Search

Symmetry is an important principle and characteristic that is prevalent in nature and artificial environments. In the three-dimensional packing problem, leveraging the inherent symmetry of goods and the symmetry of the packing space can enhance packing efficiency and utilization.The three-dimensional packing problem is an NP-hard combinatorial optimization problem in the field of modern logistics, with high computational complexity. This paper proposes an improved genetic algorithm by incorporating a fusion tabu search strategy to address this problem. The algorithm employs a three-dimensional loading mathematical model and utilizes a wall-building method under residual space constraints for stacking goods. Furthermore, adaptation of fitness variation strategy, chromosome adjustment, and tabu search algorithm are introduced to balance the algorithm's global and local search capabilities, as well as to enhance population diversity and convergence speed. Through testing on benchmark cases such as Bischoff and Ratcliff, the improved algorithm demonstrates an average increase of over 3% in packing space utilization compared to traditional genetic algorithms and other heuristic algorithms, validating its feasibility and effectiveness. The proposed improved genetic algorithm provides new insights for solving three-dimensional packing problems and optimizing logistics loading schedules, offering promising prospects for various applications. [ABSTRACT FROM AUTHOR]

Three mathematical models have been used to predict the dehydration kinetics of grape pomace. To the aim, three different temperatures have been set to determine the different desorption kinetics. All the dehydration kinetic data have been fitted to verify the predictive ability of the proposed models. The fitting goodness were assessed by using four coefficients: E ‾ % , R M S E , χ 2 , and R 2. Results show that all the proposed models can adequately fit the experimental data. In particular, the Dual-Stage Type B model is the best one, whereas the Fick model is the worst. The soundness of the mathematical approach adopted was tested by assessing the dependence of the model's parameters on the testing temperature: only some parameters followed the expected trend. The computational complexity referred to each model was considered evaluating the suitability of the proposed models to describe the desorption kinetics. To sum up, the Dual-Stage Type B model is the most complex, whereas the simplest one is the Dual-Stage Type A model. Considering both suitability and complexity, the Dual-Stage Type A is the best model to predict the dehydration kinetic of grape pomace. • 3 models have been used to predict grape pomace's dehydration kinetic. • The best but most complex model is the Dual-Stage Type B model. • The Dual-Stage Type A combines good predictive capacity with simplicity. [ABSTRACT FROM AUTHOR]