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

TL;DR

This study applies Mixed-Integer Linear Programming (MILP) combined with Lean principles to optimize garment production line balancing, achieving over 50% reduction in labor costs and demonstrating practical improvements in efficiency and resource management.

Contribution

It introduces a novel integration of MILP with Lean methodology for garment line balancing, validated through a real-world case study using IBM CPLEX Studio.

Findings

Labor costs reduced by over 50%

Effective resource capacity management

Validated practical applicability of MILP in garment industry

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.