Hybrid particle swarm optimization and neighborhood strategy search for scheduling machines and equipment and routing of tractors in sugarcane field preparation

Abstract

This paper presents the Hybrid Particle Swarm Optimization and Neighborhood Strategy Search (HPSO-NS) to solve a tractor scheduling and routing problem with equipment allocation constraint in sugarcane field preparation, to help the growers catch the season and ensure advantageous production of sugar from sugarcane. This problem can be formulated as the flexible flow shop scheduling problem with machine eligibility, time windows, sequence dependent setup time (SDST), blocking, machine restriction and machine grouping (FFS |Ssmt,Mj, Grouping, block, 6-stage, Tool, Tw |∑iNRi). A mixed-integer programming model was developed to solve small-scale problems. The HPSO-NS was developed for large-scale problems, and three neighborhood strategies were added to the PSO procedure and developed. Moreover, two new formulae which were used to select the neighborhood strategy in HPSO-NS are presented in this paper to increase the performance of the proposed method. The computational results show that the HPSO-NS outperforms the original PSO and the lower bound obtained from the optimization software, while using 97% less computational time.