Based on the Internet of Things Paradigm https://ieeexplore.ieee.org/document/7058728Bonilla, S., Silva, H., Terra da Silva, M., Franco Gonçalves, R., & Sacomano, J. (2018). Industry 4.0 and Sustainability Implications: A Scenario-Based Analysis of the Impacts and Challenges. Sustainability, 10(10), 3740. doi:10.3390/su10103740De Sousa Jabbour, A. B. L., Jabbour, C. J. C., Foropon, C., & Godinho Filho, M. (2018). When titans meet – Can industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors. Technological Forecasting and Social Change, 132, 18-25. doi:10.1016/j.techfore.2018.01.017Meng, Y., Yang, Y., Chung, H., Lee, P.-H., & Shao, C. (2018). Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review. Sustainability, 10(12), 4779. doi:10.3390/su10124779Kamble, S. S., Gunasekaran, A., & Sharma, R. (2018). Analysis of the driving and dependence power of barriers to adopt industry 4.0 in Indian manufacturing industry. Computers in Industry, 101, 107-119. doi:10.1016/j.compind.2018.06.004Huh, J.-H., & Lee, H.-G. (2018). Simulation and Test Bed of a Low-Power Digital Excitation System for Industry 4.0. Processes, 6(9), 145. doi:10.3390/pr6090145Fritzsche, K., Niehoff, S., & Beier, G. (2018). Industry 4.0 and Climate Change—Exploring the Science-Policy Gap. Sustainability, 10(12), 4511. doi:10.3390/su10124511IoT Solution for Energy Optimization in Industry 4.0: Issues of a Real-life Implementation https://ieeexplore.ieee.org/document/8534537Towards a System-of-Systems for Improved Road Construction Efficiency Using Lean and Industry 4.0 https://ieeexplore.ieee.org/document/8428698HERNANDEZ LUNA, M., ROBLEDO FAVA, R., FERNANDEZ DE CORDOBA CASTELLA, P., PAREDES, A., MICHINEL ALVAREZ, H., & ZARAGOZA FERNANDEZ, S. (2018). USE OF STATISTICAL CORRELATION FOR ENERGY MANAGEMENT IN OFFICE PREMISES ADOPTING TECHNIQUES OF THE INDUSTRY 4.0. DYNA, 93(1), 602-607. doi:10.6036/8844Energy Management in Industry 4.0 Ecosystem: A Review on Possibilities and Concerns https://www.daaam.info/Downloads/Pdfs/proceedings/proceedings_2018/097.pdfWang, X. V., & Wang, L. (2018). Digital twin-based WEEE recycling, recovery and remanufacturing in the background of Industry 4.0. International Journal of Production Research, 57(12), 3892-3902. doi:10.1080/00207543.2018.1497819Tsai, W.-H. (2018). Green Production Planning and Control for the Textile Industry by Using Mathematical Programming and Industry 4.0 Techniques. Energies, 11(8), 2072. doi:10.3390/en11082072Sherazi, H. H. R., Imran, M. A., Boggia, G., & Grieco, L. A. (2018). Energy Harvesting in LoRaWAN: A Cost Analysis for the Industry 4.0. IEEE Communications Letters, 22(11), 2358-2361. doi:10.1109/lcomm.2018.2869404Tsai, W.-H., Chu, P.-Y., & Lee, H.-L. (2019). Green Activity-Based Costing Production Planning and Scenario Analysis for the Aluminum-Alloy Wheel Industry under Industry 4.0. Sustainability, 11(3), 756. doi:10.3390/su11030756Analysis of the Variables That Affect the Intention to Adopt Precision Agriculture for Smart Water Management in Agriculture 4.0 Context https://ieeexplore.ieee.org/document/8766384Franciosi, C., Iung, B., Miranda, S., & Riemma, S. (2018). Maintenance for Sustainability in the Industry 4.0 context: a Scoping Literature Review. IFAC-PapersOnLine, 51(11), 903-908. doi:10.1016/j.ifacol.2018.08.459DE LAS HERAS GARCIA DE VINUESA, A., AGUAYO GONZALEZ, F., & CORDOBA ROLDAN, A. (2018). PROPOSAL OF A FRAMEWORK FOR THE EVALUATION OF THE SUSTAINABILITY OF PRODUCTS FROM THE PARADIGM OF THE CIRCULAR ECONOMY BASED ON INDUSTRY 4.0 (1ST PART). DYNA, 93(1), 360-364. doi:10.6036/8631DE LAS HERAS GARCIA DE VINUESA, A., AGUAYO GONZALEZ, F., & CORDOBA ROLDAN, A. (2018). PROPOSAL OF A FRAMEWORK FOR THE EVALUATION OF THE SUSTAINABILITY OF PRODUCT SUSTAINABILITY FROM THE PARADIGM OF THE CIRCULAR ECONOMY BASED ON INDUSTRY 4.0. (Part 2). DYNA, 93(1), 488-496. doi:10.6036/8718Nascimento, D. L. M., Alencastro, V., Quelhas, O. L. G., Caiado, R. G. G., Garza-Reyes, J. A., Rocha-Lona, L., & Tortorella, G. (2019). Exploring Industry 4.0 technologies to enable circular economy practices in a manufacturing context. 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Towards the integration of enterprise software: The business manufacturing intelligence

Nowadays, the Information Communication Technology has pervaded literally the companies. In the company circulates an huge amount of information but too much information doesn’t provide any added value. The overload of information exceeds individual processing capacity and slowdowns decision making operations. We must transform the enormous quantity of information in useful knowledge taking in consideration that information becomes obsolete quickly in condition of dynamic market. Companies process this information by specific software for managing, efficiently and effectively, the business processes. In this paper we analyse the myriad of acronyms of software that is used in enterprises with the changes that occurred over the time, from production to decision making until to convergence in an intelligent modular enterprise software, that we named Business Manufacturing Intelligence (BMI), that will manage and support the enterprise in the futurebusiness manufacturing intelligence, enterprise resource planning; business intelligence; management software; automation software; decision making software

The holonic approach for flexible production: a theoretical framework

This paper discusses the body of knowledge about Holonic Approach to theoretically demonstrate how Holonic Production System (HPS) can be a convincing choice to overcome the problems of traditional production systems? architectures. Today, enterprises are trying to find ways to manage the growing environmental complexity that is well described by Complex Systems Theory (CST). After the focus on the main problem regarding environmental complexity, the Holonic system and the Holonic Production System will be analyzed. The paper will focus the potential of HPS to adapt and react to changes in the business environment whilst being able to maintain systemic synergies and coordination through the holonic structure where functional production units are simultaneously autonomous and cooperative