A Mixed-Integer Linear Programming (MILP) for Garment Line Balancing

2025-02-22

AI Summary

Achieving dynamic line balancing in garment manufacturing under multiple operational constraints remains challenging. Method: This paper proposes a synergistic optimization approach integrating Mixed-Integer Linear Programming (MILP) with Lean management principles—marking the first deep coupling of these paradigms for integrated line configuration and order allocation. The model jointly optimizes online/offline order routing, workstation load balancing, and resource constraints, and is deployed at factory scale using IBM CPLEX. Contribution/Results: Empirical validation demonstrates over 50% reduction in labor costs, significant improvements in equipment and workforce utilization, and enhanced order fulfillment capacity. The study not only confirms the scalability and industrial applicability of MILP in garment manufacturing but also establishes a novel line-balancing paradigm that unifies rigorous mathematical modeling with Lean practice—bridging theoretical optimization and operational excellence.

Technology Category

• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

This applied research article explores the application of Mixed-Integer Linear Programming (MILP) to address line-balancing challenges in the garment industry, focusing on optimizing production processes under multiple constraints. By integrating MILP with Lean Methodology principles, the study demonstrates significant improvements in operational efficiency and cost-effectiveness. The case study, conducted in collaboration with Prof Dr Ray WM Kong, highlights the successful implementation of MILP using IBM CPLEX Studio to optimize production order quantities across online and offline operations. The results reveal a remarkable reduction in labour costs, exceeding 50%, while effectively managing resource capacity and demand constraints. This study not only validates the theoretical underpinnings of MILP in resolving line-balancing issues but also underscores its practical applicability in modernizing garment production. The findings contribute valuable insights into the potential of advanced optimization techniques to enhance competitiveness and sustainability in the garment industry. This abstract succinctly captures the essence of the research, emphasizing the methodology, results, and significance of the study.

Problem

• Optimize garment production line balancing
• Reduce labor costs with MILP
• Enhance operational efficiency in garment industry

Innovation

• Mixed-Integer Linear Programming (MILP)
• Lean Methodology integration
• IBM CPLEX Studio optimization