Abstract
With the rapid development of various types of industrial big data technologies, in the context of industrial big data and systems science, intelligent optimization algorithms and other technologies have been widely used in the field of intelligent manufacturing. In recent years, it has not only become an important engine for the transformation and upgrading of smart manufacturing industry, but also brought new opportunities and challenges to the development task integrated management of intelligent manufacturing equipment. This paper reviews the research on task integrated management of intelligent manufacturing equipment development from the following four aspects: task analysis and management of intelligent manufacturing equipment in big data environment, task decomposition and resource allocation, task network analysis and evaluation, and task integration analysis and verification evaluation progress. Prospects for further research are pointed out, including the customized research into high-end equipment developed for the individual needs of users, data-driven optimal allocation of resources research, multi-layer interaction of complex network modeling, intelligent systems integration, and verification evaluation.
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Bingöl S, Kılıçgedik HY (2018) Application of gene expression programming in hot metal forming for intelligent manufacturing. Neural Comput Appl 30(3):937–945
Zhong RY, Xu X, Klotz E et al (2017) Intelligent manufacturing in the context of industry 4.0: a review. Engineering 3(5):616–630
Ostrosi E, Fougères AJ (2018) Intelligent virtual manufacturing cell formation in cloud-based design and manufacturing. Eng Appl Artif Intell 76:80–95
Li CQ, Chen YQ, Shang YL (2021) A review of industrial big data for decision making in intelligent manufacturing. Eng Sci Technol Int J. https://doi.org/10.1016/j.jestch.2021.06
Egger J, Masood T (2020) Augmented reality in support of intelligent manufacturing–a systematic literature review. Comput Indus Eng 140:106195
Liang J, Xu WW, Yue CT, Yu KJ, Song H, Crisalle OD, Qu BY (2019) Multimodal multiobjective optimization with differential evolution. Swarm Evol Comput 44(2):1028–1059
Yue CT, Qu BY, Yu KJ, Liang J, Li XD (2019) A novel scalable test problem suite for multimodal multiobjective optimization. Swarm Evol Comput 48(8):62–71
Rajasekhar A, Lynn N, Das S, Suganthan PN (2017) Computing with the collective intelligence of honey bees – a survey. Swarm Evol Comput 32(2):25–48
Xiang S, Xing LN, Wang L, Zou K (2019) Comprehensive learning pigeon-inspired optimization with tabu list. Sci Chin - Info Sci. https://doi.org/10.1007/s11432-018-9728-x
Wang R, Lai SM, Wu GH (2018) Multi-clustering via evolutionary multi-objective optimization. Inf Sci 450:128–140
Yi JH, Xing LN, Wang GG, Dong JY, Vasilakos AV, Alavi AH, Wang L (2019) Behavior of crossover operators in NSGA-III for large-scale optimization problems. Info Sci 509(2):470–487
Chen YB (2017) Integrated and intelligent manufacturing: perspectives and enablers. Engineering 3(5):588–595
Ser JD, Osaba E, Molina D, Yang XS, Salcedo-Sanz S, Camacho D, Das S, Suganthan PN, Coello CAC, Herrera F (2019) Bio-inspired computation: where we stand and what’s next. Swarm Evol Comput 48(8):220–250
Qu BY, Zhu YS, Jiao YC, Wu MY, Suganthan PN, Liang JJ (2018) A survey on multi-objective evolutionary algorithms for the solution of the environmental/economic dispatch problems. Swarm Evol Comput 38(2):1–11
Zhang JW, Wang L, Xing LN (2019) Large-scale medical examination scheduling technology based on intelligent optimization. J Comb Optim 37(1):385–404
Yu PF, Yan XS (2019) Stock price prediction based on deep neural network. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04212-x
Shan SQ, Wen X, Wei YG, Wang ZJ, Chen Y (2020) Intelligent manufacturing in industry 4.0: a case study of Sany heavy industry. Syst Res Behav Sci 37(4):679–690
Yang T, Yi XL, Lu SW et al (2021) Intelligent manufacturing for the process industry driven by industrial artificial intelligence. Engineering. https://doi.org/10.1016/j.eng.2021.04.023
Qiu HX, Duan HB (2020) A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles. Inf Sci 509(1):515–529
Zhang WZ, Li GQ, Zhang WW, Liang J, Yen GG (2019) A cluster based PSO with leader updating mechanism and ring-topology for multimodal multi-objective optimization. Swarm Evol Comput 50(11):100569
Chen H, Xiao X, Wen J (2021) Novel multivariate compositional data’s model for structurally analyzing sub-industrial energy consumption with economic data. Neural Comput Appl 33(8):3713–3735
Wang JL, Xu CQ, Zhang J et al (2021) Big data analytics for intelligent manufacturing systems: a review. J Manuf Syst. https://doi.org/10.1016/j.jmsy.2021.03.005
Xu Y, Luo DL, Li DY, You YC, Duan HB (2019) Target-enclosing affine formation control of two-layer networked spacecraft with collision avoidance. Chin J Aeronaut 32(12):2679–2693
Ren T, Xiao HL, Zhou ZB et al (2019) The Iterative scheme and the convergence analysis of unique solution for a singular fractional differential equation from the eco-economic complex system’s co-evolution process. Complexity 9:1–15
Guo YN, Yang H, Chen MR, Cheng J, Gong DW (2019) Ensemble prediction-based dynamic robust multi-objective optimization methods. Swarm Evol Comput 48(8):156–171
Gong WY, Wang Y, Cai ZH, Wang L (2018) Finding multiple roots of nonlinear equation systems via a repulsion-based adaptive differential evolution. IEEE Trans Syst Man Cyber Syst 5:1–15
Yan XS, Yang KW, Hu CY, Gong WY (2018) Pollution source positioning in a water supply network based on expensive optimization. Desalin Water Treat 110:308–318
Wittek P, Gao SC, Lim IS, Zhao L (2017) Somoclu: an efficient parallel library for self-organizing maps. J Stat Softw 78(1):1–21
Peng T, Liu L (2013) Focused crawling enhanced by CBP–SLC. Knowl-Based Syst 51:15–26
Mullner D (2013) “Fastcluster: fast hierarchical, agglomerative clustering routines for R and Python. J Stat Soft 53(9):1–18
Deng KY, Chen SP, Deng JW (2020) On optimization of web crawler system on scrapy framework. Int J Wireless Mobile Comput 18(4):332–338
Lei M, Yang Z, Wang Y, Xu H, Meng L, Vasquez JC, Guerrero JM (2018) An MPC-based ESS control method for PV power smoothing applications. IEEE Trans Power Electron 33(3):2136–2144
Aktas MF, Behrouzi-Far A Soljanin E Load balancing performance in distributed storage with regular balanced redundancy, In: 2019 XVI International symposium" problems of redundancy in information and control systems" (REDUNDANCY), pp. 75–80, 2019.
Gu SS, Li J, Wang Y, Wang NN, Zhang QY (2019) DR-MDS: an energy-efficient coding scheme in D2D distributed storage network for the internet of things. IEEE Access 7:24179–24191
Luna AC, Meng L, Diaz NL, Graells M, Vasquez JC, Guerrero JM (2018) Online energy management systems for microgrids: experimental validation and assessment framework. IEEE Trans Power Electron 33(3):2201–2215
Lai JG, Lu XQ, Yu XH, Monti A (2019) Cluster-oriented distributed cooperative control for multiple AC microgrids. IEEE Trans Industr Inf 15(11):5906–5918
Miller C, Smith PJ, Dmochowski PA, Tataria H, Molisch AF, “Favorable propagation with user cluster sharing, In: 2020 IEEE 31st Annual international symposium on personal, indoor and mobile radio communications, pp. 1–7, 2020.
Lu XQ, Lai JG (2020) Distributed cluster cooperation for multiple DC MGs over two-layer switching topologies. IEEE Trans Smart Grid 11(6):4676–4687
Ding K, Fan LQ, Liu C (2021) Manufacturing system under I4.0 workshop based on blockchain: research on architecture, operation mechanism and key technologies. Comput Indus Eng. https://doi.org/10.1016/j.cie.2021.107672
Kaufman L Rousseeuw PJ Finding groups in data: an introduction to cluster analysis,” DBLP, pp. 135–141, 1990.
Xu Z, Zhang S, Choo K-KR, Mei L, Wei X, Luo X, Hu C, Liu Y (2017) Hierarchy-cutting model based association semantic for analyzing domain topic on the web. IEEE Trans Industr Inf 13(4):1941–1950
Zhao WX, Wang J, He Y, Nie J-Y, Wen J-R, Li X (2015) Incorporating social role theory into topic models for social media content analysis. IEEE Trans Knowl Data Eng 27(4):1032–1044
AlJadda K, Korayem M, Ortiz C, Grainger T, Miller JA, Rasheed KM, Kochut KJ, Peng H, York WS, Ranzinger R et al (2018) Mining massive hierarchical data using a scalable probabilistic graphical model. Inf Sci 425:62–75
Yadollahi A, Shahraki AG, Zaiane OR (2017) Current state of text sentiment analysis from opinion to emotion mining. ACM Comput Surv (CSUR) 50(2):25
Malki A, Benslimane D, Benslimane S-M, Barhamgi M, Malki M, Ghodous P, Drira K (2016) Data services with uncertain and correlated semantics. World Wide Web 19(1):157–175
Zucco C, Calabrese B, Agapito G, Guzzi PH, Cannataro M (2020) Sentiment analysis for mining texts and social networks data: methods and tools. Wiley Inter Rev Data Min Knowl Discov 10(1):e1333
Uysal MP, Mergen AE (2021) Smart manufacturing in intelligent digital mesh: integration of enterprise architecture and software product line engineering. J Ind Inf Integr. https://doi.org/10.1016/j.jii.2021.100202
Khosravani MR, Nasiri S, Reinicke T (2021) Intelligent knowledge-based system to improve injection molding process. J Ind Inf Integr. https://doi.org/10.1016/j.jii.2021.100275
Zhan J, Dahal B (2017) Using deep learning for short text understanding. J Big Data 4(1):34
Gotti F, Langlais P (2018) From french wikipedia to erudit: a test case for cross-domain open information extraction. Comput Intell 34(2):420–439
Freitag D (2000) Machine learning for information extraction in informal domains. Mach Learn 39(2–3):169–202
Dong X, Gabrilovich E, Heit G, Horn W, Lao N, Murphy K, Strohmann T, Sun SH, Zhang W, “Knowledge vault: a web-scale approach to probabilistic knowledge fusion, In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 601–610,2014.
Deng MQ, Fan TC, Cao JW, Fung SY, Zhang J (2020) Human gait recognition based on deterministic learning and knowledge fusion through multiple walking views. J Franklin Inst 357(4):2471–2491
Zhao XJ, Jia Y, Li AP, Jiang R, Song YC (2020) Multi-source knowledge fusion: a survey. World Wide Web 23(4):2567–2592
Tenorth M, Beetz M, “KnowRob—knowledge processing for autonomous personal robots, In: 2009 IEEE/RSJ international conference on intelligent robots and systems. IEEE, pp. 4261–4266, 2009.
Ferré S, Huchard M, Kaytoue M, Kuznetsov SO, Napoli A (2020) Formal concept analysis: from knowledge discovery to knowledge processing. Guid Tour Artif Intell Res. https://doi.org/10.1007/978-3-030-06167-8_13
Srinivas B, Ayyavaraiah M, Sriramoju SB (2018) A review on security threats and realtime applications towards data mining. Int J Pure Appl Math 118(24):1–11
Sriramoju SB (2017) Opportunities and security implications of big data mining. Int J Res Sci Eng 3(6):44–58
Niranjan A, Nitish A, Deepa Shenoy P, Venugopal KR (2017) Security in data mining - a comprehensive survey. Global J Comput Sci Technol 16(5):51–72
Willson M, Leaver T (2015) Zynga’s FarmVille, social games, and the ethics of big data mining. Commun Res Pract 1(2):147–158
Atlam HF, Wills GB (2020) IoT security, privacy, safety and ethics. Springer, Cham, pp 123–149
Bezuglov A, Comert G (2016) Short-term freeway traffic parameter prediction: application of grey system theory models. Expert Syst Appl 62:284–292
Karadede Y, Ozdemir G, Aydemir E (2017) Breeder hybrid algorithm approach for natural gas demand forecasting model. Energy 141:1269–1284
McQueen DH, Hyland PR, Watson SJ (2004) Monte Carlo simulation of residential electricity demand for forecasting maximum demand on distribution networks. IEEE Trans Power Syst 19(3):1685–1689
Al-Zahrani MA, Abo-Monasar A (2015) Urban residential water demand prediction based on artificial neural networks and time series models. Water Resour Manage 29(10):3651–3662
Azadeh SS, Marcotte P, Savard G (2015) A non-parametric approach to demand forecasting in revenue management. Comput Oper Res 63:23–31
Ghanbari A, Kazemi SM, Mehmanpazir F, Nakhostin MM (2013) A cooperative ant colony optimization-genetic algorithm approach for construction of energy demand forecasting knowledge-based expert systems. Knowl-Based Syst 39:194–206
Manzoor T, Rovenskaya E, Muhammad A (2016) Game-theoretic insights into the role of environmentalism and social-ecological relevance: a cognitive model of resource consumption. Ecol Model 340:74–85
Gao R, Manut AB, Ji Z, Ma J, Duan M, Zhang JF, Franco J, Hatta SWM, Zhang WD, Kaczer B et al (2017) Reliable time exponents for long term prediction of negative bias temperature instability by extrapolation. IEEE Trans Electron Devices 64(4):1467–1473
Bensoussan A, Cakanyıldırım M, Sethi SP (2007) A multiperiod newsvendor problem with partially observed demand. Math Oper Res 32(2):322–344
Ching W, Fung E, Ng M (2003) A higher-order markov model for the newsboy’s problem. J Oper Res Soc 54(3):291–298
Jiang P, Li RR, Lu HY, Zhang XB (2020) Modeling of electricity demand forecast for power system. Neural Comput Appl 32(11):6857–6875
Bodendorf F, Merkl P, Franke J (2021) Intelligent cost estimation by machine learning in supply management: a structured literature review. Comput Ind Eng. https://doi.org/10.1016/j.cie.2021.107601
Jin X, Zhou Y, Mobasher B, “A maximum entropy web recommendation system: combining collaborative and content features,” In Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining. ACM, 2005, pp. 612–617.
Zeng W, Zeng A, Shang M-S, Zhang Y-C (2013) Information filtering in sparse online systems: recommendation via semi-local diffusion. PLoS ONE 8(11):1–9
Yin CY, Shi LF, Sun RX, Wang J (2020) Improved collaborative filtering recommendation algorithm based on differential privacy protection. J Supercomput 76(7):5161–5174
Cui ZH, Xu XH, Xue F, Cai XJ, Cao Y, Zhang WS, Chen JJ (2020) Personalized recommendation system based on collaborative filtering for IoT scenarios. IEEE Trans Serv Comput 13(4):685–695
Almalis N, Tsihrintzis G, Karagiannis N, “A new content-based recommendation algorithm for job recruiting,” In International conference on innovative techniques and applications of artificial intelligence. Springer, pp. 393–398, 2015.
Mobasher B, Cooley R, Srivastava J, “Creating adaptive web sites through usage-based clustering of URLs,” In Proceedings 1999 workshop on knowledge and data engineering exchange (KDEX’99). IEEE, 1999, pp.19–25
Chen S, Huang L, Lei ZW, Wang S (2020) Research on personalized recommendation hybrid algorithm for interactive experience equipment. Comput Intell 36(3):1348–1373
Cai XJ, Hu ZM, Zhao P, Zhang WS, Chen JJ (2020) A hybrid recommendation system with many-objective evolutionary algorithm. Expert Syst Appl 159:113648
Kreutler G, Jannach D, “Personalized needs elicitation in web-based configuration systems,” In: Mass customization: challenges and solutions. Springer, 2006, pp. 27–42.
Moon H, Lee H-H (2012) Product recommendation service in on-line mass customization: consumers cognitive and affective responses. J Korean Soc Cloth Text 36(11):1222–1236
Jiang J, Wang HH (2020) Application intelligent search and recommendation system based on speech recognition technology. Int J Speech Technol 24:1–8
Mashal I, Alsaryrah O, Chuang TY, Yuan FC (2020) A multi-criteria analysis for an internet of things application recommendation system. Technol Soc 60:101216
Li MX, Huang GQ (2021) Production-intralogistics synchronization of industry 4.0 flexible assembly lines under graduation intelligent manufacturing system. Int J Prod Econo. https://doi.org/10.1016/j.ijpe.2021.108272
Draghici G (2015) Infrastructure for integrated collaborative product development in distributed environment. Appl Mech Mater 760:9–14
Tao F, Cheng Y, Da Xu L, Zhang L, Li BH (2014) CCIoT-CMfg: Cloud computing and internet of things-based cloud manufacturing service system. IEEE Trans Industr Inf 10(2):1435–1442
Lu Y (2017) Industry 4.0: a survey on technologies, applications and open research issues. J Ind Inf Integr 6:1–10
Tao F, Cheng Y, Zhang L, Nee AY (2017) Advanced manufacturing systems: socialization characteristics and trends. J Intell Manuf 28(5):1079–1094
Mishra N, Singh A, Kumari S, Govindan K, Ali SI (2016) Cloud-based multi-agent architecture for effective planning and scheduling of distributed manufacturing. Int J Prod Res 54(23):7115–7128
Yuan MH, Zhou Z, Cai XX, Sun C, Gu WB (2020) Service composition model and method in cloud manufacturing. Robot Comput-Integr Manuf 61:101840
Wang K (2020) Migration strategy of cloud collaborative computing for delay-sensitive industrial IoT applications in the context of intelligent manufacturing. Comput Commun 150(15):413–420
Yi S, Tan M, Guo Z, Wen P, Zhou J (2015) Manufacturing task decomposition optimization in cloud manufacturing service platform. Comput Integr Manuf Syst 16(1):1–7
Silvente J, Kopanos GM, Pistikopoulos EN, Espuña A (2015) A rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids. Appl Energy 155:485–501
Wang S-Y, Wang L (2016) An estimation of distribution algorithm-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem. IEEE Trans Syst Man Cyber Syst 46(1):139–149
Zheng H-Y, Wang L (2015) An effective teaching–learning-based optimization algorithm for RCPSP with ordinal interval numbers. Int J Prod Res 53(6):1777–1790
Pe´rez-Rodr´ıguez R, Herna´ndez-Aguirre “An estimation of distribution algorithm-based approach for the order batching problem: an experimental study,” In Handbook of research on military, aeronautical, and maritime logistics and operations. IGI Global, pp. 509–518, 2016.
Yi S, Liu M, Wen P (2016) Overview of cloud manufacturing service based on lifecycle theory. Comput Integr Manuf Syst 22(04):872–883
Li J-Q, Pan Q-K, Tasgetiren MF (2014) A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities. Appl Math Model 38(3):1111–1132
Li J-Q, Pan Q-K, Mao K (2015) A discrete teaching-learning-based optimization algorithm for realistic flowshop rescheduling problems. Eng Appl Artif Intell 37:279–292
Orsva¨rn K, “Principles for libraries of task decomposition methodsconclusions from a case-study,” In: International conference on knowledge engineering and knowledge management. Springer, 1996, pp. 48–65.
Loureiro G, Fulindi JB, Romero AG, de Novaes Kucinskis Lemonge CEA, Vazquez RF, Miyashiro MAS, “Systems concurrent engineering of an electrical ground support equipment for an on-board computer,” In: New world situation: new directions in concurrent engineering. Springer, 2010, pp. 513–526.
Nonsiri S, “An integrated model-based approach for systems engineering-towards the use of model-based approach in the early development stages of the systems engineering process,” pp. 1–19, 2015.
Browning TR (2016) Design structure matrix extensions and innovations: a survey and new opportunities. IEEE Trans Eng Manage 63(1):27–52
Chen Z, Zhu Q, Masood MK, Soh YC (2017) Environmental sensors-based occupancy estimation in buildings via IHMM-MLR. IEEE Trans Industr Inf 13(5):2184–2193
Wang G, Zhang G, Guo X et al (2021) Digital twin-driven service model and optimal allocation of manufacturing resources in shared manufacturing. J Manuf Syst 59:165–179
Trevisan L (2011) Combinatorial optimization: exact and approximate algorithms. Standford University. https://lucatrevisan.github.io/books/cs261.pdf
Boshkovska E, Ng DWK, Zlatanov N, Koelpin A, Schober R (2017) Robust resource allocation for MIMO wireless powered communica- tion networks based on a non-linear EH model. IEEE Trans Commun 65(5):1984–1999
Trautman P, “Assistive planning in complex, dynamic environments: a probabilistic approach,” In: 2015 IEEE international conference on systems, man, and cybernetics. IEEE, pp. 3072–3078, 2015.
Ren T, Li S, Zhang X, Liu L (2017) Maximum and minimum solutions for a nonlocal p-laplacian fractional differential system from eco-economical processes. Bound Value Probl 2017(1):118
Zhou Z, Jin Q, Xiao H, Wu Q, Liu W (2018) Estimation of cardinality constrained portfolio efficiency via segmented dea. Omega 76:28–37
Jahanshahloo GR, Sadeghi J, Khodabakhshi M (2017) Proposing a method for fixed cost allocation using DEA based on the efficiency invariance and common set of weights principles. Math Methods Oper Res 85(2):223–240
Chinnaiah V, Pudi SG, Somasundaram TS, Basha SS (2018) A cloud resource allocation strategy based on fitness based live migration and clustering. Wireless Pers Commun 98(3):2943–2958
Lamond BF, Sodhi MS, Noël M, Assani OA (2014) Dynamic speed control of a machine tool with stochastic tool life: Analysis and simulation. J Intell Manuf 25(5):1153–1166
Rubio J, Munoz O, Pascual-Iserte A (2016) A stochastic approach for resource allocation with backhaul and energy harvesting constraints. IEEE Trans Veh Technol 65(7):5788–5797
Wang L, Pei J, Menhas MI, Pi J, Fei M, Pardalos PM (2017) A hybrid-coded human learning optimization for mixed-variable optimization problems. Knowl-Based Syst 127:114–125
Liu Y, Zhang L, Tao F, Wang L (2017) Resource service sharing in cloud manufacturing based on the gale–shapley algorithm: advantages and challenge. Int J Comput Integr Manuf 30(4–5):420–432
Zoulfaghari H, Nematian J, Nezhad AAK (2016) A resource-constrained project scheduling problem with fuzzy activity times. Int J Fuzzy Syst Appl 5(4):1–15
Sukkerd W, Wuttipornpun T (2016) Hybrid genetic algorithm and tabu search for finite capacity material requirement planning system in flexible flow shop with assembly operations. Comput Ind Eng 97:157–169
Kadri RL, Boctor FF (2018) An efficient genetic algorithm to solve the resource-constrained project scheduling problem with transfer times: the single mode case. Eur J Oper Res 265(2):454–462
Yang J, Yang S, Ni P (2016) A vector tabu search algorithm with enhanced searching ability for pareto solutions and its application to multiobjective optimizations. IEEE Trans Magn 52(3):1–4
Mei Y, Omidvar MN, Li X, Yao X (2016) A competitive divide-and- conquer algorithm for unconstrained large-scale black-box optimization. ACM Trans Math Soft 42(2):13
Poloczek M, Schnitger G, Williamson DP, Van Zuylen A (2017) Greedy algorithms for the maximum satisfiability problem: simple algorithms and inapproximability bounds. SIAM J Comput 46(3):1029–1061
Rajabzadeh M, Haghighat AT (2017) Energy-aware framework with markov chain-based parallel simulated annealing algorithm for dynamic management of virtual machines in cloud data centers. J Supercomput 73(5):2001–2017
Módos I, Šůcha P, Hanzálek Z (2017) Algorithms for robust production scheduling with energy consumption limits. Comput Indus Eng 112:391–408
Chen H, Zhu X, Liu G, Pedrycz W (2018) Uncertainty-aware online scheduling for real-time workflows in cloud service environment. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2018.2866421
Singh SP, Kumar A (2018) Multiobjective differential evolution using homeostasis based mutation for application in software cost estimation. Appl Intell 48(3):628–650
Hao X, Gen M, Lin L, Suer GA (2017) Effective multiobjective EDA for bi-criteria stochastic job-shop scheduling problem. J Intell Manuf 28(3):833–845
Yuan MH, Li YD, Zhang LZ et al (2021) Research on intelligent workshop resource scheduling method based on improved NSGA-II algorithm. Robot Comput-Integr Manuf. https://doi.org/10.1016/j.rcim.2021.102141
Xu FL, Yin ZY, Gu A et al (2021) Adaptive scheduling strategy of fog computing tasks with different priority for intelligent production lines. Proc Comput Sci 183:311–317
Simeone A, Caggiano A, Boun L et al (2021) Cloud-based platform for intelligent healthcare monitoring and risk prevention in hazardous manufacturing contexts. Proc CIRP 99:50–56
Vasco RA, Morabito R (2016) The dynamic vehicle allocation problem with application in trucking companies in Brazil. Comput Oper Res 76:118–133
Khakifirooz M, Chien CF, Chen Y-J (2018) Bayesian inference for mining semiconductor manufacturing big data for yield enhancement and smart production to empower industry 4.0. Appl Soft Comput 68:990–999
O’Donovan P, Leahy K, Bruton K, O’Sullivan DT (2015) An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. J Big Data 2(1):1–26
Mahapatra C, Moharana A, Leung V (2017) Energy management in smart cities based on internet of things: peak demand reduction and energy savings. Sensors 17(12):2812
Li J-Q, Pan Q-K, Liang Y-C (2010) An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems. Comput Ind Eng 59(4):647–662
Berlian MH, Sahputra TER, Ardi BJW, Dzatmika LW , Besari ARA, Sudibyo RW, Sukaridhoto S, “Design and implementation of smart environment monitoring and analytics in real-time system framework based on internet of underwater things and big data,” In: 2016 International electronics symposium (IES). IEEE, pp. 403–408, 2016.
Garcia-de Prado A, Ortiz G, Boubeta-Puig J (2017) COLLECT: COLLaborativE ConText-aware service oriented architecture for intelligent decision-making in the Internet of Things. Expert Syst Appl 85:231–248
Zhong RY, Xu C, Chen C, Huang GQ (2017) Big data analytics for physical internet-based intelligent manufacturing shop floors. Int J Prod Res 55(9):2610–2621
Chen W (2020) Intelligent manufacturing production line data monitoring system for industrial internet of things. Comput Commun 151:31–41
Pinilla SL, Llorente RR, Toledo GN et al (2019) TRLs 5–7 advanced manufacturing centres, practical model to boost technology transfer in manufacturing. Sustainability 11(18):4890
Escudero GG, de Lucio PF, Barrio HG et al (2021) Machine learning en el campo de la fabricación. DYNA 96(6):600–604
Quintana G, Campa FJ, Ciurana J et al (2011) Productivity improvement through chatter-free milling in workshops. Proc Inst Mech Eng Part B J Eng Manuf 225(7):1163–1174
Pimenov DY, Bustillo A, Mikolajczyk T (2018) Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth. J Intell Manuf 29(5):1045–1061
Bustillo A, Pimenov DY, Mia M et al (2021) Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth. J Intell Manuf 32(3):895–912
Baruwa OT, Piera MA (2016) A coloured petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles. Int J Prod Res 54(16):4773–4792
Neamatollahi P, Abrishami S, Naghibzadeh M, Moghaddam MHY, Younis O (2018) Hierarchical clustering-task scheduling policy in cluster-based wireless sensor networks. IEEE Trans Industr Inf 14(5):1876–1886
Murtadha MK, Noordin NK, Ali BM, Hashim F (2017) Design and simulation analysis of network-based fully distributed mobility management in flattened network architecture. Telecommun Syst 65(2):253–267
Kolodenkova AE, Muntyan ER, “Researches of interaction of actors with use fuzzy hypergraph and cognitive modeling,” In: 2018 XIV international scientific-technical conference on actual problems of electronics instrument engineering (APEIE). IEEE, pp. 127– 131, 2018.
Payan AP, Mavris DN (2016) Multilevel probabilistic morphological analysis for facilitating modeling and simulation of notional scenarios. Syst Eng 19(1):3–23
Fang C, Marle F, Xie M (2017) Applying importance measures to risk analysis in engineering project using a risk network model. IEEE Syst J 11(3):1548–1556
Parraguez P, Eppinger S, Maier A (2016) Characterizing design process interfaces as organization networks: insights for engineering systems management. Syst Eng 19(2):158–173
Tien I, Der Kiureghian A (2017) Reliability assessment of critical infrastructure using Bayesian networks. J Infrastruct Syst 23(4):04017025
Zou J, Chang Q, Arinez J et al (2017) Dynamic production system diagnosis and prognosis using model-based data-driven method. Expert Syst Appl 80:200–209
Qian R, Zhang B, Yue Y, Wang Z, Coenen F, “Robust chinese traffic sign detection and recognition with deep convolutional neural network,” In: 2015 11th international conference on natural computation (ICNC). IEEE, 2015, pp. 791–796.
Zhang J-W, Liu J-B, Li R-N (2013) Robustness-based discrete time/cost trade-off project scheduling. Comput Integr Manuf Syst 11:2884–2892
Almaktoom AT, Krishnan KK, Wang P, Alsobhi S (2014) Assurance of system service level robustness in complex supply chain networks. Int J Adv Manuf Technol 74(1–4):445–460
Lamas P, Demeulemeester E (2016) A purely proactive scheduling procedure for the resource-constrained project scheduling problem with stochastic activity durations. J Sched 19(4):409–428
Emamian S, Jalali Naini SG, Shahanaghi K (2017) Application of particle swarm optimization and robust net present value for BOT-type contracts. Transport Plan Technol 40(8):901–913
Aouchiche M, Hansen P (2016) Proximity, remoteness and distance eigenvalues of a graph. Discret Appl Math 213:17–25
Wang XH, Yuan Z, Wang LZ et al (2020) Robustness evaluation method for unmanned aerial vehicle swarms based on complex network theory. Chin J Aeronaut 33(1):352–364
Ratnayake P, Weragoda S, Wansapura J et al (2021) Quantifying the robustness of complex networks with heterogeneous nodes. Mathematics 9(21):2769
Lu X, Wang HP, Deng Y (2015) Evaluating the robustness of temporal networks considering spatiality of connections. Chaos Solitons Fractals 78:176–184
Then M, Günnemann S, Kemper A, Neumann T (2017) Efficient batched distance, closeness and betweenness centrality computation in unweighted and weighted graphs. Datenbank-Spektrum 17(2):169–182
Yang F, Zhang R, Yang Z, Hu R, Li M, Yuan Y, Li K (2017) Identifying the most influential spreaders in complex networks by an extended local k-shell sum. Int J Mod Phys C 28(01):1750014
Zhao X, Liu F, Wang J, Li T et al (2017) Evaluating influential nodes in social networks by local centrality with a coefficient. ISPRS Int J Geo Inf 6(2):35
Nielsen I, Axehill D (2018) Low-rank modifications of riccati factorizations for model predictive control. IEEE Trans Autom Control 63(3):872–879
Lin TY, Jia ZX, Yang C et al (2021) Evolutionary digital twin: a new approach for intelligent industrial product development. Adv Eng Inform. https://doi.org/10.1016/j.aei.2020.101209
Zhou J, Li P, Zhou Y et al (2018) Toward new-generation intelligent manufacturing. Engineering 4(1):11–20
Fan LY, Zhang L (2021) Multi-system fusion based on deep neural network and cloud edge computing and its application in intelligent manufacturing. Neural Comput Appl. https://doi.org/10.1007/s00521-021-05735-y
Wan JF, Li JP, Hua QS et al (2020) Intelligent equipment design assisted by Cognitive Internet of Things and industrial big data. Neural Comput Appl 32(9):4463–4472
Patriarca R, Di Gravio G, Costantino F (2017) A Monte Carlo evolution of the functional resonance analysis method (FRAM) to assess performance variability in complex systems. Saf Sci 91:49–60
Kucherov S, Lipko J, Schevchenko O, “The integrated life cycle model of configurable information system,” In: 2014 IEEE 8th international conference on application of information and communication technologies (AICT). IEEE, 2014, pp. 1–5.
Rodríguez A, Ortega F, Concepción R (2017) An intuitionistic method for the selection of a risk management approach to information technology projects. Info Sci 375:202–218
Medcof JW, Lee T (2017) The effects of the chief technology officer and firm and industry R&D intensity on organizational performance. R&D Manag 47(5):767–781
Lee S, Park J-G, Lee J (2015) Explaining knowledge sharing with social capital theory in information systems development projects. Ind Manag Data Syst 115(5):883–900
Manupati VK, Chang P, Tiwari MK (2016) Intelligent search techniques for network-based manufacturing systems: multi-objective formulation and solutions. Int J Comput Integr Manuf 29(8):850–869
Tiwari SR (2015) Knowledge integration in government–industry project network. Knowl Process Manag 22(1):11–21
Chakra HA, Tannir A, Ashi AT (2017) Validating the integration among project management knowledge areas in lebanon. Int J Innov Manag Technol 8(1):38
Williams BD, Roh J, Tokar T, Swink M (2013) Leveraging supply chain visibility for responsiveness: The moderating role of internal integration. J Oper Manag 31(7–8):543–554
Vasin YGE, Yasakov YVE (2016) Distributed database management system for integrated processing of spatial data in a GIS. Comput Opt 40(6):919–928
Brettel M, Heinemann F, Engelen A, Neubauer S (2011) Cross-functional integration of R&D, marketing, and manufacturing in radical and incremental product innovations and its effects on project effectiveness and efficiency. J Prod Innov Manag 28(2):251–269
Edgar TF, Pistikopoulos EN (2018) Smart manufacturing and energy systems. Comput Chem Eng 114:130–144
Cook I, Coates G (2016) Optimising the time-based design structure matrix using a divide and hybridise algorithm. J Eng Des 27(4–6):306–332
Son S, Kim J, Ahn J (2017) Design structure matrix modeling of a supply chain management system using biperspective group decision. IEEE Trans Eng Manage 64(2):220–233
Yang Q, Yao T, Lu T, Zhang B (2014) An overlapping-based design structure matrix for measuring interaction strength and clustering analysis in product development project. IEEE Trans Eng Manage 61(1):159–170
da Cunha Barbosa GE, de Souza GFM (2017) A risk-based framework with design structure matrix to select alternatives of product modernization. J Eng Des 28(1):23–46
Rostami S, Shenfield A (2017) A multi-tier adaptive grid algorithm for the evolutionary multi-objective optimization of complex problems. Soft Comput 21(17):4963–4979
Chen H, Tian Y, Pedrycz W, Wu G, Wang R, Wang L (2019) Hyperplane assisted evolutionary algorithm for many-objective optimization problems. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2019.2899225
Starke AR, Cardemil JM, Escobar R, Colle S (2018) Multi-objective optimization of hybrid CSP+PV system using genetic algorithm. Energy 147:490–503
Korssen T, Dolk V, van de Mortel-Fronczak J, Reniers M, Heemels M (2018) Systematic model-based design and implementation of supervisors for advanced driver assistance systems. IEEE Trans Intell Transp Syst 19(2):533–544
Hakamada K, Okamoto M, Hanai T (2006) Novel technique for pre-processing high dimensional time-course data from DNA microarray: mathematical model-based clustering. Bioinformatics 22(7):843–848
Backhaus J, Reinhart G (2017) Digital description of products, processes and resources for task-oriented programming of assembly systems. J Intell Manuf 28(8):1787–1800
Cassettari L, Bendato I, Mosca M, Mosca R (2017) Energy resources intelligent management using on line real-time simulation: a decision support tool for sustainable manufacturing. Appl Energy 190:841–851
Lechevalier D, Shin S-J, Rachuri S, Foufou S, Lee YT, Bouras A (2017) Simulating a virtual machining model in an agent-based model for advanced analytics. J Intell Manuf 30:1–19
Bousdekis A, Magoutas B, Apostolou D, Mentzas G (2018) Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance. J Intell Manuf 29(6):1303–1316
Vidal JC, Lama M, Díaz-Hermida F, Bugarín A (2013) A petri net model for changing units of learning in runtime. Know-Based Syst 41:26–42
Mejía G, Niño K, Montoya C, Sánchez MA, Palacios J, Amodeo L (2016) A petri net-based framework for realistic project management and scheduling: an application in animation and videogames. Comput Oper Res 66:190–198
Mittal S, Risco-Mart´ın JL, “DEVSML 3.0 stack: rapid deployment of DEVS farm in distributed cloud environment using microservices and containers,” In: Proceedings of the symposium on theory of modeling & simulation. Society for Computer Simulation International, 2017, p. 19.
Kurapati S, Kourounioti I, Lukosch H, Tavasszy L, Verbraeck A (2018) Fostering sustainable transportation operations through corridor management: a simulation gaming approach. Sustainability 10(2):455
Tsadimas A, Kapos G-D, Dalakas V, Nikolaidou M, Anag-nostopoulos D (2016) Simulating simulation-agnostic SysML models for enterprise information systems via DEVS. Simul Model Pract Theory 66:243–259
Ali S, Mostafa GA, Leila E (2020) An elastic controller using Colored Petri Nets in cloud computing environment. Clust Comput 23(2):1045–1071
Wang L, Lida R, Wyglinski AM (2019) Vehicular network simulation environment via discrete event system modeling. IEEE ACCESS 7:87246–87264
Cardin O, Trentesaux D, Thomas A, Castagna P, Berger T, El-Haouzi HB (2017) Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges. J Intell Manuf 28(7):1503–1517
Haq AN, Boddu V (2017) Analysis of enablers for the implementation of leagile supply chain management using an integrated fuzzy QFD approach. J Intell Manuf 28(1):1–12
DeSmit Z, Elhabashy AE, Wells LJ, Camelio JA (2017) An approach to cyber-physical vulnerability assessment for intelligent manufacturing systems. J Manuf Syst 43:339–351
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This research work is supported by the National Natural Science Foundation of China (61773120). It is also supported by the Founda-tion for the Author of National Excellent Doctoral Dissertation of China (2014-92), and the Hunan Key Laboratory of Intelligent Logistics Technology (2019TP1015), Beijing intelligent logistics system collabo-rative innovation center open funding project (BILSCIC-2019KF-25).
Rights and permissions
About this article
Cite this article
Ren, T., Luo, T., Li, S. et al. Review on R&D task integrated management of intelligent manufacturing equipment. Neural Comput & Applic 34, 5813–5837 (2022). https://doi.org/10.1007/s00521-022-07023-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-022-07023-9