{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T14:38:52Z","timestamp":1780670332966,"version":"3.54.1"},"reference-count":73,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004410","name":"T\u00dcB\u0130TAK","doi-asserted-by":"publisher","award":["2209"],"award-info":[{"award-number":["2209"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Small and medium-sized enterprises (SMEs) constitute a substantial share of industrial production. However, their operational performance is frequently constrained by delivery delays caused by inefficiencies in workforce scheduling and task sequencing. These limitations reduce overall competitiveness, particularly in project-based manufacturing environments where task heterogeneity and multi-skill variability are prominent. To address this challenge, this study develops an artificial intelligence based workforce planning framework tailored to capital-constrained manufacturing settings. The new proposed hybrid system integrates a Genetic Algorithm (GA), Monte Carlo Simulation (MCS), and Taguchi methodology to generate robust, uncertainty-aware labor assignments. The framework is validated through 18-month deployments in two manufacturing facilities with differing levels of technological maturity, demonstrating consistent improvements in operational outcomes. Furthermore, specific weekly examples were validated against the solutions of exact mixed integer linear programming solvers on the deterministic core to assess the optimality gap and ensure constant solution quality. Across the deployments, the system achieved 13% and 15% reduction in task completion times. The resulting GA\u2013MCS\u2013Taguchi pipeline operates efficiently on standard SMEs hardware, requires only short historical performance windows for calibration, and exhibits high user adoption in real industrial settings, which indicates strong operational viability and practical deployability.<\/jats:p>","DOI":"10.3390\/systems14010026","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T00:50:21Z","timestamp":1766710221000},"page":"26","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Intelligent Workforce Scheduling in Manufacturing: An Integrated Optimization Framework Using Genetic Algorithm, Monte Carlo Simulation, and Taguchi Method"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0212-0087","authenticated-orcid":false,"given":"Berrin","family":"Denizhan","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Sakarya University, Sakarya 54050, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6902-3911","authenticated-orcid":false,"given":"Elif","family":"Y\u0131ld\u0131r\u0131m","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Sakarya University, Sakarya 54050, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8367-1020","authenticated-orcid":false,"given":"Beyza","family":"F\u0131nd\u0131kl\u0131","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Sakarya University, Sakarya 54050, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7716-0052","authenticated-orcid":false,"given":"Mehmet Efe","family":"Erba\u015f","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Sakarya University, Sakarya 54050, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6030-2021","authenticated-orcid":false,"given":"Batuhan","family":"\u00d6z","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Sakarya University, Sakarya 54050, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1110-2184","authenticated-orcid":false,"given":"Bengisu","family":"Derya","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Sakarya University, Sakarya 54050, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13964","DOI":"10.1016\/j.ifacol.2017.08.2221","article-title":"A MILP model for an integrated project scheduling and multi-skilled workforce allocation with flexible working hours","volume":"50","author":"Karam","year":"2017","journal-title":"IFAC-PapersOnLine"},{"key":"ref_2","unstructured":"Bobek, A., Imondi, C., Shott, T., and Toobaei, M. (August, January 29). Heterogeneous project scheduling for optimal six-sigma cost reduction using linear programing. Proceedings of the 2012 Proceedings of PICMET\u201912: Technology Management for Emerging Technologies, Vancouver, BC, Canada. Available online: https:\/\/ieeexplore.ieee.org\/document\/6304257."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Angelidis, E., Bohn, D., and Rose, O. (2013, January 8\u201311). A simulation tool for complex assembly lines with multiskilled resources. Proceedings of the of the 2013 Winter Simulation Conference, Washington, DC, USA.","DOI":"10.1109\/WSC.2013.6721630"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"108240","DOI":"10.1016\/j.cie.2022.108240","article-title":"Multi-project scheduling with multi-skilled workforce assignment considering uncertainty and learning effect for large-scale equipment manufacturer","volume":"169","author":"Chen","year":"2022","journal-title":"Comput. Ind. Eng."},{"key":"ref_5","first-page":"737","article-title":"Digital transformation in SMEs: Barriers, drivers, and roadmap","volume":"181","author":"Gerekli","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gezgin, A.Y., and Ar\u0131c\u0131o\u011flu, M.A. (2025). Industry 4.0 and management 4.0: Examining the impact of environmental, cultural, and technological changes. Sustainability, 17.","DOI":"10.3390\/su17083601"},{"key":"ref_7","unstructured":"Garey, M.R., and Johnson, D.S. (1979). Computers and Intractability: A Guide to the Theory of NP-Completeness, W.H. Freeman."},{"key":"ref_8","unstructured":"Pinedo, M.L. (2016). Scheduling: Theory, Algorithms, and Systems, Springer International Publishing. [5th ed.]."},{"key":"ref_9","unstructured":"MESA International (2024, September 30). MESA Model: A Framework for Smarter Manufacturing. Available online: https:\/\/mesa.org\/topics-resources\/mesa-model\/."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.procs.2021.12.016","article-title":"Improvement of planning and time control in the project management of a metalworking industry\u2014Case study","volume":"196","author":"Silva","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_11","unstructured":"World Economic Forum (2025). Future of Jobs Report 2025, World Economic Forum. Available online: https:\/\/reports.weforum.org\/docs\/WEF_Future_of_Jobs_Report_2025.pdf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"110856","DOI":"10.1016\/j.cie.2025.110856","article-title":"Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions","volume":"201","author":"Khadivi","year":"2025","journal-title":"Comput. Ind. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107139","DOI":"10.1016\/j.cor.2025.107139","article-title":"Flexible job shop scheduling problem using graph neural networks and reinforcement learning","volume":"182","author":"Liu","year":"2025","journal-title":"Comput. Oper. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1109\/TII.2023.3272661","article-title":"Large-scale dynamic scheduling for flexible job-shop with random arrivals of new jobs by hierarchical reinforcement learning","volume":"20","author":"Lei","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111456","DOI":"10.1016\/j.cie.2025.111456","article-title":"How to achieve sustainable emergency management? A case study for Istanbul city with a stochastic approach","volume":"209","author":"Bulak","year":"2025","journal-title":"Comput. Ind. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Denizhan, B., Y\u0131ld\u0131r\u0131m, E., and Akkan, \u00d6. (2025). An order-picking problem in a medical facility using genetic algorithm. Processes, 13.","DOI":"10.3390\/pr13010022"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1002\/nav.3800010110","article-title":"Optimal two- and three-stage production schedules with setup times included","volume":"1","author":"Johnson","year":"1954","journal-title":"Nav. Res. Logist. Q."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/00207549308956713","article-title":"Addressing the gap in scheduling research: A review of optimization and heuristic methods in production scheduling","volume":"31","author":"MacCarthy","year":"1993","journal-title":"Int. J. Prod. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.compchemeng.2013.12.001","article-title":"Scope for industrial applications of production scheduling models and solution methods","volume":"62","author":"Harjunkoski","year":"2014","journal-title":"Comput. Chem. Eng."},{"key":"ref_20","unstructured":"Board of Governors of the Federal Reserve System (2025, September 17). Industrial Production and Capacity Utilization: G.17 Statistical Release, Available online: https:\/\/www.federalreserve.gov\/releases\/g17\/current\/."},{"key":"ref_21","unstructured":"(2025, September 30). FourKites. Ocean Shipping Report: 2024 Trends and Challenges. Available online: https:\/\/www.fourkites.com\/resources\/ocean-shipping-report\/."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s10479-023-05784-7","article-title":"Solving a real-life multi-skill resource-constrained multi-project scheduling problem","volume":"338","author":"Torba","year":"2024","journal-title":"Ann. Oper. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"93","DOI":"10.17562\/PB-52-10","article-title":"Project Scheduling: A Memetic Algorithm with Diversity-Adaptive Components that Optimizes the Effectiveness of Human Resources","volume":"52","author":"Yannibelli","year":"2015","journal-title":"Polibits"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7238","DOI":"10.1080\/00207543.2025.2496971","article-title":"An efficient problem-specific evolutionary algorithm for flexible job shop scheduling problem with specific workers in highly customised manufacturing systems","volume":"63","author":"Li","year":"2025","journal-title":"Int. J. Prod. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1016\/j.ejor.2007.11.005","article-title":"Scheduling projects with heterogeneous resources to meet time and quality objectives","volume":"193","author":"Tiwari","year":"2009","journal-title":"Eur. J. Oper. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1080\/00207543.2024.2371097","article-title":"Strategic workforce and project planning for engineering automotive production systems: Tackling the transition to electric vehicles","volume":"63","author":"Kolter","year":"2025","journal-title":"Int. J. Prod. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/evco.1999.7.1.1","article-title":"Production scheduling with Genetic Algorithm","volume":"7","author":"Bierwirth","year":"1999","journal-title":"Evol. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.ejor.2023.11.009","article-title":"A GA with resource buffers for the resource-constrained multi-project scheduling problem","volume":"315","author":"Bredael","year":"2024","journal-title":"Eur. J. Oper. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7763","DOI":"10.1080\/00207543.2024.2328131","article-title":"A multi-skilled staff scheduling and team configuration optimisation model for artificial intelligence project portfolio considering competence development and innovation-driven","volume":"62","author":"Chen","year":"2024","journal-title":"Int. J. Prod. Res."},{"key":"ref_30","unstructured":"Ono, I., Yamamura, M., and Kobayashi, S. (1996, January 20\u201322). A genetic algorithm for job-shop scheduling problems using job-based order crossover. Proceedings of the IEEE International Conference on Evolutionary Computation, Nagoya, Japan."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1007\/s10732-018-9385-x","article-title":"Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem","volume":"25","author":"Algethami","year":"2019","journal-title":"J. Heuristics"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/S0377-2217(99)00391-4","article-title":"A genetic algorithm for scheduling staff of mixed skills under multi-criteria","volume":"125","author":"Cai","year":"2000","journal-title":"Eur. J. Oper. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1528","DOI":"10.3906\/elk-1212-26","article-title":"Optimization of job shop scheduling problems using modified clonal selection algorithm","volume":"22","author":"Atay","year":"2014","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Y\u0131ld\u0131r\u0131m, E., and Denizhan, B. (2022). A two-echelon pharmaceutical supply chain optimization via genetic algorithm. Lecture Notes in Mechanical Engineering, Springer.","DOI":"10.1007\/978-981-16-7164-7_7"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"\u00c7ubuk\u00e7uo\u011flu, A., Karacan, I., Ceylan, Z., and Bulkan, S. (2025). Minimizing Makespan in Ordered Flow Shop Scheduling Using a Robust Genetic Algorithm. Processes, 13.","DOI":"10.3390\/pr13051583"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"102171","DOI":"10.1016\/j.omega.2019.102171","article-title":"Models and methods for workforce planning under uncertainty: Optimizing U.S. Army cyber branch readiness and manning","volume":"92","author":"Bastian","year":"2020","journal-title":"Omega"},{"key":"ref_37","first-page":"4711","article-title":"A Monte Carlo simulation to estimate fatigue allowance for female order pickers in high traffic manual picking systems","volume":"59","author":"Kremer","year":"2020","journal-title":"Int. J. Prod. Res."},{"key":"ref_38","first-page":"184","article-title":"Estimating confidence lower bounds of Weibull lower percentiles with small samples in material reliability analysis","volume":"26","year":"2020","journal-title":"Pamukkale Univ. Muh. Bilim Derg."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/S0951-8320(00)00007-7","article-title":"Optimizing maintenance and repair policies via genetic algorithms and Monte Carlo simulation","volume":"68","author":"Marseguerra","year":"2000","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/S0951-8320(02)00043-1","article-title":"Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation","volume":"77","author":"Marseguerra","year":"2002","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1111\/1475-3995.00429","article-title":"A genetic algorithm and the Monte Carlo method for stochastic job-shop scheduling","volume":"10","author":"Yoshitomi","year":"2003","journal-title":"Int. Trans. Oper. Res."},{"key":"ref_42","unstructured":"Magalh\u00e3es-Mendes, J. (2011, January 20\u201322). A genetic algorithm for the job shop scheduling with a new local search using the Monte Carlo method. Proceedings of the 10th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED), Cambridge, UK. Available online: https:\/\/www.wseas.us\/e-library\/conferences\/2011\/Cambridge\/AIKED\/AIKED-02.pdf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1080\/00207543.2014.939244","article-title":"Genetic algorithm parameter optimisation using Taguchi method for a flexible manufacturing system scheduling problem","volume":"53","author":"Candan","year":"2015","journal-title":"Int. J. Prod. Res."},{"key":"ref_44","first-page":"607","article-title":"Managing production process in a pet resin industry using data mining and genetic programming","volume":"29","author":"Denizhan","year":"2022","journal-title":"Int. J. Ind. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Mondal, S., and Singh, R. (2025). Optimizing cybersecurity budgets in financial networks: A comparative study of genetic algorithms and trust-region methods. Preprint.","DOI":"10.21203\/rs.3.rs-7323238\/v1"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1007\/s12667-017-0265-5","article-title":"Robust optimization vs. stochastic programming incorporating risk measures for unit commitment with uncertain variable renewable generation","volume":"10","author":"Kazemzadeh","year":"2017","journal-title":"Energy Syst."},{"key":"ref_47","unstructured":"Taguchi, G. (1986). Introduction to Quality Engineering: Designing Quality into Products and Processes, Asian Productivity Organization."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/S0924-0136(98)00079-X","article-title":"Design optimization of cutting parameters for turning operations based on the Taguchi method","volume":"84","author":"Yang","year":"1998","journal-title":"J. Mater. Process. Technol."},{"key":"ref_49","first-page":"169","article-title":"A clustering-based simulated annealing algorithm with Taguchi method for the discrete ordered median problem","volume":"26","author":"Toksoy","year":"2022","journal-title":"Sak. Univ. J. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"64","DOI":"10.22271\/maths.2023.v8.i6a.1406","article-title":"Optimize the Taguchi method, the signal-to-noise ratio, and the sensitivity","volume":"8","author":"Rashid","year":"2023","journal-title":"Int. J. Stat. Appl. Math."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.jer.2023.10.029","article-title":"Mathematical modeling and optimization of machining parameters in CNC turning process of Inconel 718 using the Taguchi method","volume":"13","author":"Zhujani","year":"2025","journal-title":"J. Eng. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/s00170-011-3291-9","article-title":"A new algorithm and multi-response Taguchi method to solve line balancing problem in an automotive industry","volume":"57","author":"Yazgan","year":"2011","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1007\/s00170-007-1142-5","article-title":"An improved genetic algorithm for job-shop scheduling problems using Taguchi-based crossover","volume":"38","author":"Tsai","year":"2008","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.2339\/politeknik.1018428","article-title":"Rough-AHP and MOORA-based Taguchi optimization for mixture proportion of building concrete","volume":"26","author":"Himmetoglu","year":"2023","journal-title":"Politek. Derg."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Song, L., Xu, Z., Wang, C., and Su, J. (2023). A new decision method of flexible job shop rescheduling based on WOA-SVM. Systems, 11.","DOI":"10.3390\/systems11020059"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Poto\u010dnik, P., Jeromen, A., and Govekar, E. (2024). Genetic Algorithm-Based Framework for Optimization of Laser Beam Path in Additive Manufacturing. Metals, 14.","DOI":"10.3390\/met14040410"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Yang, J., Zheng, Y., and Wu, J. (2024). Towards Sustainable Production: An Adaptive Intelligent Optimization Genetic Algorithm for Solid Wood Panel Manufacturing. Sustainability, 16.","DOI":"10.3390\/su16093785"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yang, J., Zheng, Y., Wu, J., Wang, Y., He, J., and Tang, L. (2024). Enhancing Manufacturing Excellence with Digital-Twin-Enabled Operational Monitoring and Intelligent Scheduling. Appl. Sci., 14.","DOI":"10.3390\/app14156622"},{"key":"ref_59","unstructured":"G\u00fcnay, E.E., Ramadani, R., Mundiwala, M., Kashef, A., Ma, J., Hu, C., Kremer, P., and Kremer, G.E. (2025, January 17\u201320). Design Improvement for Facilitating Transmission Control Unit Remanufacturing. Proceedings of the ASMEs 2025 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Boston, MA, USA."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Y\u0131ld\u0131r\u0131m, E., and Denizhan, B. (2025). Comparative Study of Application of Production Sequencing and Scheduling Problems in Tire Mixing Operations with ADAM, Grey Wolf Optimizer, and Genetic Algorithm. Systems, 13.","DOI":"10.3390\/systems13110998"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wu, C., Xiao, Y., and Zhu, X. (2023). Research on Optimization Algorithm of AGV Scheduling for Intelligent Manufacturing Company: Taking the Machining Shop as an Example. Processes, 11.","DOI":"10.3390\/pr11092606"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Elbasheer, M., Longo, F., Mirabelli, G., and Solina, V. (2024). Flexible Symbiosis for Simulation Optimization in Production Scheduling: A Design Strategy for Adaptive Decision Support in Industry 5.0. J. Manuf. Mater. Process., 8.","DOI":"10.3390\/jmmp8060275"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Gen, M., and Cheng, R. (2000). Genetic Algorithms and Engineering Optimization, Wiley.","DOI":"10.1002\/9780470172261"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Eiben, A.E., and Smith, J.E. (2015). Introduction to Evolutionary Computing, Springer. [2nd ed.].","DOI":"10.1007\/978-3-662-44874-8"},{"key":"ref_65","unstructured":"Montgomery, D.C. (2019). Design and Analysis of Experiments, John Wiley & Sons. [10th ed.]."},{"key":"ref_66","first-page":"1602","article-title":"Developing end-to-end intelligent finance solutions through AI and cloud integration","volume":"10","author":"Malempati","year":"2021","journal-title":"Int. J. Sci. Res."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Miller, R.E., and Thatcher, J.W. (1972). Reducibility among combinatorial problems. Complexity of Computer Computations, Springer.","DOI":"10.1007\/978-1-4684-2001-2"},{"key":"ref_68","unstructured":"Papadimitriou, C.H., and Steiglitz, K. (1982). Combinatorial Optimization: Algorithms and Complexity, Prentice Hall. Available online: https:\/\/api.semanticscholar.org\/CorpusID:265900001."},{"key":"ref_69","first-page":"106","article-title":"A systematic literature network analysis approach to assess the topology of modern-era supply chain risk management research","volume":"50","author":"Dass","year":"2025","journal-title":"Int. J. Ind. Syst. Eng."},{"key":"ref_70","unstructured":"OECD (2021). The Digital Transformation of SMEs, OECD Publishing. OECD Studies on SMEs and Entrepreneurship."},{"key":"ref_71","unstructured":"Schulze Brock, P., Lag\u00fcera Gonz\u00e1lez, J., Di Bella, L., and Katsinis, A. (2025). SMEs Performance Review 2025, Publications Office of the European Union."},{"key":"ref_72","unstructured":"Eurostat (2025). Digitalisation in Europe\u20132025 Edition, Publications Office of the European Union. Available online: https:\/\/ec.europa.eu\/eurostat\/web\/interactive-publications\/digitalisation-2025."},{"key":"ref_73","unstructured":"OECD (2025). SMEs Digitalisation for Competitiveness: 2025 OECD D4SMEs Survey\u2014Policy Highlights, Organisation for Economic Co-operation and Development (OECD). Available online: https:\/\/www.oecd.org\/content\/dam\/oecd\/en\/networks\/oecd-digital-for-smes-global-initiative\/D4SME-2025-Policy-Highlights.pdf."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/1\/26\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T05:10:41Z","timestamp":1766812241000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/1\/26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,25]]},"references-count":73,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["systems14010026"],"URL":"https:\/\/doi.org\/10.3390\/systems14010026","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,25]]}}}