{"id":1059,"date":"2021-01-21T12:48:47","date_gmt":"2021-01-21T12:48:47","guid":{"rendered":"https:\/\/cigip2.webs.upv.es\/wp\/?page_id=1059"},"modified":"2024-02-28T11:02:36","modified_gmt":"2024-02-28T10:02:36","slug":"i4opt","status":"publish","type":"post","link":"https:\/\/cigip.webs.upv.es\/en\/i4opt\/","title":{"rendered":"E. Industrial Production and Logistics Optimization in Industry 4.0 (i4OPT)"},"content":{"rendered":"\t\t
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Proyectos Regionales - a\u00f1o 2021<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Producci\u00f3n industrial y optimizaci\u00f3n log\u00edstica en la industria 4.0 (I4OPT)<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Referencia:\u00a0<\/span>PROMETEO\/2021\/065
Periodo de ejecuci\u00f3n:\u00a0<\/span>01\/01\/2021 – 31\/12\/2023
Tipo:<\/span>\u00a0Investigaci\u00f3n competitiva proyectos
Clave espec\u00edfica:\u00a0<\/span>2021698
Importe:\u00a0<\/span>287.757,12\u20ac
Entidad financiadora:\u00a0<\/span>GENERALITAT VALENCIANA
Investigador principal:\u00a0<\/span>Mula Bru, Josefa
Participantes:\u00a0<\/span>MAndres, B.; D\u00edaz-Madro\u00f1ero Boluda, Francisco Manuel; Sanchis, R.; Rodr\u00edguez Rodr\u00edguez, Ra\u00fal; P\u00e9rez Bernabeu, Elena; Peidro Pay\u00e1, David; Ortiz Bas, \u00c1ngel; Alemany D\u00edaz, Mar\u00eda Del Mar; Vicens Salort, Eduardo; Poler, R.; Juan; Carracedo-Garnateo, Patricia; Juan-P\u00e9rez, \u00c1ngel Alejandro; Torre-Mart\u00ednez, Mar\u00eda Roc\u00edo de la<\/div>

Descripci\u00f3n:<\/span><\/p>

The COVID-19 pandemic has been the greatest health challenge we have known in the last century. Its scale has caused one of the largest disruptions in health systems and supply chains,but has also changed consumer behaviour by generating stockouts in some manufacturing sectors;e.g., consumer products like food, cleaning products and toilet paper, fitness equipment, gardening supplies\u00a0 andhome\u00a0 improvement\u00a0 items,\u00a0 among\u00a0 others.\u00a0 Additionally,\u00a0 the\u00a0 demand\u00a0 levels\u00a0 of\u00a0 other\u00a0 manufacturing\u00a0 sectors\u00a0 have drastically\u00a0 dropped, i.e., textile, automobile, metal and fashion apparel sectors. Thus,manufacturing supply chains are complex networks exposed to constant disruptions. In this situation, supply chain planning and coordination can, in resilience terms, refer to\u00a0 a\u00a0 supply\u00a0 chain’s\u00a0 capacity\u00a0 to\u00a0 anticipate\u00a0 the\u00a0 impacts\u00a0 of\u00a0 disruptions,\u00a0 and\u00a0 to\u00a0 recover\u00a0 from\u00a0 the\u00a0 significant\u00a0 and\u00a0 lengthy\u00a0 impacts caused, which take place or emerge all along the supply chain because of the ripple effect and its consequences. In this project, operational\u00a0 research\u00a0 and\u00a0 artificial\u00a0 intelligence\u00a0 algorithms\u00a0 and\u00a0 technologies\u00a0 for\u00a0 resilient\u00a0 supply\u00a0 chain\u00a0 planning,\u00a0 mainly\u00a0 based\u00a0 on optimisation and simulation, in an Industry 4.0 (I4.0) framework are developed to cope with the ripple effect in the cyber physical supply chain and for the COVID-19 pandemic context.The i4OPT Project aims to bridge the gap between developingacademic and research optimisation algorithms and technologies and using them to solve real problems in industrial companiesby payingspecial attention to artificial intelligence (AI) as the main axis of the fourth industrial revolutionbyproviding facilitating elements for evolution towards I4.0. Manufacturing systems are not a\u00a0 single\u00a0 organic\u00a0 whole,\u00a0 rather \u00a0a\u00a0 compendium\u00a0 of\u00a0 several\u00a0 subsystems\u00a0 integrated\u00a0 in\u00a0 a\u00a0 complex manner.\u00a0 Each\u00a0 company\u00a0 has\u00a0 a variety\u00a0 of\u00a0 management\u00a0 needs\u00a0 that renderthe\u00a0 use\u00a0 of\u00a0 standardised\u00a0 business\u00a0 applications\u00a0 without\u00a0 customisation\u00a0 insufficient\u00a0 to adapt to unique requirements which,in most cases,are those that provideadded value. In addition, the complexity of the current global\u00a0 environment\u00a0 subject\u00a0 to COVID-19\u00a0 pandemicevolution,\u00a0 with\u00a0 highly\u00a0 developed\u00a0 digital\u00a0 technology,\u00a0 highly\u00a0 sophisticated demand\u00a0 and\u00a0 competitive\u00a0 global\u00a0 supply, makes\u00a0 the\u00a0 search\u00a0 for\u00a0 maximum\u00a0 efficiencyin\u00a0 production\u00a0 and\u00a0 logistics\u00a0 operationsviaoptimisation a key component in companies’ decision-making processes. Thus,most real problems in companies are enormous in terms of computing needs to achieve optimisation, and these companies do not have the appropriate software and hardware to solve\u00a0 them\u00a0 in\u00a0 a\u00a0 computationally\u00a0 efficient\u00a0 way. Itcan\u00a0 be presently stated\u00a0 that\u00a0 the\u00a0 most\u00a0 advanced\u00a0 optimisation\u00a0 technologies\u00a0 for modelling and resolution liein the academic and research fields withexperimental datasets, and that R&Dprojects are needed toadapt existing optimisation algorithms and tocreatenew computationally efficient algorithms for solving problems with real data, withthe\u00a0 innovation\u00a0 and\u00a0 transfer\u00a0 of\u00a0 these\u00a0 technologies\u00a0 to\u00a0 companies\u00a0 so\u00a0 they can optimise\u00a0 the\u00a0 use\u00a0 of\u00a0 resources,\u00a0 increase\u00a0 their competitiveness, move towards the digital company and guarantee its sustainability in its three aspects: economic, environmental and social. Optimisation algorithms must be combined with the learning capacity provided byartificial intelligence in such a way that the improvement of the solution will be achieved in both time and qualitytermsby either selectingthe most suitable algorithm combiningseveral of them. Obtaining a satisfactory solution in a shorttimewill improve companies\u2019resilience because they willbe\u00a0 able\u00a0 to\u00a0 adapt\u00a0 more\u00a0 easily\u00a0 to\u00a0 unexpected\u00a0 events\u00a0 through\u00a0 a\u00a0 quick\u00a0 correct\u00a0 response\u00a0 due\u00a0 to\u00a0 the\u00a0 combination\u00a0 of\u00a0 optimisation algorithms, simulation, the use of artificial intelligence and big data handlingcapacities. All this will give rise to the cyber physical or smart factory in an I4.0 context.Enterprises,\u00a0 especially\u00a0 small-and\u00a0 medium-sizedenterprises\u00a0 (SMEs), \u00a0are\u00a0 often\u00a0 constrained\u00a0 by\u00a0 the information\u00a0 systems\u00a0 or\u00a0 ERP (enterprise\u00a0 resource\u00a0 planning)\u00a0 they have\u00a0 installed.\u00a0 When attempting\u00a0 to\u00a0 introduce\u00a0 improvements\u00a0 that\u00a0 must\u00a0 necessarily\u00a0 be implemented intoexisting ERPs, they often find that these ERPs do not allow such adaptations or are excessively costly for them. The i4OPT Project\u00a0 aims\u00a0 to overcome\u00a0 this\u00a0 limitation\u00a0 by\u00a0 providing\u00a0 optimisation\u00a0 services\u00a0 that\u00a0 are\u00a0 interoperable\u00a0 with\u00a0 companies’ ERPs. The i4OPT Project will provide companies, especially SMEs, with new intelligent optimisation algorithms embedded in an open\u00a0 cloud-based platform\u00a0 for\u00a0 the e-optimisation of\u00a0 industrial operations\u00a0 (production,\u00a0 logistics,transport) as\u00a0 a\u00a0 configurable\u00a0 and interoperable service with business applications to improve their competitivenessbasedon four fundamental pillars: (i) efficiency of operations; (ii) resilience; (iii) sustainability;(iv) digitalisation of factories and smart factories. Thus,the aim is to make it easier for\u00a0 companies\u00a0 to\u00a0 base\u00a0 their\u00a0 competitiveness\u00a0 on\u00a0 manufacturing\u00a0 excellence by\u00a0 means\u00a0 ofthe\u00a0 highly\u00a0 efficient\u00a0 implementation\u00a0 and customisation\u00a0 of\u00a0 advanced\u00a0 optimisation\u00a0 capabilities.\u00a0 The i4OPT Cloud Platform\u00a0 will\u00a0 be\u00a0 characterised\u00a0 by\u00a0 being\u00a0 computationally efficient,\u00a0 scalable,\u00a0 extensible\u00a0 and\u00a0 open-sourceand,therefore,low-cost\u00a0 and\u00a0 high\u00a0 added\u00a0 value\u00a0 for\u00a0 companies.\u00a0 The developedintelligent optimisation algorithms, like artificial intelligence, will be characterised by their transparency, capacity to solve complex problems, adaptability, transversality, and necessary and permanent renewal and improvement.<\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Proyectos Regionales – a\u00f1o 2021 Producci\u00f3n industrial y optimizaci\u00f3n log\u00edstica en la industria 4.0 (I4OPT) Referencia:\u00a0PROMETEO\/2021\/065Periodo de ejecuci\u00f3n:\u00a001\/01\/2021 – 31\/12\/2023Tipo:\u00a0Investigaci\u00f3n competitiva proyectosClave espec\u00edfica:\u00a02021698Importe:\u00a0287.757,12\u20acEntidad financiadora:\u00a0GENERALITAT VALENCIANAInvestigador principal:\u00a0Mula Bru, JosefaParticipantes:\u00a0MAndres, B.; D\u00edaz-Madro\u00f1ero Boluda, Francisco Manuel; Sanchis, R.; Rodr\u00edguez Rodr\u00edguez, Ra\u00fal; P\u00e9rez Bernabeu, Elena; Peidro Pay\u00e1, David; Ortiz Bas, \u00c1ngel; Alemany D\u00edaz, Mar\u00eda Del Mar; Vicens Salort, Eduardo; […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":"","footnotes":""},"categories":[102,103,106,641],"tags":[],"class_list":["post-1059","post","type-post","status-publish","format-standard","hentry","category-investigacion","category-proyectos-i-d","category-proyectos-regionales","category-proyectos-regionales-2021"],"_links":{"self":[{"href":"https:\/\/cigip.webs.upv.es\/en\/wp-json\/wp\/v2\/posts\/1059","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cigip.webs.upv.es\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cigip.webs.upv.es\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cigip.webs.upv.es\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/cigip.webs.upv.es\/en\/wp-json\/wp\/v2\/comments?post=1059"}],"version-history":[{"count":0,"href":"https:\/\/cigip.webs.upv.es\/en\/wp-json\/wp\/v2\/posts\/1059\/revisions"}],"wp:attachment":[{"href":"https:\/\/cigip.webs.upv.es\/en\/wp-json\/wp\/v2\/media?parent=1059"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cigip.webs.upv.es\/en\/wp-json\/wp\/v2\/categories?post=1059"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cigip.webs.upv.es\/en\/wp-json\/wp\/v2\/tags?post=1059"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}