In this study, twelve modified differential evolution algorithms with memory properties and adaptive parameters were proposed to address optimization problems. In the experimental process, these modified differential evolution algorithms were applied to 23 continuous test functions. The results indicate that MBDE2 and IHDE-BPSO3 outperform the original differential evolution algorithm and its extended variants, consistently achieving optimal solutions in most cases. The findings suggest that the proposed improved differential evolution algorithm is highly adaptable across various problems, yielding superior results. Additionally, integrating memory properties significantly enhances the algorithm’s performance and effectiveness.

The Apparent Tardiness Cost (ATC) dispatching rule was initially developed to minimize tardiness in single-machine scheduling problems. ATC extensions have been frequently applied in other production settings, relying heavily on blocking idle machine capacity with a single-machine outlook; this approach may not result in the best outcomes, considering that machines have different efficiencies. This study develops a new dispatching rule for parallel-machine scheduling, considering different machine efficiencies, ready times, and sequence-dependent setup times to minimize the total weighted tardiness. The proposed method reduces the time interference factor of the denominator item in the dispatching rule and uses more effective methods for selecting the best processing machine for the jobs. The grid approach is used to evaluate the method against the state-of-the-art. The experimental results confirm that the developed method is superior regardless of the type of parallel machines, the problem scale, and other operational parameters. It is also shown that other ATC dispatching rules can be improved by applying the proposed approach. The proposed method could be incorporated into soft computing techniques for more effective and efficient scheduling.

A cross-docking system is a kind of facility design for the purpose of enhancing the time efficiency of a distribution center. In this study, we attempt to consider a cross-docking system without temporary storage and obtain great system performance by addressing the scheduling problem of inbound and outbound trucks, in which the total operation time of trucks is minimized. In order to reduce computational efforts, three hybrid metaheuristic approaches based on particle swarm optimization, simulated annealing, and a variable neighborhood search are proposed. By the computational experiments, the three optimized approaches are analyzed and compared with each other. The experimental results show that all of these three approaches can obtain pretty good solutions, even in the large-scale examples. Moreover, one of these approaches—a hybrid metaheuristic with particle swarm optimization and a variable neighborhood search—can usually obtain the best solutions.