Integration of industrial IoT architectures for dynamic scheduling

Computers & Industrial Engineering - Tập 171 - Trang 108387 - 2022
Tiago Coito1, Bernardo Firme1, Miguel S.E. Martins1, Andrea Costigliola2, Rafael Lucas2, João Figueiredo3, Susana M. Vieira1, João M.C. Sousa1
1IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
2Hovione FarmaCiencia, S.A., Lisboa, Portugal
3IDMEC, Universidade de Évora, Évora, Portugal

Tài liệu tham khảo

Aazam, 2018, Deploying Fog Computing in Industrial Internet of Things and Industry 4.0, IEEE Transactions on Industrial Informatics, 14, 4674, 10.1109/TII.2018.2855198 An, 2018, Hypergraph clustering model-based association analysis of DDOS attacks in fog computing intrusion detection system, Eurasip Journal on Wireless Communications and Networking, 2018, 10.1186/s13638-018-1267-2 Angelopoulos, 2020, Tackling faults in the Industry 4.0 era—a survey of machine-learning solutions and key aspects, Sensors (Switzerland), 20, 1 Ashton, 2009, That ‘internet of things’ thing, RFID Journal. Bader, 2019, Structuring reference architectures for the industrial Internet of Things, Future Internet, 11, 1, 10.3390/fi11070151 Baker, 2020, A secure fog-based platform for SCADA-based IoT critical infrastructure, Software - Practice and Experience, 50, 503, 10.1002/spe.2688 Barber, 2019, WiSer: A highly available HTAP DBMS for IoT applications, ArXiv, 268 Branda, A., Castellano, D., Guizzi, G., & Popolo, V. (2021). Metaheuristics for the flow shop scheduling problem with maintenance activities integrated. Computers and Industrial Engineering, 151(November 2020), 106989. https://doi.org/10.1016/j.cie.2020.106989. Bruckner, 2019, OPC UA TSN A new Solution for Industrial Communication, Proceedings of the IEEE, 107, 1, 10.1109/JPROC.2018.2888703 Caiza, 2020, Fog computing at industrial level, architecture, latency, energy, and security: A review, Heliyon, 6, 10.1016/j.heliyon.2020.e03706 Çakici, 2011, Using RFID for the management of pharmaceutical inventory-system optimization and shrinkage control, Decision Support Systems, 51, 842, 10.1016/j.dss.2011.02.003 Camacho-Cogollo, 2020, Chapter 4 - RFID technology in health care, 33 Chakrabortty, R. K., Rahman, H. F., Haque, K. M. A., Paul, S. K., & Ryan, M. J. (2021). An event-based reactive scheduling approach for the Resource Constrained Project Scheduling Problem with unreliable resources. Computers and Industrial Engineering, 151(April 2020), 106981. https://doi.org/10.1016/j.cie.2020.106981. Chakrabortty, 2017, Resource constrained project scheduling with uncertain activity durations, Computers and Industrial Engineering, 112, 537, 10.1016/j.cie.2016.12.040 Chalapathi, 2019, Industrial Internet of Things (IIoT) Applications of Edge and Fog Computing, A Review and Future Directions., 1–15 Chang, 2018, Chapter 1: Internet of Things (IoT) and new computing paradigms., ArXiv, 1 Chongwatpol, 2013, RFID-enabled track and traceability in job-shop scheduling environment, European Journal of Operational Research, 227, 453, 10.1016/j.ejor.2013.01.009 Coito, 2022, Digital Twin of a Flexible Manufacturing System for Solutions Preparation, Automation, 3, 153, 10.3390/automation3010008 Coito, 2021, Intelligent Sensors for Real-Time Decision-Making. Automation, 2, 62 Coito, 2022, Assessing the impact of automation in pharmaceutical quality control labs using a digital twin, Journal of Manufacturing Systems, 62, 270, 10.1016/j.jmsy.2021.11.014 Coito, 2020, A Middleware Platform for Intelligent Automation: An Industrial Prototype Implementation, Computers in Industry, 123, 10.1016/j.compind.2020.103329 Coito, 2019, A Novel Framework for Intelligent Automation, IFAC-PapersOnLine, 52, 1825, 10.1016/j.ifacol.2019.11.501 Coito, 2021, The Impact of Intelligent Automation in Internal Supply Chains, International Journal of Integrated Supply Management, 1, 1, 10.1504/IJISM.2021.113563 Cunha, 2019, Dual Resource Constrained Scheduling for Quality Control Laboratories, IFAC-PapersOnLine, 52, 1421, 10.1016/j.ifacol.2019.11.398 Delamare, 2019, Evaluation of an UWB localization system in static and dynamic, CEUR Workshop Proceedings, 2498, 80 FDA-cGMP/compliance. (2008). FDA Guidance for Industry: Part 11, Electronic Records, Electronic Signatures: Scope and Application. In Division of Drug Information (Issue Cvm). Fernández-Caramés, 2018, A fog computing based cyber-physical system for the automation of pipe-related tasks in the Industry 4.0 shipyard, Sensors (Switzerland), 18 Ferreira, 2020, Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems, 10.1007/978-3-030-50146-4_19 Firme, 2020, Multi-agent system for dynamic scheduling, Proceedings of the International Joint Conference on Neural Networks Guo, 2015, An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment, International Journal of Production Economics, 159, 16, 10.1016/j.ijpe.2014.09.004 Hamzadayi, 2016, Event driven strategy based complete rescheduling approaches for dynamic m identical parallel machines scheduling problem with a common server, Computers and Industrial Engineering, 91, 66, 10.1016/j.cie.2015.11.005 Huang, 2017, A Real-time Location System Based on RFID and UWB for Digital Manufacturing Workshop, Procedia CIRP, 63, 132, 10.1016/j.procir.2017.03.085 Jin, 2019, Intelligent vibration detection and control system of agricultural machinery engine, Measurement: Journal of the International Measurement Confederation, 145, 503, 10.1016/j.measurement.2019.05.059 Joshi, 2017, The Industrial Internet of Things Volume G5: Connectivity Framework, Industrial Internet Consortium, December, 129 Kammergruber, 2014, The future of the laboratory information system - What are the requirements for a powerful system for a laboratory data management?, Clinical Chemistry and Laboratory Medicine, 52, e225 Kundakci, 2016, Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem, Computers and Industrial Engineering, 96, 31, 10.1016/j.cie.2016.03.011 Lavassani, 2018, Combining fog computing with sensor mote machine learning for industrial IoT, Sensors (Switzerland), 18, 10.3390/s18051532 Li, 2021, An effective MCTS-based algorithm for minimizing makespan in dynamic flexible job shop scheduling problem, Computers and Industrial Engineering, 155 Limon, 2020, Dynamic resource scheduling of biomanufacturing projects, Computers and Industrial Engineering, 147 Liu, 2019, Outsourcing and rescheduling for a two-machine flow shop with the disruption of new arriving jobs: A hybrid variable neighborhood search algorithm, Computers and Industrial Engineering, 130, 198, 10.1016/j.cie.2019.02.015 Lu, 2014, Robust single machine scheduling for minimizing total flow time in the presence of uncertain processing times, Computers and Industrial Engineering, 74, 102, 10.1016/j.cie.2014.04.013 Luo, C., Tözün, P., Tian, Y., Barber, R., Raman, V., & Sidle, R. (2019). Umzi: Unified multi-zone indexing for large-scale HTAP. Advances in Database Technology - EDBT, 2019-March, 1–12. https://doi.org/10.5441/002/edbt.2019.02. Ma, 2019, A computational experiment to explore better robustness measures for project scheduling under two types of uncertain environments, Computers and Industrial Engineering, 131, 382, 10.1016/j.cie.2019.04.014 Martins, 2020, Reinforcement learning for dual-resource constrained scheduling, IFAC-PapersOnLine, 53, 10810, 10.1016/j.ifacol.2020.12.2866 Maslaton, 2012, Resource scheduling in QC laboratories, Pharmaceutical Engineering, 32, 68 Montazerolghaem, 2020, Load-Balanced and QoS-Aware Software-Defined Internet of Things, IEEE Internet of Things Journal, 7, 3323, 10.1109/JIOT.2020.2967081 O’Donovan, 2019, A comparison of fog and cloud computing cyber-physical interfaces for Industry 4.0 real-time embedded machine learning engineering applications, Computers in Industry, 110, 12, 10.1016/j.compind.2019.04.016 Omer, 2018, Indoor distance estimation for passive UHF RFID tag based on RSSI and RCS, Measurement: Journal of the International Measurement Confederation, 127, 425, 10.1016/j.measurement.2018.05.116 Omg, 2011, Business Process Model and Notation (BPMN) Version 2.0, Object Management Group specification (Issue January) Open fog reference architecture for fog computing. (2017). Open Fog Consortium Architecture Working Group, February, 1–162. https://www.iiconsortium.org/pdf/OpenFog_Reference_Architecture_2_09_17.pdf. Ouelhadj, D., & Petrovic, S. (2009). A survey of dynamic scheduling in manufacturing systems. Journal of Scheduling, 12(4), 417–431. https://doi.org/10.1007/s10951-008-0090-8. Pacciarelli, D., & D’Ariano, A. (2009). Assessing the value of RFID in pharmaceutical production scheduling. In IFAC Proceedings Volumes (IFAC-PapersOnline) (Vol. 42, Issue 4 PART 1). IFAC. https://doi.org/10.3182/20090603-3-RU-2001.0215. Pinedo, 2016 Prabhu, 2017, Trends in Analytical Chemistry The dawn of unmanned analytical laboratories, Trends in Analytical Chemistry, 88, 41, 10.1016/j.trac.2016.12.011 Prasad, 2012, Trends in laboratory information management system, Chemometrics and Intelligent Laboratory Systems, 118, 187, 10.1016/j.chemolab.2012.07.001 Raza, 2020, Adaptive HTAP through Elastic Resource Scheduling, Proceedings of the ACM SIGMOD International Conference on Management of Data, 2043–2054 Rossit, D. A., Tohmé, F., & Delgadillo, G. M. (2020). The Tolerance Scheduling Problem in a Single Machine Case. In International Series in Operations Research and Management Science (Vol. 289, pp. 255–273). https://doi.org/10.1007/978-3-030-43177-8_13. Rossit, D. A., Tohmé, F., & Frutos, M. (2019a). A data-driven scheduling approach to smart manufacturing. Journal of Industrial Information Integration, 15(December 2018), 69–79. https://doi.org/10.1016/j.jii.2019.04.003. Rossit, 2019, Industry 4.0: Smart Scheduling, International Journal of Production Research, 57, 3802, 10.1080/00207543.2018.1504248 Ruiz-Sarmiento, J. R., Monroy, J., Moreno, F. A., Galindo, C., Bonelo, J. M., & Gonzalez-Jimenez, J. (2020). A predictive model for the maintenance of industrial machinery in the context of Industry 4.0. Engineering Applications of Artificial Intelligence, 87. https://doi.org/10.1016/j.engappai.2019.103289. Schrecker, 2016, The Industrial Internet of Things Volume G4: Security Framework, Industrial Internet Consortium, 129 Shahrabi, 2017, A reinforcement learning approach to parameter estimation in dynamic job shop scheduling, Computers and Industrial Engineering, 110, 75, 10.1016/j.cie.2017.05.026 Sharma, D. K., Bhargava, S., & Singhal, K. (2020). Chapter 6 - Internet of Things applications in the pharmaceutical industry. In V. E. Balas, V. K. Solanki, & R. B. T.-A. I. I. A. for P. I. G. Kumar (Eds.), An Industrial IoT Approach for Pharmaceutical Industry Growth Volume 2 (pp. 153–190). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-821326-1.00006-1. Shi-Wan, 2017, The Industrial Internet of Things Volume G1: Reference Architecture, Industrial Internet Consortium, Version, 1, 58 Seiten Sun, 2019, Intelligent sensor-cloud in fog computer: A novel hierarchical data job scheduling strategy, Sensors (Switzerland), 19, 10.3390/s19235083 Syafrudin, 2018, Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing, Sensors (Switzerland), 18, 10.3390/s18092946 Telecommunication Standardization Sector of ITU. (2012). Overview of the Internet of things. In International Telecommunication Union (ITU). https://www.itu.int/ITU-T/recommendations/rec.aspx?rec=y.2060. Tom, 2017, IoT based SCADA integrated with Fog for power distribution automation, Iberian Conference on Information Systems and Technologies, CISTI Trakadas, 2019, Hybrid clouds for data-intensive, 5G-enabled IoT applications: An overview, key issues and relevant architecture, Sensors (Switzerland), 19, 10.3390/s19163591 Trunzer, E., Prata, P., Vieira, S., & Vogel-Heuser, B. (2019). Concept and Evaluation of a Technology-independent Data Collection Architecture for Industrial Automation. IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, 1, 2830–2836. https://doi.org/10.1109/iecon.2019.8927399. U.S. Department of Health and Human Services. (2016). Data Integrity and Compliance With CGMP Guidance for Industry. Pharmaceutical Quality/Manufacturing Standards (CGMP), April 2016, 20993–2. http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm495891.pdf. Urso, 2020, An RFID application for the process mapping automation, Procedia Manufacturing, 42, 8, 10.1016/j.promfg.2020.02.017 Venkatesan, 2004, Leveraging radio frequency identification (RFID) technology to improve laboratory information management, American Laboratory, 36 Verba, 2019, Modeling Industry 4.0 based fog computing environments for application analysis and deployment, Future Generation Computer Systems, 91, 48, 10.1016/j.future.2018.08.043 Vieira, 2003, Rescheduling manufacturing systems: A framework of strategies, policies, and methods, Journal of Scheduling, 6, 39, 10.1023/A:1022235519958 Wan, 2018, Artificial Intelligence for Cloud-Assisted Smart Factory, IEEE Access, 6, 55419, 10.1109/ACCESS.2018.2871724 Wang, 2021, The evolution of the Internet of Things (IoT) over the past 20 years, Computers and Industrial Engineering, 155 Weiser, 1999, The origins of ubiquitous computing research at PARC, IBM Systems Journal, 38, 693, 10.1147/sj.384.0693 Weyrich, 2016, Reference architectures for the internet of things, IEEE Software, 33, 112, 10.1109/MS.2016.20 Wollschalaeger, M., Sauter, T., & Jasperneite, J. (2017). The future of industrial communication: automation networks in the era of the internet of things and Industry 4.0. IEEE Ind Electron Mag, march. https://doi.org/10.4324/9781315132143. World Health Organization, 2016, WHO Expert Committee on Specifications for Pharmaceutical Preparations _Annex 5 Guidance on good data and record management practices, WHO Technical Report Series, 996, 165 Wu, 2017, A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing, Journal of Manufacturing Systems, 43, 25, 10.1016/j.jmsy.2017.02.011 Yang, 2020, Beyond beaconing: Emerging applications and challenges of BLE, Ad Hoc Networks, 97, 10.1016/j.adhoc.2019.102015 Zhang, 2013, A hybrid genetic algorithm and tabu search for a multi-objective dynamic job shop scheduling problem, International Journal of Production Research, 51, 3516, 10.1080/00207543.2012.751509 Zhong, 2013, RFID-enabled real-time manufacturing execution system for mass-customization production, Robotics and Computer-Integrated Manufacturing, 29, 283, 10.1016/j.rcim.2012.08.001 Zhong, R. Y., Huang, G. Q., & Dai, Q. (2013). Mining standard operation times for real-time advanced production planning and scheduling from RFID-enabled shopfloor data. In IFAC Proceedings Volumes (IFAC-PapersOnline) (Vol. 46, Issue 9). IFAC. https://doi.org/10.3182/20130619-3-RU-3018.00166. Zhong, 2015, A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing, Advanced Engineering Informatics, 29, 799, 10.1016/j.aei.2015.01.002 Zonta, 2020, Predictive maintenance in the Industry 4.0: A systematic literature review, Computers and Industrial Engineering, 150