Optimizing ergonomic interventions in production tasks for sustainability in Bangladeshi RMG industries

• Proposes a novel hybrid framework integrating AHP and multi-objective optimization to prioritize ergonomic interventions in RMG production. • Embeds expert-derived AHP composite scores directly into the into the optimization model’s allowing ergonomic priorities to directly influence decision-making. • Optimizes task selection under real-world constraints including limited budgets, productivity targets, and ergonomic risk thresholds. • Demonstrates practical applicability using data from 12 representative RMG tasks, contributing to sustainable production and SSCM goals. • Contributes to Sustainable Production by operationalizing human-centered strategies that support SDGs. The Ready-Made Garments (RMG) industry in Bangladesh, a cornerstone of the national economy, faces critical challenges in aligning worker safety with productivity under financial and operational constraints. To address these challenges within the framework of Sustainable Supply Chain Management (SSCM), this study develops a hybrid decision-making framework that combines the AHP with Multi-objective Optimization. AHP was first employed to derive composite priority scores for twelve representative RMG tasks, using expert input across three key criteria: Ergonomic Risk Factors, workforce productivity, and cost of ergonomic intervention. These composite scores were subsequently incorporated into the optimization model as penalty terms to guide task prioritization. Multi-objective linear programming was then applied to identify the most effective intervention set under real-world constraints. The optimization model simultaneously maximizes productivity and minimizes ergonomic risk while accounting for task-level priority differences. The results show that the optimal intervention set favors tasks with relatively low ergonomic risk and high productivity contributions. Under baseline conditions, the selected task set achieves a total productivity of 524 units per hour with a performance score of 417.98. Sensitivity analysis and Pareto-based trade-off further demonstrate the model’s capability to balance competing objectives and support transparent decision-making. The proposed framework provides a systematic and data-driven approach for integrating ergonomic considerations into sustainable production planning in resource-constrained RMG environments and offers practical insights for improving both operational efficiency and worker well-being.