Assembly Line Task Balancing Optimization Genetic Algorithm with MATLAB Source Code

Assembly Line Balancing (ALB) is a classical manufacturing optimization problem focused on optimally allocating work tasks to stations to maximize production line efficiency. Genetic algorithms serve as effective heuristic methods for solving such combinatorial optimization challenges.

This MATLAB implementation incorporates three key enhancements over classical approaches: First, it employs a dynamic fitness function design that evaluates individuals based not only on workstation count but also includes cycle time deviation penalties. Second, innovative two-point crossover and mutation operators maintain offspring feasibility through precedence relationship preservation. Third, an elitism strategy prevents loss of high-quality solutions during evolution.

The algorithm's core workflow involves five critical steps implemented in code: Population initialization using heuristic rules generates feasible solutions; Selection phase applies roulette wheel mechanism to maintain diversity; Crossover operations preserve feasibility through task precedence constraints; Mutation implements both insertion and exchange perturbation methods; Adaptive parameters dynamically control evolutionary direction. These improvements significantly enhance convergence speed and solution quality.

The implementation handles various ALB problem types including Simple Assembly Line Balancing (SALB) and Mixed-Model Assembly Line (MMAL) scenarios. Users can adapt the code to specific production requirements by modifying input parameters such as task time matrices, precedence graphs, and cycle times. For large-scale problems, parallel computing integration is recommended for improved performance.