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We introduce a novel method called subdividing labeling genetic algorithm (SLGA) to solve optimization problems involving n – dimensional continuous nonlinear functions. SLGA is based on the mutation and crossover operators of genetic algorithms, which are applied on a subdivided search space where an integer label is defined on a polytope built on the n – dimensional space. The SLGA method approaches a global optimal solution by reducing the feasible search region in each iteration. One of its main advantages is that it does not require computing the derivatives of the objective function to guarantee convergence. We apply the SLGA method to solve optimization problems involving complex combinatorial and large-scale systems and illustrate numerically how it outperforms several other competing algorithms such as Differential Evolution even when considering problems with a large number of elements. [ABSTRACT FROM AUTHOR]

Cross-docking is a logistics strategy for direct distribution of products from a supplier or manufacturing plant to a customer or retail outlet with little or no handling and storage time. The classical cross-docking models are used to find the optimal inbound/outbound truck schedule that minimizes the total operational time. We propose a new multi-period cross-docking model with multiple products, due dates, variable truck capacities, and temporary warehouse. The problem is formulated as mixed-integer programming and an evolutionary computation approach based on a genetic algorithm (GA) is designed to solve it. The structure of the chromosomes, the operators, and the constraint handling strategy are specifically designed for multi-period problems. Several test instances have been generated to compare the performance of the proposed GA with that of a branch and bound solution procedure. Moreover, a comprehensive statistical analysis is conducted to illustrate the performance efficacy of the proposed GA relative to the branch and bound algorithm. This analysis reveals that the GA provides a substantial decrease in the computational burden when compared to the branch and bound algorithm. [ABSTRACT FROM AUTHOR]