Pallet Scheduling Models Under Deterministic and Non-Deterministic Scenarios Using a Hybrid GA Method

Abstract

This study examines the pallet scheduling problem considering random demands under the novel pallet operation mechanism by resources sharing among the pallet sharing system. Two nonlinear integer pallet scheduling models under deterministic and non-deterministic environment are formulated in terms of the pallet demand variable. To solve the pallet programming model, the hybrid genetic algorithm (HGA) integrating local search strategy is designed to derive the optimal pallet scheduling solution. Besides, the fixed sample size sampling strategy is employed to deal with the uncertain demand during the non-deterministic programming model, realized by the Monte Carlo simulation. The two models can assist decision makers arrange a scientific pallet scheduling solution under deterministic and non-deterministic atmosphere. Finally, the numerical case is implemented to testify the effectiveness of the two models and efficiency of the hybrid algorithms.