Skip to main content

Advertisement

Log in

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

  • Published:
Operations Management Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

The data that support the findings of this study are openly available in the SCOPUS database and are available from the corresponding author on request.

References

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

    Google Scholar 

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

    Article  Google Scholar 

  • 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):748–774

    Article  Google Scholar 

  • 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/s12063-021-00226-8

    Article  Google Scholar 

  • 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/s10479-020-03877-1

    Article  Google Scholar 

  • 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 314:127904

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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/S1363919616400156

    Article  Google Scholar 

  • 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):864–898

    Article  Google Scholar 

  • 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 163:120420

    Article  Google Scholar 

  • 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 15:743–759

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 13:244–270

    Article  Google Scholar 

  • 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):041013

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Google Scholar 

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

    Article  Google Scholar 

  • 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 166:1407–1414

    Article  Google Scholar 

  • 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 14:235–255

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 16:345–362

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 33:10–21

    Article  Google Scholar 

  • 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 147:106653

    Article  Google Scholar 

  • 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 26:255–268

    Article  Google Scholar 

  • 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 38:874–894

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 279:123075

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 11:217–222

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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 23:11015–11034

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 55:226–237

    Article  Google Scholar 

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

    Google Scholar 

  • 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

    Google Scholar 

  • 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):710

    Article  Google Scholar 

  • 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 248:119300

    Article  Google Scholar 

  • 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-z

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 280:124405

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 273:122870

    Article  Google Scholar 

  • 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 65:87–96

    Article  Google Scholar 

  • 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 102:418–427

    Article  Google Scholar 

  • 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 179:259–272

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 256:120345

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 31:183–197

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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) 118:43–51

    Article  Google Scholar 

  • 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 63:316–325

    Article  Google Scholar 

  • 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 34:274–285

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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) 16:620

    Article  Google Scholar 

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

    Chapter  Google Scholar 

  • 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 25:102–122

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 25:24535–24547

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 7:219–238

    Article  Google Scholar 

  • 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 227:58–69

    Article  Google Scholar 

  • 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 61:703–711

    Article  Google Scholar 

  • 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/s12913-020-05544-4

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 127:240–252

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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 34:230–266

    Article  Google Scholar 

  • 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 28:1421–1440

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 65:9–12

    Article  Google Scholar 

  • 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 62:102383

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 33:441–472

    Article  Google Scholar 

  • 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 5:164–182

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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 293:126121

    Article  Google Scholar 

  • 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 277:122905

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 87:1247–1263

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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 59:585–597

    Article  Google Scholar 

  • 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/s00521-021-06575-6

    Article  Google Scholar 

  • 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-y

    Article  Google Scholar 

  • 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 18:520–530

    Article  Google Scholar 

  • 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 219:336–349

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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 148:174–184

    Article  Google Scholar 

  • 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 255:120196

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Funding

This research received no external funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anil Kumar.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Table 4 Lead articles from the clusters

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agrawal, R., Majumdar, A., Kumar, A. et al. Integration of artificial intelligence in sustainable manufacturing: current status and future opportunities. Oper Manag Res 16, 1720–1741 (2023). https://doi.org/10.1007/s12063-023-00383-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12063-023-00383-y

Keywords

Navigation