Truck Loading Schedule Optimization Using Genetic Algorithm for Yard Management
A new information system for order and yard management was implemented and deployed in a timber products company. The system was equipped with an innovative mechanism which automatically updates loading appointment schedule on the basis of current data of truck arrivals and departures. During a day the current schedule often becomes outdated due to various unexpected difficulties in loadings and unpredicted delays in trucks arrival. In the paper a genetic algorithm which is the core of the updating mechanism was presented. Penalty functions were employed in order to protect its solution against violating constraints. The algorithm was enhanced by additional processing just before computing the value of the fitness function. The improved genetic algorithm was experimentally evaluated both in terms of correctness and speed of producing the loading appointment schedule for a test problem. Moreover the simulation of its planned exploitation was performed using real-world data. The proposed genetic algorithm revealed better performance than the competitive particle swarm optimisation method as well as rescheduling made by the dispatchers manually.
Keywords:Loading schedule Genetic algorithm Particle swarm optimization Yard management Manufacturing company
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