Towards the 5th industrial revolution: a literature review and a framework for process optimization based on big data analytics and semantics

• Autorzy: Mourtzis Dimitris

Identyfikatory

DOI

10.36897/jme/141834

• Języki publikacji: EN

Abstrakty

EN

The digitalization of modern manufacturing systems has resulted to increasing data generation, also known as Big Data. Although there are several technologies and techniques under the term Data Analytics for gathering such data, their interpretation to information, and ultimately to knowledge remains in its infancy. Consequently, albeit engineers currently can monitor the factory level, optimization is cut off of the data acquisition, and is based on data related methodologies. The focus should be pivoted on designing and developing suitable frameworks for integrating Big Data to process optimization based on the context of information gathered from the shopfloor. This paper aims to investigate the opportunities and the gaps as well as the challenges arising in the current industrial landscape, towards the efficient utilization of Big Data, for process optimization based on the integration of semantics. To that end, a literature review is performed, and a data-based framework is presented.

Słowa kluczowe

EN

data analytics; process optimization; semantics; Industry 5.0

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2021
• Tom: Vol. 21, No. 3
• Strony: 5--39
• Opis fizyczny: Bibliogr. 92 poz., rys., tab.

Twórcy

autor

Mourtzis Dimitris

• Laboratory for Manufacturing Systems and Automation (LMS), Department of Mechanical Engineering and Aeronautics, University of Patras, Greece

• https://orcid.org/0000-0002-7923-7363

Bibliografia

• [1] EUROSTAT, 2019, Employment Specialisation of EU Regions, Available at: https://ec.europa.eu/eurostat/web/products-eurostat-news/-/DDN-20190917-1.
• [2] CLARK D., 2021, Number of Employees in Europe 2020, Available at: https://www.statista.com/statistics/1195197/employment-by-sector-in-europe/.
• [3] LABOR MARKET BRIEFING SERIES, 2020, The Manufacturing Sector in Europe, Available at: https://cdn4.euraxess.org/sites/default/files/labor_market_information-_manufacturing_sector.pdf.
• [4] LANZA G., FERDOWS K., KARA S., MOURTZIS D., SCHUH G., VÁNCZA J., WANG L., WIENDAHL H.P., 2019, Global Production Networks: Design and Operation, CIRP Annals, 68/2, 823–841, DOI: https://doi.org/10.1016/j.cirp.2019.05.008.
• [5] CHRYSSOLOURIS G., 2006, Manufacturing Systems: Theory and Practice (2nd ed.), New York, NY, Springer Verlag.
• [6] MOURTZIS D., 2011, Internet Based Collaboration in the Manufacturing Supply Chain, CIRP Journal of Manufacturing Science and Technology, 4/3, 296–304, DOI: https://doi.org/10.1016/j.cirpj.2011.06.005.
• [7] LU Υ., XU X., WANG L., 2020, Smart Manufacturing Process and System Automation – A Critical Review of the Standards and Envisioned Scenarios, Journal of Manufacturing Systems, 56, 312–325. DOI: https://doi.org/10.1016/j.jmsy.2020.06.010.
• [8] MOURTZIS D., DOUKAS, M., 2014, The Evolution of Manufacturing Systems: From craftsmanship to the Era of customization. Design and Management of Lean Production Systems, IGI Global, 1–29, DOI: http://dx.doi.org/10.4018/978-1-4666-5039-8.
• [9] YANG F., GU S., 2021, Industry 4.0, a Revolution that Requires Technology and National Strategies, Complex Intell. Systems, 7, 1311–1325, DOI: https://doi.org/10.1007/s40747-020-00267-9.
• [10] MOURTZIS D., MILAS N., VLACHOU E., LIAROMATIS J., 2018, Digital Transformation of Structural Steel Manufacturing Enabled by IoT-Based Monitoring and Knowledge Reuse, International Conference on Control. Decision and Information Technologies CoDIT’18, 295–301, DOI: https://doi.org/10.1109/CoDIT.2018.8394874.
• [11] MOURTZIS D., MILAS N., ATHINAIOS N., 2016, Towards Machine Shop 4.0: A General Machine Model for CNC machine-tools through OPC-UA, Procedia CIRP 78, 301–306, DOI: HTTPS://DOI.ORG/10.1016/J.PROCIR.2018.09.045.
• [12] MOURTZIS D., VLACHOU E., 2018, A Cloud-Based Cyber-Physical System for Adaptive Shop-Floor Scheduling and Condition-Based Maintenance, Journal of manufacturing systems, 47, 179–198. DOI: https://doi.org/10.1016/j.jmsy.2018.05.008.
• [13] MOURTZIS D., 2020, Simulation in the Design and Operation of Manufacturing Systems: State of the Art and New Trends, International Journal of Production Research, 58/7, 1927–1949, DOI: https://doi.org/10.1080/00207543.2019.1636321.
• [14] Industry 5.0: Towards a Sustainable, Human-Centric and Resilient European industry, DOI: https://doi.org/10.2777/308407.
• [15] BENOTSMANE R., KOVÁCS G., & DUDÁS L., 2019, Economic, Social Impacts and Operation of Smart Factories in Industry 4.0 Focusing on Simulation and Artificial Intelligence of Collaborating Robots, Social Sciences, 8/5, 143, DOI: http://dx.doi.org/10.3390/socsci8050143.
• [16] MOURTZIS D., 2020, Machine Tool 4.0 in the Era of Digital Manufacturing, Proceedings of the 32nd European Modeling & Simulation Symposium (EMSS 2020), 416–429, DOI: https://doi.org/10.46354/i3m.2020.emss.060.
• [17] LIU C., VENGAYIL H., ZHONG Y.R., XU X., 2018, A Systematic Development Method for Cyber-Physical Machine Tools, Journal of Manufacturing Systems, 48, 13–24, DOI: https://doi.org/10.1016/j.jmsy.2018.02.001.
• [18] ROMERO D., BERNUS P., NORAN O., STAHRE J., FAST-BERGLUND Å., 2016, The Operator 4.0: Human Cyber-Physical Systems & Adaptive Automation Towards Human-Automation Symbiosis Work Systems, IFIP International Conference on Advances in Production Management Systems. Springer, Cham, DOI: https://doi.org/10.1007/978-3-319-51133-7_80.
• [19] GAZZANEO L., PADOVANO A., UMBRELLO S., 2020, Designing Smart Operator 4.0 for Human Values: A Value Sensitive Design Approach, Procedia Manufacturing, 42, 219–226, DOI: https://doi.org/10.1016/j.promfg.2020.02.073.
• [20] GAO R., WANG L., HELU M., TETI R., 2020, Big Data Analytics for Smart Factories of the Future, CIRP Annals, 69/2, 668–92, DOI: https://doi.org/10.1016/j.cirp.2020.05.002.
• [21] MOURTZIS D., DOUKAS M., PSAROMMATIS F., 2014, Design of Manufacturing Networks for Mass Customisation Using an Intelligent Search Method, International Journal of Computer Integrated Manufacturing, 28/7, 679–700, DOI: https://doi.org/10.1080/0951192X.2014.900867.
• [22] MOURTZIS D., DOUKAS M., PSAROMMATIS F., 2015, A Toolbox for the Design, Planning and Operation of Manufacturing Networks in a Mass Customisation Environment, Journal of Manufacturing Systems, 36, 274–286, DOI: https://doi.org/10.1016/j.jmsy.2014.06.004.
• [23] MOURTZIS D., DOUKAS M., PSAROMMATIS F., 2013, Design and Operation of Manufacturing Networks for Mass Customisation, CIRP Annals, 62/1, 467–470, DOI: https://doi.org/10.1016/j.cirp.2013.03.126.
• [24] WANG G., GUNASEKARAN A., NGAI E.W.T., PAPADOPOULOS T., 2016, Big Data Analytics in Logistics and Supply Chain Management: Certain Investigations for Research and Applications, Int. J. Prod. Econ. 176, 98–110, DOI: https://doi.org/10.1016/j.ijpe.2016.03.014.
• [25] KUSIAK A., 2017, Smart Manufacturing Must Embrace Big Data, Nature 544, 7648, DOI: https://doi.org/10.1038/544023a.
• [26] ZHONG R.Y., NEWMAN S.T., HUANG G.Q., LAN S., 2016, Big Data for Supply Chain Management in the Service and Manufacturing Sectors: Challenges, Opportunities, and Future Perspectives, Comput. Ind. Eng., 10, DOI: https://doi.org/10.1016/j.cie.2016.07.013.
• [27] MOURTZIS D., GARGALLIS A., ZOGOPOULOS V., 2019, Modelling of Customer-Oriented Applications in Product Lifecycle Using RAMI 4.0, Procedia Manufacturing, 28, 31–36, DOI: https://doi.org/10.1016/j.promfg.2018.12.006.
• [28] WEYRICH M., EBERT C., 2016, Reference Architectures for the Internet of Things, IEEE Software, 33/1, 112–116, DOI: 10.1109/MS.2016.20.
• [29] FORREST C., 2016, Big Data, Business Analytics to hit $203 Billion by 2020, Says IDC report, Available at: https://www.techrepublic.com/article/big-data-business-analytics-to-hit-203-billion-by-2020-says-idc-report/.
• [30] IBM, 2020, Building a Robust, Governed Data Lake for AI, Available at: https://www.ibm.com/downloads/cas/RMAMZNRY.
• [31] KURTZ J., SHOCKEY R., 2013, Analytics: The Real-World Use of Big Data in Manufacturing, IBM Global Business Services Business Analytics and Optimization, Available at: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/viewer.html?pdfurl=https%3A%2F%2Fwww.ibm.com%2Fdownloads%2Fc as%2FONBGKB82.
• [32] Big Data Market Size Revenue Forecast Worldwide from 2011 to 2027, Available at: https://www.statista.com/statistics/254266/global-big-data-market-forecast/.
• [33] WANG L., 2019, From Intelligence Science to Intelligent Manufacturing, Engineering, 5/4, 615–8, DOI: https://doi.org/10.1016/j.eng.2019.04.011.
• [34] RÜSMANN M., LORENZ M., GERBERT P., WALDNER M., et al., 2015, Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries, Boston, The Boston Consulting Group.
• [35] MEERMANN L., 2019, Sensors As Drivers of Industry 4.0: A study on Germany, Switzerland and Austria, EY Building a better working world.
• [36] DEMIR K. A., HALIL C., 2018, The Next Industrial Revolution: Industry 5.0 and Discussions on Industry 4.0, Industry 4.0 from the Management Information Systems Perspectives, Peter Lang Publishing House.
• [37] SKOBELEV P., YU., B.S., 2017, On the Way from Industry 4.0 to Industry 5.0: from Digital Manufacturing to Digital Society, Industry 4.0, 2/6, 307–311.
• [38] CISCO, 2020, The Internet of Everything – Cisco IoE Value Index Study, Available at: https://www.cisco.com/c/dam/en_us/about/business-insights/docs/ioe-value-index-faq.pdf.
• [39] MOURTZIS D., VLACHOU E., MILAS N., DIMITRAKOPOULOS G., 2016, Energy Consumption Estimation for Machining Processes Based on Real-Time Shop Floor Monitoring via Wireless Sensor Networks, Procedia CIRP, 57, 637–642, DOI: https://doi.org/10.1016/j.procir.2016.11.110.
• [40] MÜLLER J.P., FISCHER K., 2014, Application Impact of Multi-agent Systems and Technologies: A Survey, In: Shehory O., Sturm A., (eds) Agent-Oriented Software Engineering, Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-642-54432-3_3.
• [41] MODONI G.E., DOUKAS M., TERKAJ W., SACCO M., MOURTZIS D., 2017, Enhancing Factory Data Integration Through the Development of an Ontology: from the Reference Models Reuse to the Semantic Conversion of the Legacy Models, International Journal of Computer Integrated Manufacturing, 30, 1043–1059. DOI: https://doi.org/10.1080/0951192X.2016.1268720.
• [42] MALEKI E., BELKADI F., BOLIET N., al., 2018, Ontology-Based Framework Enabling Smart Product-Service Systems: Application of Sensing Systems for Machine Health Monitoring, IEEE Internet of Things Journal, 5/6, 4496–4505, DOI: https://doi.org/10.1109/JIOT.2018.2831279.
• [43] RZEVSKI G., SKOBELEV P., 2007, Emergent Intelligence in Large Scale Multi-Agent Systems, International Journal of Education and Information Technology, 1/2, 64–71.
• [44] VITTIKH V., 2016, Evergetics: Science of Intersubjective Management Processes in Everyday Life, International Journal of Management Concepts and Philosophy, 9, 63, DOI: https://doi.org/10.1504/IJMCP.2016.077763.
• [45] TAOULI A., DJAMEL A.B., KESKES N., BENCHERIF K., HASSAN B., 2018, Semantic for Big Data Analysis: A survey, NTIS2018, BigData & Internet of things IoT.
• [46] FRICKÉ M.H., 2018, Data-Information-Knowledge-Wisdom (DIKW) Pyramid, Framework, Continuum. In: Schintler L., McNeely C., (eds) Encyclopedia of Big Data, Springer, Cham, DOI: https://doi.org/10.1007/978-3319-32001-4_331-1.
• [47] YAO X., LIAN Z., YANG Y., ZHANG Y., JIN H., 2014, Wisdom Manufacturing: New Humans-Computers-Things Collaborative Manufacturing Model, Computer Integrated Manufacturing Systems, 20/6, 1490–1498. DOI: http://dx.doi.org/10.13196/j.cims.2014.06.yaoxifan.1490.9.20140627.
• [48] OXFORD LEARNER’S DICTIONARY, Available at: https://www.oxfordlearnersdictionaries.com/us/definition/english/wisdom?q=WISDOM.
• [49] ROWLEY J., 2007, The Wisdom Hierarchy: Representations of the DIKW Hierarchy, Journal of Information Science, 33/2, 163–180, DOI: https://doi.org/10.1177/0165551506070706.
• [50] ZHENG X., HU Y., XIE K., ZHANG W., SU L., LIU M., 2015, An Evolutionary Trend Reversion Model for Stock Trading Rule Discovery, Knowledge-Based Systems 79, 27–35. DOI: https://doi.org/10.1016/j.knosys.2014.08.010.
• [51] KHODKH P., LAWANGE S., BHAGAT A., DONGRE K., INGOLE C., 2010, Query Processing Over Large RDF Using Sparql in Big Data, Communications of the ACM, DOI: http://dx.doi.org/10.1145/2905055.2905124.
• [52] LEIDA M., RUIZ C., CERAVOLO P., 2016, Facing Big Data Variety in a Model Driven Approach, IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), 1–6, DOI: https://doi.org/10.1109/RTSI.2016.7740641.
• [53] KELLER R.M., RANJAN S., WEI M.Y., ESHOW M.M., 2016, Semantic Representation and Scale-up of Integrated Air Traffic Management Data, Proceedings of the International Workshop on Semantic Big Data, 1–6. DOI: https://doi.org/10.1145/2928294.2928296.
• [54] ISSA H.,L. ELST V., DENGEL A., 2016, Using Smartphones for Prototyping Semantic Sensor Analysis Systems, SBD'16, Proceedings of the International Workshop on Semantic Big Data 7, 1–6, DOI: https://doi.org/10.1145/2928294.2928299.
• [55] KAISLER S., ARMOUR F., ESPINOSA J., MONEY W., 2013, Big Data: Issues and Challenges Moving Forward, 46th Hawaii International Conference on System Sciences (HICSS), 995–1004, DOI: http://dx.doi.org/10.1109/HICSS.2013.645.
• [56] ZHANG X., ZHAO J., LECUN Y., 2015, Character-Level Convolutional Networks for Text Classification, Proceedings of the 28th International Conference on Neural Information Processing Systems, 1, 649–657.
• [57] NURAL M., COTTERELL M., MILLER J., 2015, Using Semantics in Predictive Big Data Analytics, IEEE International Congress on Big Data, 254–261, DOI: https://doi.org/10.1109/BigDataCongress.2015.43.
• [58] BANAGE T.G., S. KUMARA I., PAIK J., ZHANG T.H.A., SIRIWEERA S., KOSWATTE K.R.C., 2015, Ontology-Based Workflow Generation for Intelligent Big Data Analytic, IEEE International Conference on Web Services, 495–502, DOI: https://doi.org/10.1109/ICWS.2015.72.
• [59] YAO Y., ZHANG L., YI J., PENG Y., HU W., SHI L., 2016, A Framework for Big Data Security Analysis and the Semantic Technology, 6th International Conference on IT Convergence and Security (ICITCS), 1–4. DOI: https://doi.org/10.1109/ICITCS.2016.7740303.
• [60] NURAL M.V., COTTERELL M.E., PENG H., XIE R., MA P., MILLER J., 2015, Automated Predictive Big Data Analytics Using Ontology Based Semantics, International Journal of Big Data 2/2, 43–56, DOI: https://doi.org/10.29268/stbd.2015.2.2.4.
• [61] NAJAFABADI M.M., VILLANUSTRE F., KHOSHGOFTAAR T.M., SELIYA N., WALD R., MUHAREMAGIC E., 2015, Deep Learning Applications and Challenges in Big Data Analytics, Journal of Big Data, 2/1, 1–21, DOI: https://doi.org/10.1186/s40537-014-0007-7.
• [62] BENGIO Y., COURVILLE A., VINCENT P., 2013, Representation Learning: A Review and New Perspectives, Pattern Analysis and Machine Intelligence, IEEE Transactions, 35/8, 1798–1828. DOI: https://doi.org/10.1109/TPAMI.2013.50.
• [63] BENGIO Y., LECUN Y., 2007, Scaling Learning Algorithms Towards AI, In: Bottou L, Chapelle O., DeCoste D., Weston J., (eds) Large Scale Kernel Machines, MIT Press, Cambridge, MA, 34, 321–360, Available at: http://www.iro.umontreal.ca/~lisa/pointeurs/bengio+lecun_chapter2007.pdf.
• [64] HINTON G., SALAKHUTDINOV R., 2010, Discovering Binary Codes for Documents by Learning Deep Generative Models, Topics Cogn. Sci. 3/1, 74–91, DOI: https://doi.org/10.1111/j.1756-8765.2010.01109.x.
• [65] RANZATO M, SZUMMER M., 2008, Semi-Supervised Learning of Compact Document Representations with Deep Networks, Proceedings of the 25th International Conference on Machine Learning, ACM, 792–799.
• [66] MIKOLOV T., CHEN K., DEAN J., 2013, Efficient Estimation of Word Representations in Vector Space, CoRR: Computing Research Repository, 1–12.
• [67] CHENG S., SHI Y., QIN Q., BAI R., 2013, Swarm Intelligence in Big Data Analytics, International Conference on Intelligent Data Engineering and Automated Learning, Springer, 417–426.
• [68] ABDUL-RAHMAN S., BAKAR A.A., MOHAMED-HUSSEIN Z.A., 2013, Optimizing Big Data in Bioinformatics with Swarm Algorithms, Computational Science and Engineering (CSE), 1091–1095, DOI: https://doi.org/10.1109/CSE.2013.158.
• [69] GOVINDARAJAN K., SOMASUNDARAM T.S., KUMAR V.S., et al., 2013, Continuous Clustering in Big Data Learning Analytics, Technology for Education (T4E), 2013 IEEE Fifth International Conference, 61–64, DOI: https://doi.org/10.1109/T4E.2013.23.
• [70] TANNAHILL B.K., JAMSHIDI M., 2014, System of Systems and Big Data analytics, Bridging the gap, Computers and Electrical Engineering, 40/1, 40th-year commemorative issue, 2–15.
• [71] CABANAS-ABASCAL A., GARCÍA-MACHICADO E., PRIETO-GONZÁLEZ L., DE AMESCUA SECO A., 2013, An Item based Geo-Recommender System Inspired by Artificial Immune Algorithms, J. Univers. Comput. Sci., 19, 2013–2033.
• [72] LEE W.-P., HSIAO Y.-T., HWANG W.-C.., 2014, Designing a Parallel Evolutionary Algorithm for Inferring Gene Networks on the Cloud Computing Environment, BMC Systems Biology, 8/1, 1–19.
• [73] SUN W., ZHANG N., WANG H., YIN W., QIU T., 2013, PACO: A Period ACO Based Scheduling Algorithm in Cloud Computing, Cloud Computing and Big Data (CloudCom-Asia), 2013 International Conference on 482–486.
• [74] BARBA GONZÁLEZ C., 2018, Big Data Optimization: Algorithmic Framework for Data Analysis Guided by Semantics, PhD thesis, University of Malaga.
• [75] NEBRO A.J., RUIZ A.B., BARBA-GONZÁLEZ C., GARCÍA-NIETO J., LUQUE M., ALDANA-MONTES J.F., 2018, InDM2: Interactive Dynamic Multi-Objective Decision Making Using Evolutionary Algorithms, Swarm and Evolutionary Computation 40, 184–195.
• [76] NOY N.F., MCGUINNESS D.L., 2001, Ontology Development 101: A Guide to Creating your First Ontology, Available at: https://protege.stanford.edu/publications/ontology_development/ontology101.pdf.
• [77] STAAB S., STUDER R., 2003, Handbook on Ontologies, Springer Science & Business Media, DOI: https://doi.org/10.1007/978-3-540-92673-3.
• [78] MCGUINNESS D.L. VAN HARMELEN F., 2004, OWL Web Ontology Language Overview, W3C Recommendation 10.10.
• [79] HARRIS S., SEABORNE A., PRUD’HOMMEAUX E., 2013, SPARQL 1.1 Query Language, W3C Recommendation 21.10.
• [80] GROSOF B.N. POON T.C., 2004, SweetDeal: Representing Agent Contracts with Exceptions Using SemanticWeb Rules, Ontologies, and Process Descriptions, Int. Journal of Electronic Commerce, 8/4, 61–97.
• [81] COMPTON M., BARNAGHI P., BERMUDEZ L., GARCIA-CASTRO R., et al., 2012, The SSN Ontology of the W3C Semantic Sensor Network Incubator Group, Web Semantics: Science, Services and Agents on the World Wide Web, 17.
• [82] MOURTZIS D., VLACHOU E., MILAS N.J.P.C., 2016, Industrial Big Data As a Result of IoT Adoption in Manufacturing, Procedia CIRP, 55, 290–295, DOI: https://doi.org/10.1016/j.procir.2016.07.038.
• [83] ZHANG J., WANG J., LYU Y., BAO J., 2019, Big Data Driven Intelligent Manufacturing, Zhongguo Jixie Gongcheng/China Mech. Eng., 30/2, DOI: https://doi.org/10.3969/j.issn.1004-132X.2019.02.001.
• [84] GLIMM B., STUCKENSCHMIDT H., 2016, 15 years of semantic web: An incomplete survey, KI-Künstliche Intelligenz, 30/2, 117–130.
• [85] TAYLOR A., 2015, Semantics for Dummies, John Wiley & Sons, Inc.
• [86] BAUMGAERTEL H., 2017, Semantic Web in Industrial Companies - Methods, Architectures, Applications, DOI: 10.13140/RG.2.2.15745.56166.
• [87] WANG J., XU C., ZHANG J., ZHONG R., 2021, Big Data Analytics for Intelligent Manufacturing Systems: A review, Journal of Manufacturing Systems, DOI: https://doi.org/10.1016/j.jmsy.2021.03.005.
• [88] TILBURY D.M., 2019, Cyber-Physical Manufacturing Systems, Annual Review of Control, Robotics, and Autonomous Systems, 2, 427–443, DOI: https://doi.org/10.1146/annurev-control-053018-023652.
• [89] CULOT G., FATTORI F., PODRECCA M., SARTOR M., 2019, Addressing Industry 4.0 Cybersecurity Challenges, IEEE Engineering Management Review, 47/3, 79–86, DOI: 10.1109/EMR.2019.2927559.
• [90] LEZZI M., LAZOI M., CORALLO A., 2018, Cybersecurity for Industry 4.0 in the current literature: A reference framework, Computers in Industry, 103, 97–110, DOI: https://doi.org/10.1016/j.compind.2018.09.004.
• [91] MÜLLER J., DIRECTORATE-GENERAL for RESEARCH and INNOVATION (EUROPEAN COMMISSION), 2020, Enabling Technologies for Industry 5.0, DOI: https://doi.org/10.2777/082634.
• [92] DALENOGARE L.S., BENITEZ G.B., AYALA N.F., FRANK A.G., 2018, The Expected Contribution of Industry 4.0 Technologies for Industrial Performance, International Journal of Production Economics, 204, 383–394. DOI: https://doi.org/10.1016/j.ijpe.2018.08.019.