Strategic Decision-Making in SMEs: A Review of Heuristics and Machine Learning for Multi-Objective Optimization
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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/13/7/173 |
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| author | Gines Molina-Abril Laura Calvet Angel A. Juan Daniel Riera |
| author_facet | Gines Molina-Abril Laura Calvet Angel A. Juan Daniel Riera |
| author_sort | Gines Molina-Abril |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-16bf5bc2aa0846fbbcb12dfb5bf18f97 |
| institution | Kabale University |
| issn | 2079-3197 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj-art-16bf5bc2aa0846fbbcb12dfb5bf18f972025-08-20T03:36:19ZengMDPI AGComputation2079-31972025-07-0113717310.3390/computation13070173Strategic Decision-Making in SMEs: A Review of Heuristics and Machine Learning for Multi-Objective OptimizationGines Molina-Abril0Laura Calvet1Angel A. Juan2Daniel Riera3Department of Computer Science, Multimedia, and Telecommunication, Open University of Catalonia, 08018 Barcelona, SpainTelecommunications and Systems Engineering Department, Universitat Autònoma de Barcelona, 08202 Sabadell, SpainResearch Centre on Production Management and Engineering, Universitat Politècnica de València, 03801 Alcoy, SpainDepartment of Computer Science, Multimedia, and Telecommunication, Open University of Catalonia, 08018 Barcelona, SpainSmall- 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.https://www.mdpi.com/2079-3197/13/7/173heuristicsmachine learningstrategic decision-makingoptimizationsmall and medium enterprises |
| spellingShingle | Gines Molina-Abril Laura Calvet Angel A. Juan Daniel Riera Strategic Decision-Making in SMEs: A Review of Heuristics and Machine Learning for Multi-Objective Optimization Computation heuristics machine learning strategic decision-making optimization small and medium enterprises |
| title | Strategic Decision-Making in SMEs: A Review of Heuristics and Machine Learning for Multi-Objective Optimization |
| title_full | Strategic Decision-Making in SMEs: A Review of Heuristics and Machine Learning for Multi-Objective Optimization |
| title_fullStr | Strategic Decision-Making in SMEs: A Review of Heuristics and Machine Learning for Multi-Objective Optimization |
| title_full_unstemmed | Strategic Decision-Making in SMEs: A Review of Heuristics and Machine Learning for Multi-Objective Optimization |
| title_short | Strategic Decision-Making in SMEs: A Review of Heuristics and Machine Learning for Multi-Objective Optimization |
| title_sort | strategic decision making in smes a review of heuristics and machine learning for multi objective optimization |
| topic | heuristics machine learning strategic decision-making optimization small and medium enterprises |
| url | https://www.mdpi.com/2079-3197/13/7/173 |
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