COMPARISON OF TWO HEURISTIC APPROACHES FOR SOLVING THE PRODUCTION SCHEDULING PROBLEM

Production scheduling problems attract a lot of attention among applied scientists and practitioners working in the field of combinatorial optimization and optimization software development since they are encountered in many different manufacturing processes and thus effective solutions to them offer great benefits. In this work, two commonly used heuristic methods for solving production scheduling problems, namely, the Nearest Neighbor (NN) and Ant Colony Optimization (ACO) have been tested on a specific real-life problem and the results discussed. The problem belongs to the class of Asymmetric Travelling Salesman Problems (ATSP), which is known as a hard type problem with no effective solutions for large scale problems available yet. The performances of the Nearest Neighbor algorithm and the Ant Colony Optimization technique were evaluated and compared using two criteria, namely: the minimum value of the objective function achieved and the CPU time it took to find it (including the statistical confidence limits). The conclusions drawn suggest that on one hand the ACO algorithm works better than NN if looking at the achieved minimum values of the objective function. On the other hand, the computational time of the ACO algorithm is slightly longer.