{"title":"一个分析驱动的循环供应链框架,集成了质量、保证和人力效率","authors":"Lalji Kumar, Uttam Kumar Khedlekar","doi":"10.1016/j.sca.2025.100140","DOIUrl":null,"url":null,"abstract":"<div><div>This paper presents an advanced human-centric circular supply chain optimization framework that integrates economic, environmental, and behavioral dimensions into a unified multi-objective model. By jointly optimizing selling price, product quality, warranty duration, and production cycle time, the model captures the intricate trade-offs between profitability and sustainability-related penalties. A distinctive feature of the framework is the incorporation of a Human Efficiency Index and a circularity-based return function, enabling dynamic modeling of skill-driven waste minimization and quality-sensitive consumer behavior. The resulting nonlinear optimization problem is addressed using four powerful metaheuristic algorithms—Teaching-Learning-Based Optimization (TLBO), TLBO with Learning Rate, Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Particle Swarm Optimization (MOPSO). Extensive numerical simulations demonstrate the efficacy of the TLBO-based methods in achieving high-profit, low-penalty solutions, while statistical analyses confirm their robustness and superiority through the Friedman test and the Wilcoxon signed-rank test. From a managerial perspective, the model offers critical insights for aligning operational decisions with sustainability-oriented goals by demonstrating the nonlinear effects of human efficiency and product lifecycle attributes on supply chain performance. From a policy standpoint, the findings advocate for institutional mechanisms that incentivize investment in skill development, recycling, and circularity-driven design practices. Furthermore, the social relevance of this work lies in its contribution to Industry 5.0 paradigms, where inclusive, sustainable, and human-empowered production systems are prioritized. This research thus provides a robust, actionable framework for decision-makers seeking to design resilient and circular supply chains that promote long-term economic value and social welfare.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"11 ","pages":"Article 100140"},"PeriodicalIF":0.0000,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An analytics-driven circular supply chain framework integrating quality, warranty, and human efficiency\",\"authors\":\"Lalji Kumar, Uttam Kumar Khedlekar\",\"doi\":\"10.1016/j.sca.2025.100140\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper presents an advanced human-centric circular supply chain optimization framework that integrates economic, environmental, and behavioral dimensions into a unified multi-objective model. By jointly optimizing selling price, product quality, warranty duration, and production cycle time, the model captures the intricate trade-offs between profitability and sustainability-related penalties. A distinctive feature of the framework is the incorporation of a Human Efficiency Index and a circularity-based return function, enabling dynamic modeling of skill-driven waste minimization and quality-sensitive consumer behavior. The resulting nonlinear optimization problem is addressed using four powerful metaheuristic algorithms—Teaching-Learning-Based Optimization (TLBO), TLBO with Learning Rate, Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Particle Swarm Optimization (MOPSO). Extensive numerical simulations demonstrate the efficacy of the TLBO-based methods in achieving high-profit, low-penalty solutions, while statistical analyses confirm their robustness and superiority through the Friedman test and the Wilcoxon signed-rank test. From a managerial perspective, the model offers critical insights for aligning operational decisions with sustainability-oriented goals by demonstrating the nonlinear effects of human efficiency and product lifecycle attributes on supply chain performance. From a policy standpoint, the findings advocate for institutional mechanisms that incentivize investment in skill development, recycling, and circularity-driven design practices. Furthermore, the social relevance of this work lies in its contribution to Industry 5.0 paradigms, where inclusive, sustainable, and human-empowered production systems are prioritized. This research thus provides a robust, actionable framework for decision-makers seeking to design resilient and circular supply chains that promote long-term economic value and social welfare.</div></div>\",\"PeriodicalId\":101186,\"journal\":{\"name\":\"Supply Chain Analytics\",\"volume\":\"11 \",\"pages\":\"Article 100140\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Supply Chain Analytics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2949863525000408\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Supply Chain Analytics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949863525000408","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An analytics-driven circular supply chain framework integrating quality, warranty, and human efficiency
This paper presents an advanced human-centric circular supply chain optimization framework that integrates economic, environmental, and behavioral dimensions into a unified multi-objective model. By jointly optimizing selling price, product quality, warranty duration, and production cycle time, the model captures the intricate trade-offs between profitability and sustainability-related penalties. A distinctive feature of the framework is the incorporation of a Human Efficiency Index and a circularity-based return function, enabling dynamic modeling of skill-driven waste minimization and quality-sensitive consumer behavior. The resulting nonlinear optimization problem is addressed using four powerful metaheuristic algorithms—Teaching-Learning-Based Optimization (TLBO), TLBO with Learning Rate, Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Particle Swarm Optimization (MOPSO). Extensive numerical simulations demonstrate the efficacy of the TLBO-based methods in achieving high-profit, low-penalty solutions, while statistical analyses confirm their robustness and superiority through the Friedman test and the Wilcoxon signed-rank test. From a managerial perspective, the model offers critical insights for aligning operational decisions with sustainability-oriented goals by demonstrating the nonlinear effects of human efficiency and product lifecycle attributes on supply chain performance. From a policy standpoint, the findings advocate for institutional mechanisms that incentivize investment in skill development, recycling, and circularity-driven design practices. Furthermore, the social relevance of this work lies in its contribution to Industry 5.0 paradigms, where inclusive, sustainable, and human-empowered production systems are prioritized. This research thus provides a robust, actionable framework for decision-makers seeking to design resilient and circular supply chains that promote long-term economic value and social welfare.