Top

Operations Management Research

Published in:

01-06-2023

Integration of artificial intelligence in sustainable manufacturing: current status and future opportunities

Authors: Rohit Agrawal, Abhijit Majumdar, Anil Kumar, Sunil Luthra

Published in: Operations Management Research | Issue 4/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Manufacturing firms often struggle to attain the optimum balance of environmental, economic, and social goals. Sustainable Manufacturing (SM) is one of the ways to balance the aforesaid aspects. Many disruptive technologies such as Artificial Intelligence (AI), blockchain, machine learning, the Internet of Things, and Big Data, are contributing immensely to the digitalisation in SM. This article aims to explore the trends of AI applications in SM during the period of 2010–2021 by conducting a systematic literature review and bibliometric and network analyses. Prominent research themes, namely sustainable scheduling, smart manufacturing and remanufacturing, energy consumption, sustainable practices and performances, and smart disassembly and recovery have been identified through network analysis. Content analysis of extant literature reveals that Genetic Algorithm (GA), Artificial Neural Network (ANN), and Fuzzy Logic are the most widely used AI techniques in SM. Potential future research directions like amalgamation of AI with Industry 4.0, use of hybrid AI systems, focus on social sustainability and use of emerging AI techniques (Deep learning, CNN etc.) have also been proposed. The intellectual map of AI in SM delineated in this article will be helpful for the researchers as well as industry practitioners in their future endeavours.

previous article Agile supply chain management based on critical success factors and most ideal risk reduction strategy in the era of industry 4.0: application to plastic industry

next article An optimization model for routing—location of vehicles with time windows and cross-docking structures in a sustainable supply chain of perishable foods

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

Abubakr M, Abbas AT, Tomaz I et al (2020) Sustainable and smart manufacturing: an integrated approach. Sustain (Switzerland) 12:1–19

Agrawal R, Vinodh S (2020) Sustainability evaluation of additive manufacturing processes using grey-based approach. Grey Syst Theory Appl 10:393–412CrossRef

Agrawal R, Wankhede VA, Kumar A et al (2021) Nexus of circular economy and sustainable business performance in the era of digitalization. Int J Product Perform Manage 71(3):748–774CrossRef

Ahmad T, Zhang D, Huang C et al (2021) Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. J Clean Prod 289

Akbari M, Hopkins JL (2022) Digital technologies as enablers of supply chain sustainability in an emerging economy. Oper Manag Res 15:689–710. https://doi.org/10.1007/s12063-021-00226-8CrossRef

Ali SS, Kaur R, Persis DJ et al (2020) Developing a hybrid evaluation approach for the low carbon performance on sustainable manufacturing environment. Ann Oper Res. https://doi.org/10.1007/s10479-020-03877-1CrossRef

Ali M, Prakash K, Hossain MA, Pota HR (2021) Intelligent energy management: evolving developments, current challenges, and research directions for sustainable future. J Clean Prod 314:127904CrossRef

Amjad MS, Rafique MZ, Khan MA (2021) Leveraging optimized and cleaner production through industry 4.0. Sustain Prod Consum 26:859–871CrossRef

Arnold C, Kiel D, Voigt K-I (2016) How the industrial internet of things changes business models in different manufacturing industries. Int J Innov Manag 20(08):1640015. https://doi.org/10.1142/S1363919616400156CrossRef

Bag S, Pretorius JHC (2020) Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework. Int J Organ Anal 30(4):864–898CrossRef

Bag S, Pretorius JHC, Gupta S, Dwivedi YK (2021) Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technol Forecast Soc Change 163:120420CrossRef

Bai L, Garcia FJS, Mishra AR (2022) Adoption of the sustainable circular supply chain under disruptions risk in manufacturing industry using an integrated fuzzy decision-making approach. Oper Manage Res 15:743–759CrossRef

Bala PK (2012) Improving inventory performance with clustering based demand forecasts. J Modelling Manage 7:23–37CrossRef

Bangsa AB, Schlegelmilch BB (2020) Linking sustainable product attributes and consumer decision-making: insights from a systematic review. J Clean Prod 245:118902CrossRef

Bhanot N, Rao PV, Deshmukh SG (2016) Identifying the perspectives for sustainability enhancement: a text mining approach for a machining process. J Adv Manage Res 13:244–270CrossRef

Bhinge R, Park J, Law KH et al (2017) Toward a Generalized Energy Prediction Model for Machine Tools. J Manuf Sci Eng Trans ASME 139(4):041013CrossRef

Bonino B, Giannini F, Monti M, Raffaeli R (2023) Shape and context-based Recognition of Standard Mechanical Parts in CAD Models. Comput Aided Des 155:103438CrossRef

Borade AB, Sweeney E (2015) Decision support system for vendor managed inventory supply chain: a case study. Int J Prod Res 53:4789–4818CrossRef

Carbonneau R, Vahidov R, Laframboise K (2007) Machine learning-based demand forecasting in supply chains. Int J Intell Inf Technol 3:40–57CrossRef

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 190:841–851CrossRef

Chaaban K (2023) A New Algorithm for Real-Time Scheduling and Resource Mapping for Robot Operating Systems (ROS). Appl Sci 13:1532CrossRef

Chan FTS, Li N, Chung SH, Saadat M (2017) Management of sustainable manufacturing systems-a review on mathematical problems. Int J Prod Res 55:1210–1225CrossRef

Cioffi R, Travaglioni M, Piscitelli G et al (2020) Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustain (Switzerland) 12(2):492

Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197CrossRef

Deng Z, Zhang H, Fu Y et al (2017) Optimization of process parameters for minimum energy consumption based on cutting specific energy consumption. J Clean Prod 166:1407–1414CrossRef

Dhiaf MM, Atayah OF, Nasrallah N, Frederico GF (2021) Thirteen years of Operations Management Research (OMR) journal: a bibliometric analysis and future research directions. Oper Manage Res 14:235–255CrossRef

Fahimnia B, Sarkis J, Davarzani H (2015) Green supply chain management: a review and bibliometric analysis. Int J Prod Econ 162:101–114CrossRef

Fallahpour A, Wong KY, Olugu EU, Musa SN (2017) A Predictive Integrated Genetic-Based model for supplier evaluation and selection. Int J Fuzzy Syst 19:1041–1057CrossRef

Feng Y, Hong Z, Li Z et al (2020a) Integrated intelligent green scheduling of sustainable flexible workshop with edge computing considering uncertain machine state. J Clean Prod 246

Feng Y, Zhao Y, Zheng H et al (2020b) Data-driven product design toward intelligent manufacturing: a review. Int J Adv Robot Syst 17. https://doi.org/10.1177/1729881420911257

Ferrari A, Mangano G, Cagliano AC, de Marco A (2022) 4.0 technologies in city logistics: an empirical investigation of contextual factors. Oper Manage Res 16:345–362CrossRef

Fertig A, Weigold M, Chen Y (2022) Machine learning based quality prediction for milling processes using internal machine tool data. Adv Industrial Manuf Eng 4:100074CrossRef

Firdaus A, Razak MFA, Feizollah A et al (2019) The rise of “blockchain”: bibliometric analysis of blockchain study. Scientometrics 120:1289–1331CrossRef

Ghadimi P, Azadnia AH, Mohd Yusof N, Mat Saman MZ (2012) A weighted fuzzy approach for product sustainability assessment: a case study in automotive industry. J Clean Prod 33:10–21CrossRef

Gholizadeh H, Fazlollahtabar H (2020) Robust optimization and modified genetic algorithm for a closed loop green supply chain under uncertainty: case study in melting industry. Comput Ind Eng 147:106653CrossRef

Gokulachandran J, Mohandas K (2015) Comparative study of two soft computing techniques for the prediction of remaining useful life of cutting tools. J Intell Manuf 26:255–268CrossRef

Golini R, Gualandris J (2018) An empirical examination of the relationship between globalization, integration and sustainable innovation within manufacturing networks. Int J Oper Prod Manage 38:874–894CrossRef

Govindan K, Hasanagic M (2018) A systematic review on drivers, barriers, and practices towards circular economy: a supply chain perspective. Int J Prod Res 56:278–311CrossRef

Govindan K, Diabat A, Madan Shankar K (2015) Analyzing the drivers of green manufacturing with fuzzy approach. J Clean Prod 96:182–193CrossRef

Govindan K, Shaw M, Majumdar A (2021) Social sustainability tensions in multi-tier supply chain: a systematic literature review towards conceptual framework development. J Clean Prod 279:123075CrossRef

Goyal D, Pabla BS, Dhami SS, Lachhwani K (2017) Optimization of condition-based maintenance using soft computing. Neural Comput Appl 28:829–844CrossRef

Graafland J, Smid H (2017) Reconsidering the relevance of social license pressure and government regulation for environmental performance of european SMEs. J Clean Prod 141:967–977CrossRef

Guo X, Zhou M, Abusorrah A et al (2021) Disassembly sequence planning: a Survey. IEEE/CAA J Automatica Sinica 8:1308–1324CrossRef

Habidin NF, Mohd Zubir AF, Mohd Fuzi N et al (2018) Critical success factors of sustainable manufacturing practices in malaysian automotive industry. Int J Sustain Eng 11:217–222CrossRef

Henn M-A, Zhou H, Barnes BM (2019) Data-driven approaches to optical patterned defect detection. OSA Contin 2:2683–2693CrossRef

Huang J, Jin L, Zhang C (2017) Mathematical modeling and a hybrid NSGA-II algorithm for process planning problem considering machining cost and carbon emission. Sustain (Switzerland) 9(10):1769

Jagadish BS, Ray A (2019) Development of fuzzy logic-based decision support system for multi-response parameter optimization of green manufacturing process: a case study. Soft Comput 23:11015–11034CrossRef

Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685CrossRef

Jia Z-H, Zhang Y-L, Leung JY-T, Li K (2017) Bi-criteria ant colony optimization algorithm for minimizing makespan and energy consumption on parallel batch machines. Appl Soft Comput J 55:226–237CrossRef

Jo D-S, Kim T-W, Kim J-W (2020) Intelligent rework process management system under smart factory environment. Sustain (Switzerland) 12:1–17

Karuppiah K, Sankaranarayanan B, Ali SM, Paul SK (2021) Key challenges to sustainable humanitarian supply chains: Lessons from the covid-19 pandemic. Sustain (Switzerland) 13(11):5850

Khan AM, Jamil M, Salonitis K et al (2019) Multi-objective optimization of energy consumption and surface quality in nanofluid SQCl assisted face milling. Energies (Basel) 12(4):710CrossRef

Kong M, Pei J, Liu X et al (2020) Green manufacturing: Order acceptance and scheduling subject to the budgets of energy consumption and machine launch. J Clean Prod 248:119300CrossRef

Konur S, Lan Y, Thakker D et al (2021) Towards design and implementation of industry 4.0 for food manufacturing. Neural Comput Appl. https://doi.org/10.1007/s00521-021-05726-zCrossRef

Kumar A, Shankar R, Thakur LS (2018) A big data driven sustainable manufacturing framework for condition-based maintenance prediction. J Comput Sci 27:428–439CrossRef

Kusiak A (2018) Smart manufacturing. Int J Prod Res 56:508–517CrossRef

Lee J, Davari H, Singh J, Pandhare V (2018) Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manuf Lett 18:20–23CrossRef

Leng J, Ruan G, Song Y et al (2021) A loosely-coupled deep reinforcement learning approach for order acceptance decision of mass-individualized printed circuit board manufacturing in industry 4.0. J Clean Prod 280:124405CrossRef

Leo Kumar SP (2017) State of the art-intense review on Artificial Intelligence Systems application in process planning and Manufacturing. Eng Appl Artif Intell 65:294–329CrossRef

Leong WD, Teng SY, How BS et al (2019) Adaptive analytical approach to lean and green operations. J Clean Prod 235:190–209CrossRef

Leong WD, Teng SY, How BS et al (2020) Enhancing the adaptability: lean and green strategy towards the Industry Revolution 4.0. J Clean Prod 273:122870CrossRef

Liu Y, Dong H, Lohse N et al (2014) An investigation into minimising total energy consumption and total weighted tardiness in job shops. J Clean Prod 65:87–96CrossRef

Liu Y, Dong H, Lohse N, Petrovic S (2015) Reducing environmental impact of production during a rolling blackout policy - A multi-objective schedule optimisation approach. J Clean Prod 102:418–427CrossRef

Liu Y, Dong H, Lohse N, Petrovic S (2016) A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance. Int J Prod Econ 179:259–272CrossRef

Liu Q, Liu Z, Xu W et al (2019) Human-robot collaboration in disassembly for sustainable manufacturing. Int J Prod Res 57:4027–4044CrossRef

Lotter W, Gabriel K, David C (2020) A neural network trained for prediction mimics diverse features of biological neurons and perception. Nat Mach Intell 2:210–219CrossRef

Luo H, Du B, Huang GQ et al (2013) Hybrid flow shop scheduling considering machine electricity consumption cost. Int J Prod Econ 146:423–439CrossRef

Majumdar A, Agrawal R, Raut RD, Narkhede BE (2022) Two years of COVID-19 pandemic: understanding the role of knowledge-based supply chains towards resilience through bibliometric and network analyses. Oper Manage Res 1–17. https://doi.org/10.1007/s12063-022-00328-x

Malek J, Desai TN (2020) A systematic literature review to map literature focus of sustainable manufacturing. J Clean Prod 256:120345CrossRef

May G, Stahl B, Taisch M, Prabhu V (2015) Multi-objective genetic algorithm for energy-efficient job shop scheduling. Int J Prod Res 53:7071–7089CrossRef

Meng K, Qian X, Lou P, Zhang J (2020) Smart recovery decision-making of used industrial equipment for sustainable manufacturing: belt lifter case study. J Intell Manuf 31:183–197CrossRef

Mhlanga D (2023) Artificial Intelligence and Machine Learning for Energy Consumption and Production in Emerging Markets: a review. Energies (Basel) 16:745CrossRef

Mia M, Morshed MS, Kharshiduzzaman M et al (2018) Prediction and optimization of surface roughness in minimum quantity coolant lubrication applied turning of high hardness steel. Meas (Lond) 118:43–51CrossRef

Mogre R, Talluri SS, Damico F (2016) A decision framework to mitigate supply chain risks: an application in the offshore-wind industry. IEEE Trans Eng Manag 63:316–325CrossRef

Mokhtarinejad M, Ahmadi A, Karimi B, Rahmati SHA (2015) A novel learning based approach for a new integrated location-routing and scheduling problem within cross-docking considering direct shipment. Appl Soft Comput J 34:274–285CrossRef

Mongeon P, Paul-Hus A (2016) The journal coverage of web of Science and Scopus: a comparative analysis. Scientometrics 106:213–228CrossRef

Murgia A, Verbeke R, Tsiporkova E et al (2023) Discussion on the suitability of SCADA-Based Condition monitoring for wind turbine Fault diagnosis through Temperature Data Analysis. Energies (Basel) 16:620CrossRef

Nagaty KA (2023) IoT Commercial and Industrial Applications and AI-Powered IoT. Frontiers of Quality Electronic Design (QED). Springer International Publishing, Cham, pp 465–500CrossRef

Nara EOB, da Costa MB, Baierle IC et al (2021) Expected impact of industry 4.0 technologies on sustainable development: a study in the context of Brazil’s plastic industry. Sustain Prod Consum 25:102–122CrossRef

Nasir V, Kooshkbaghi M, Cool J (2020) Sensor fusion and random forest modeling for identifying frozen and green wood during lumber manufacturing. Manuf Lett 26:53–58CrossRef

Naz F, Kumar A, Majumdar A, Agrawal R (2021) Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research. Oper Manage Res 1–21. https://doi.org/10.1007/s12063-021-00208-w

Nujoom R, Mohammed A, Wang Q (2018) A sustainable manufacturing system design: a fuzzy multi-objective optimization model. Environ Sci Pollut Res 25:24535–24547CrossRef

Orji IJ, Wei S (2015) Dynamic modeling of sustainable operation in green manufacturing environment. J Manuf Technol Manage 26:1201–1217CrossRef

Panetto H, Iung B, Ivanov D et al (2019) Challenges for the cyber-physical manufacturing enterprises of the future. Annu Rev Control 47:200–213CrossRef

Pang R, Zhang X (2019) Achieving environmental sustainability in manufacture: a 28-year bibliometric cartography of green manufacturing research. J Clean Prod 233:84–99CrossRef

Park KT, Kang YT, Yang SG et al (2020) Cyber Physical Energy System for saving energy of the dyeing process with Industrial Internet of Things and Manufacturing Big Data. Int J Precision Eng Manuf - Green Technol 7:219–238CrossRef

Peng S, Li T, Zhao J et al (2019) Towards energy and material efficient laser cladding process: modeling and optimization using a hybrid TS-GEP algorithm and the NSGA-II. J Clean Prod 227:58–69CrossRef

Pirraglia A, Saloni DE (2012) Measuring environmental improvements image in companies implementing green manufacturing, by means of a fuzzy logic model for decision-making purposes. Int J Adv Manuf Technol 61:703–711CrossRef

Pirri S, Lorenzoni V, Turchetti G (2020) Scoping review and bibliometric analysis of Big Data applications for Medication adherence: An explorative methodological study to enhance consistency in literature. BMC Health Serv Res 20:688. https://doi.org/10.1186/s12913-020-05544-4CrossRef

Rajesh R (2020) A grey-layered ANP based decision support model for analyzing strategies of resilience in electronic supply chains. Eng Appl Artif Intell 87:103338CrossRef

Recchioni M, Mandorli F, Otto HE (2009) Supporting development of modular products utilising simplified LCA and fuzzy logic. Int J Sustainable Manuf 1:396–414CrossRef

Rickli JL, Camelio JA (2013) Multi-objective partial disassembly optimization based on sequence feasibility. J Manuf Syst 32:281CrossRef

Rubaiee S, Yildirim MB (2019) An energy-aware multiobjective ant colony algorithm to minimize total completion time and energy cost on a single-machine preemptive scheduling. Comput Ind Eng 127:240–252CrossRef

Senthiil PV, Aakash Sirusshti VS, Sathish T (2019) Artificial intelligence based green manufacturability quantification of a unit production process. Int J Mech Prod Eng Res Dev 9:841–852

Sharma R, Jabbour CJC, de Sousa Jabbour AB (2020) Sustainable manufacturing and industry 4.0: what we know and what we don’t. J Enterp Inform Manage 34:230–266CrossRef

Shin S-J, Kim DB, Shao G et al (2017) Developing a decision support system for improving sustainability performance of manufacturing processes. J Intell Manuf 28:1421–1440CrossRef

Singh M, Singh K, Sethi AS (2020) Justification of green manufacturing implementation parameters using fuzzy-based simulation model. J Sci Technol Policy Manage 11:377–394CrossRef

Song Z, Young M (2017) Assessing sustainability benefits of cyber manufacturing systems. Int J Adv Manuf Technol 90:1365–1382CrossRef

Tao F, Bi LN, Zuo Y, Nee AYC (2016) A hybrid group leader algorithm for green material selection with energy consideration in product design. CIRP Ann Manuf Technol 65:9–12CrossRef

Tayal A, Solanki A, Singh SP (2020) Integrated frame work for identifying sustainable manufacturing layouts based on big data, machine learning, meta-heuristic and data envelopment analysis. Sustain Cities Soc 62:102383CrossRef

Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag 14:207–222CrossRef

Traore BB, Kamsu-Foguem B, Tangara F (2018) Deep convolution neural network for image recognition. Ecol Inf 48:257–268CrossRef

Tuptuk N, Hailes S (2018) Security of smart manufacturing systems. J Manuf Syst 47:93–106CrossRef

Tura N, Hanski J, Ahola T et al (2019) Unlocking circular business: a framework of barriers and drivers. J Clean Prod 212:90–98CrossRef

Vahdani B, Iranmanesh SH, Mousavi SM, Abdollahzade M (2012) A locally linear neuro-fuzzy model for supplier selection in cosmetics industry. Appl Math Model 36:4714–4727CrossRef

Vijayaraghavan V, Castagne S (2016) Sustainable manufacturing models for mass finishing process. Int J Adv Manuf Technol 86:49–57CrossRef

Vikhorev K, Greenough R, Brown N (2013) An advanced energy management framework to promote energy awareness. J Clean Prod 43:103–112CrossRef

Vinodh S, Eazhil Selvan K, Hari Prakash N (2011) Evaluation of sustainability using fuzzy association rules mining. Clean Technol Environ Policy 13:809–819CrossRef

Vinodh S, Jayakrishna K, Kumar V, Dutta R (2014) Development of decision support system for sustainability evaluation: a case study. Clean Technol Environ Policy 16:163–174CrossRef

Vinodh S, Antony J, Agrawal R, Douglas JA (2021) Integration of continuous improvement strategies with industry 4.0: a systematic review and agenda for further research. TQM J 33:441–472CrossRef

Wang W, Tseng MM (2011) Design for sustainable manufacturing: applying modular design methodology to manage product end-of-life strategy. Int J Prod Lifecycle Manag 5:164–182CrossRef

Wang J, Wang P, Gao RX (2015a) Enhanced particle filter for tool wear prediction. J Manuf Syst 36:35–45CrossRef

Wang S, Lu X, Li XX, Li WD (2015b) A systematic approach of process planning and scheduling optimization for sustainable machining. J Clean Prod 87:914–929CrossRef

Wang T, Ma Z, Yang L (2023) Creativity and Sustainable Design of Wickerwork handicraft patterns based on Artificial Intelligence. Sustainability 15:1574CrossRef

Xia X, Govindan K, Zhu Q (2015) Analyzing internal barriers for automotive parts remanufacturers in China using grey-DEMATEL approach. J Clean Prod 87:811–825CrossRef

Xia X, Liu W, Zhang Z et al (2019) A balancing method of mixed-model disassembly line in random working environment. Sustain (Switzerland) 11(8):2304

Xin X, Jiang Q, Li S et al (2021) Energy-efficient scheduling for a permutation flow shop with variable transportation time using an improved discrete whale swarm optimization. J Clean Prod 293:126121CrossRef

Yadegaridehkordi E, Hourmand M, Nilashi M et al (2020) Assessment of sustainability indicators for green building manufacturing using fuzzy multi-criteria decision making approach. J Clean Prod 277:122905CrossRef

Yan J, Hua D, Wang X, Li X (2012) Sustainable manufacturing oriented prognosis for facility reuse combining ann and reliability. Qual Reliab Eng Int 28:265–277CrossRef

Yang SS, Nasr N, Ong SK, Nee AYC (2016a) A holistic decision support tool for remanufacturing: end-of-life (EOL) strategy planning. Adv Manuf 4:189–201CrossRef

Yang Y, Li X, Gao L, Shao X (2016b) Modeling and impact factors analyzing of energy consumption in CNC face milling using GRASP gene expression programming. Int J Adv Manuf Technol 87:1247–1263CrossRef

Yang D, Li J, Wang B, Jia Y-J (2020) Module-Based product configuration decisions considering both economical and carbon emission-related environmental factors. Sustain (Switzerland) 12(3):1145

Yildirim MB, Mouzon G (2012) Single-machine sustainable production planning to minimize total energy consumption and total completion time using a multiple objective genetic algorithm. IEEE Trans Eng Manag 59:585–597CrossRef

Yu J, Zhang C, Wang S (2021a) Sparse one-dimensional convolutional neural network-based feature learning for fault detection and diagnosis in multivariable manufacturing processes. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06575-6CrossRef

Yu Z, Razzaq A, Rehman A et al (2021b) Disruption in global supply chain and socio-economic shocks: a lesson from COVID-19 for sustainable production and consumption. Oper Manage Res. https://doi.org/10.1007/s12063-021-00179-yCrossRef

Yu P, Yang M, Xiong A et al (2022) Intelligent-Driven Green Resource Allocation for Industrial Internet of things in 5G heterogeneous networks. IEEE Trans Industr Inform 18:520–530CrossRef

Zarte M, Pechmann A, Nunes IL (2019) Decision support systems for sustainable manufacturing surrounding the product and production life cycle – A literature review. J Clean Prod 219:336–349CrossRef

Zhang Y (2010) Analysis on fuzzy evaluation of green design based on life cycle

Zhang R (2017) Sustainable scheduling of cloth production processes by multi-objective genetic algorithm with tabu-enhanced local search. Sustain (Switzerland) 9(10):1754

Zhang H, Deng Z, Fu Y et al (2017) A process parameters optimization method of multi-pass dry milling for high efficiency, low energy and low carbon emissions. J Clean Prod 148:174–184CrossRef

Zhang Z, Ming W, Zhang Y et al (2020) Analyzing sustainable performance on high-precision molding process of 3D ultra-thin glass for smart phone. J Clean Prod 255:120196CrossRef

Zhou C, Guo K, Sun J (2021) Sound singularity analysis for milling tool condition monitoring towards sustainable manufacturing. Mech Syst Signal Process 157:107738CrossRef

Title: Integration of artificial intelligence in sustainable manufacturing: current status and future opportunities
Authors: Rohit Agrawal
Abhijit Majumdar
Anil Kumar
Sunil Luthra
Publication date: 01-06-2023
Publisher: Springer US
Published in: Operations Management Research / Issue 4/2023
Print ISSN: 1936-9735
Electronic ISSN: 1936-9743
DOI: https://doi.org/10.1007/s12063-023-00383-y

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 4/2023

Modelling challenges of blockchain technology enabled healthcare sustainable supply chain management: a modified-total interpretive structural modelling approach

Strategic data capital investment in a supply chain

Big data analytics in mitigating challenges of sustainable manufacturing supply chain

Correction: A blockchain platform for the truck freight marketplace in India

Blockchain adoption in food supply chain for new business opportunities: an integrated approach

Supply chain responses to global disruptions and its ripple effects: an institutional complexity perspective

Premium Partner