Abstract
This paper presents the results obtained through research on travel scheduling defined by large forestry companies using a truck allocation system known as Asicam for the systematization of the transportation process. This system carries out an efficient programming of the transport of wood in the different centers, reducing transport costs to a minimum and respecting the technical, political and operational restrictions of the company. However, Asicam only optimizes at the strategic level, but not at the operational level. For this reason, the truck scheduling process can be improved. That is why an extension of the vehicle programming problem is introduced and a genetic algorithm is proposed that self-adapts its parameters to solve the problem. The objective is to optimize the round-trip journey by intelligently self-adjusting the entry parameters, which in turn will optimize travel time, system performance and company costs.
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Enzo, VL., Oscar, RV. (2022). Genetic Algorithm for Optimization in Forest Industry Truck Scheduling. In: Arai, K. (eds) Advances in Information and Communication. FICC 2022. Lecture Notes in Networks and Systems, vol 438. Springer, Cham. https://doi.org/10.1007/978-3-030-98012-2_18
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DOI: https://doi.org/10.1007/978-3-030-98012-2_18
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