bbce2e50926d8f44748de2a992f13a75 computation-13-00173.pdf 353f19476170131fe41380ea081e8b3645d3eaca computation-13-00173.pdf d81b77a025f051512ea5c4809b4e8ab5e87f19e9ae5fd939d0da796a58ba534c computation-13-00173.pdf Title: Strategic Decision-Making in SMEs: A Review of Heuristics and Machine Learning for Multi-Objective Optimization Subject: Small- and medium-sized enterprises (SMEs) face dynamic and competitive environments where resilience and data-driven decision-making are critical. Despite the potential benefits of artificial intelligence (AI), machine learning (ML), and optimization techniques, SMEs often struggle to adopt these tools due to high costs, limited training, and restricted hardware access. This study reviews how SMEs can employ heuristics, metaheuristics, ML, and hybrid approaches to support strategic decisions under uncertainty and resource constraints. Using bibliometric mapping with UMAP and BERTopic, 82 key works are identified and clustered into 11 thematic areas. From this, the study develops a practical framework for implementing and evaluating optimization strategies tailored to SMEs’ limitations. The results highlight critical application areas, adoption barriers, and success factors, showing that heuristics and hybrid methods are especially effective for multi-objective optimization with lower computational demands. The study also outlines research gaps and proposes future directions to foster digital transformation in SMEs. Unlike prior reviews focused on specific industries or methods, this work offers a cross-sectoral perspective, emphasizing how these technologies can strengthen SME resilience and strategic planning. Keywords: heuristics; machine learning; strategic decision-making; optimization; small and medium enterprises Author: Gines Molina-Abril, Laura Calvet, Angel A. Juan and Daniel Riera Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25; modified using OpenPDF 1.4.2 CreationDate: Fri Jul 18 09:36:56 2025 CEST ModDate: Fri Jul 18 09:41:30 2025 CEST Custom Metadata: yes Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 22 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 536406 bytes Optimized: no PDF version: 1.5 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- WLCVAH+URWPalladioL-Roma Type 1 Custom yes yes yes 167 0 ILTNHH+URWPalladioL-Ital Type 1 Custom yes yes yes 168 0 QLMWXE+URWPalladioL-Bold Type 1 Custom yes yes yes 169 0 AAAAAA+NunitoExtraLight-Medium CID TrueType Identity-H yes yes yes 255 0 AAAAAA+NunitoExtraLight-Medium CID TrueType Identity-H yes yes yes 256 0 AAAAAA+NunitoExtraLight-Medium CID TrueType Identity-H yes yes yes 257 0 BAAAAA+CascadiaCode-Regular CID TrueType Identity-H yes yes yes 258 0 FXXUVH+CMSY10 Type 1 Builtin yes yes yes 410 0 AAAAAB+Verdana TrueType MacRoman yes yes no 301 0 Verdana CID TrueType Identity-H yes no yes 364 0 Verdana CID TrueType Identity-H yes no yes 365 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-09-17 03:09:30 CEST RepresentationInformation: computation-13-00173.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-09-16 13:40:50 CEST Size: 536406 Format: PDF Version: 1.5 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 950 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: ViewerPreferences: HideToolbar: false HideMenubar: false HideWindowUI: false FitWindow: true CenterWindow: false DisplayDocTitle: false NonFullScreenPageMode: UseNone Direction: L2R ViewArea: CropBox ViewClip: CropBox PrintArea: CropBox PageClip: CropBox PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Item: Title: Integrating Heuristics and Machine Learning Children: Item: Title: Heuristics and Metaheuristics Item: Title: Machine Learning Item: Title: Hybrid Approaches Item: Title: Research Methodology: Exploring Heuristics and Machine Learning for Optimization Item: Title: Exploring Cluster Grouping: Patterns and Insights Children: Item: Title: Segment A: Multi-Domain Feature Engineering and Uncertainty Quantification Item: Title: Segment B: Data-Driven Modeling and Uncertainty Management—Cross-Domain Applications Item: Title: Segment C: Energy Demand and Wind Power Forecasting Item: Title: Segment D: Energy Efficiency and Thermal Performance in Building Systems Item: Title: Segment E: Advanced Materials and Manufacturing Item: Title: Segment F: Control Strategies for Energy Systems and Autonomous Vehicles Item: Title: Segment G: Management of Renewable Energy Systems Item: Title: Segment H: Supply Chain and Logistics Management Item: Title: Segment I: Algorithms for Complex Operational Problems Item: Title: Segment J: Cloud and Edge Computing for Internet of Things (IoT) and Mobile Systems Item: Title: Segment K: Unmanned Aerial Vehicle (UAV) and Wireless Sensor Networks Item: Title: Optimization in SMEs: Optimization Strategy Implementation and Impact Assessment Children: Item: Title: Key Optimization Strategies Item: Title: Potential Business Impact Item: Title: Implementation Feasibility Item: Title: Recommendations and Managerial Insights Item: Title: Conclusions Item: Title: Appendix A Item: Title: References Info: Title: Strategic Decision-Making in SMEs: A Review of Heuristics and Machine Learning for Multi-Objective Optimization Author: Gines Molina-Abril, Laura Calvet, Angel A. Juan and Daniel Riera Subject: Small- and medium-sized enterprises (SMEs) face dynamic and competitive environments where resilience and data-driven decision-making are critical. Despite the potential benefits of artificial intelligence (AI), machine learning (ML), and optimization techniques, SMEs often struggle to adopt these tools due to high costs, limited training, and restricted hardware access. This study reviews how SMEs can employ heuristics, metaheuristics, ML, and hybrid approaches to support strategic decisions under uncertainty and resource constraints. Using bibliometric mapping with UMAP and BERTopic, 82 key works are identified and clustered into 11 thematic areas. From this, the study develops a practical framework for implementing and evaluating optimization strategies tailored to SMEs’ limitations. The results highlight critical application areas, adoption barriers, and success factors, showing that heuristics and hybrid methods are especially effective for multi-objective optimization with lower computational demands. The study also outlines research gaps and proposes future directions to foster digital transformation in SMEs. Unlike prior reviews focused on specific industries or methods, this work offers a cross-sectoral perspective, emphasizing how these technologies can strengthen SME resilience and strategic planning. 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