Workshop Scheduling (MATLAB Implementation)

Workshop Scheduling (MATLAB).rar

1. Parameter Initialization:

Population size: 60 individuals, Maximum iterations: 500 generations, Crossover probability: 0.8, Mutation probability: 0.6, Generation gap: 0.9. These parameters control the genetic algorithm's convergence behavior and search capabilities.

2. Population Initialization:

Initial population is generated using priority-based encoding for scheduling. For example, with 3 parts each containing 3 processes (total 9 operations), the chromosome representation could be initialized as:

Encoding example 1: 1, 3, 4, 5, 6, 7, 8, 9, 2

Encoding example 2: 2, 1, 3, 4, 5, 6, 7, 8, 9

Each number represents an operation sequence, where the encoding scheme maps operations to their execution order in the scheduling timeline.

The workshop scheduling optimization is implemented using MATLAB, utilizing genetic algorithm operators including selection, crossover, and mutation functions to evolve better scheduling solutions through successive generations.