Applying Metaheuristic For Harvester Maintenance Workforce Scheduling And Routing Optimization

Abstract

This thesis focused on the primary problem of optimizing the scheduling for maintenance of combined harvester to minimize the total cost and reduce the interruption during the process of harvesting rice. To increase the ability of conducting many repairs within one system of combined harvester or postpone the task into the next day, the mathematical model is used to modify the system and propose the optimized maintenance schedule for workforce problems. This MILP model concept is first applied to CPLEX software to give the optimal solution. Then, it was reimplemented using metaheuristics technique, specifically Particle Swarm Optimization Algorithm to effectively tackle the larger data of the problem by Python code. After having the result, the sensitivity analysis is applied to evaluate the efficiency of this methodologies.