In the quest for enhancing global optimization techniques, this paper introduces the Soccer Match Algorithm (SMA), a novel metaheuristic inspired by soccer dynamics. SMA models the strategic elements of a soccer game including tactical roles, compositions, playing styles, and player interactions. Existing metaheuristic algorithms often struggle with the balance between reliability and computational efficiency. Furthermore, many algorithms lack the adaptive mechanisms necessary for dynamic parameter tuning which are based on ongoing performance feedback. The objective of this research is to create a soccer-inspired algorithm that integrates an unprecedented array of soccer concepts and characteristics, alongside an adaptive learning framework, to dynamically boost performance and efficiency. This approach is novel among soccer-inspired algorithms. SMA is designed using simple, soccer-related conceptual frameworks such as player roles and game tactics. It includes mechanisms for dynamic parameter adjustment and tactical shifts during a game. The algorithm’s effectiveness was assessed through a series of benchmark unconstrained optimization problems. The experimental analysis reveals that SMA achieves remarkable performance metrics, closely matching those of leading metaheuristics like Harris Hawks Optimization and other soccer-inspired methods such as the Tiki-Taka Algorithm. Notably, SMA demonstrates high scalability, reliability, and operational efficiency with minimal computational effort. The obtained results make SMA a promising approach for optimization problems.

We consider the makespan minimization in a flowshop environment where the job sequence does not have to be the same for all the machines. Contrarily to the classical permutation flowshop scheduling problem, this strongly NP-hard problem received very scant attention in the literature. In this paper, some improved single-machine-based adjustment procedures are proposed, and a new two-machine-based one is introduced. Based on these adjustments, new lower and upper bounding schemes are derived. Our experimental analysis shows that the proposed procedures provide promising results.

In this paper, an identical parallel machine problem was considered with the objective of minimizing the makespan. This problem is NP-hard in the strong sense. A mathematical formulation based on an improved arc flow model with enhanced bounds was proposed. A variable neighborhood search algorithm was proposed to obtain an upper bound. Three lower bounds from the literature were utilized in the improved arc flow model to improve the efficiency of the mathematical formulation. In addition, a graph compression technique was proposed to reduce the size of the graph. As a consequence, the improved arc flow model was compared with an arc flow model from the literature. The computational results on benchmark instances showed that the improved arc flow model outperformed the literature arc flow model at finding optimal solutions for 99.97% of the benchmark instances, with the overall percentage of the reduction in time reaching 87%.