A Feasibility-Oriented Heuristic for Garment Assembly Line Balancing with Machine Compatibility Constraints

Garment assembly line balancing requires workstation assignments that satisfy precedence, cycle-time, and machine-compatibility constraints. However, conventional priority-based heuristics may generate numerically attractive but technologically infeasible configurations. This study proposes a Feasibility-Oriented Heuristic (FOH) combining deterministic topological task ordering with incremental verification of workstation capacity and admissible machine types. The procedure preserves the modeled feasibility requirements throughout workstation construction under explicitly defined assumptions. Its performance is evaluated on a 17-operation garment assembly benchmark with a total work content of 20 min and a nominal cycle time of two minutes. Comparisons are conducted against conventional Ranked Positional Weight, Shortest Processing Time, and Largest Candidate Rule heuristics, their machine-compatibility-aware variants, and an exact Mixed-Integer Linear Programming formulation. Under nominal conditions, FOH generates 15 feasible workstations with a line efficiency of 66.67 percent, matching the proven MILP optimum. Conventional RPW and LCR produce machine-incompatible configurations despite achieving higher apparent efficiencies. Across nine deterministic sensitivity scenarios, FOH and all compatibility-aware variants maintain feasibility. FOH matches the optimal workstation count in three scenarios, compared with eight for the compatibility-aware Largest Candidate Rule, and its maximum optimality gap reaches 16.67 percent. The findings establish FOH as a transparent and reproducible feasibility-oriented constructive procedure, while demonstrating that workstation-count performance remains dependent on task-selection strategies. Further validation on larger and independent industrial benchmarks is required to assess its generalizability.