Abstract
The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise total operational cost. One of the main obstacles in designing a genetic algorithm for this highly-constrained combinatorial optimisation problem is the amount of empirical tests required for parameter tuning. This paper presents a genetic algorithm that uses a diversity-based adaptive parameter control method. Experimental results show the effectiveness of this parameter control method to enhance the performance of the genetic algorithm. This study makes a contribution to research on adaptive evolutionary algorithms applied to real-world problems.