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@article{194195,
        author = {ABHISHEK KUMAR SAXENA},
        title = {Algorithm-Based Pattern Nesting for Waste Reduction in Footwear Upper Cutting: A Systematic Literature Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {2868-2886},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194195},
        abstract = {The footwear manufacturing sector faces persistent material inefficiencies during the cutting of upper components, particularly when working with leather and synthetic sheets that represent a major share of production costs. Ineffective nesting of irregular patterns not only elevates manufacturing expenses but also contributes significantly to pre-consumer waste. This study presents a systematic literature review of fifty peer-reviewed publications published between 2003 and 2025, examining algorithm-driven pattern nesting techniques aimed at improving material utilization in footwear upper cutting. A quantitative meta-analytical approach is applied to assess algorithm performance across three primary dimensions: utilization efficiency, computational performance, and constraint management. 
The review identifies genetic algorithms, heuristic strategies, and hybrid metaheuristics as dominant solution frameworks, with hybrid approaches accounting for the largest proportion of studies. On average, algorithmic nesting methods demonstrate a 15.27% improvement in material utilization and achieve processing speeds approximately 11.42 times faster than manual nesting practices, although reported outcomes vary widely depending on problem complexity and material characteristics. Algorithms integrating geometric constructs such as no-fit polygons and quality-aware constraints exhibit superior handling of irregular shapes and leather-specific limitations, including directionality and surface heterogeneity. Despite demonstrated advantages, challenges related to computational scalability and the NP-hard nature of the nesting problem remain. Overall, the findings establish algorithm-based nesting as a critical enabler of sustainable footwear manufacturing and highlight the need for standardized benchmarking frameworks and deeper integration of artificial intelligence techniques to support future industrial adoption.},
        keywords = {Irregular nesting optimization, hybrid metaheuristics, material utilization efficiency, no-fit polygon (NFP), computational complexity, sustainable footwear production, meta-analysis.},
        month = {March},
        }