Truck Loading Schedule Optimization Using Genetic Algorithm for Yard Management

Abstract

Abstract.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 During a day the current schedule often becomes outdated due to various unexpected difficulties in loadings and unpredicted delays in trucks arrival.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 realworld 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 IntroductionThe problem of dock assignment and truck scheduling problem has been drawing the attention of many researchers recently. Most of works are devoted to the problem of cross docking where shipments are transferred directly from incoming to outgoing trucks without storage in between. In a cross docking model customers are known before the goods get to the warehouse and hence the two most expensive warehousing operations , i.e. storing and retrieving are eliminated. The reviews of earlier literature on o mathematical models cross-dock planning provided Agustina et al. [1], and and quantitative approaches for dock door assignment in cross-docking elaborated Shuib et al. [2]. The authors develop integer linear programming as well as non-linear dynamic programming models which typical objective is to minimize the total penalty of earliness and tardiness in incoming and outgoing trucks. Since dock assignment and truck scheduling problems are NP-hard various meta heuristic procedures have been proposed in literature to solve these models.

Intelligent Loading of Scattered Cargoes Based on Improved Ant Colony Optimization

“…Under current strategies, the cargoes are usually loaded in the patterns of walls, layers [1], towers [2] or blocks [3]. The loading process can be improved through combinatorial optimization [4,5], mathematical programming [6] and artificial intelligence [7,8]. Facing heavy computing load and fuzzy convergence conditions, the first two optimization approaches have difficulty in outputting ideal solutions.…”