This study presents an innovative application of genetic algorithms (GAs) for optimizing the Cobb–Douglas production function, a cornerstone of economic modeling that examines the relationship between production output and the inputs of labor and capital. This research integrates traditional optimization methods, such as partial derivatives, with evolutionary computation techniques to address complex economic constraints. The methodology demonstrates how GAs outperform classical techniques in solving constrained optimization problems, offering superior robustness, adaptability, and efficiency. Key results highlight the alignment between GA solutions and traditional Lagrangian methods while underscoring the computational advantages of GAs in navigating non-linear and multi-modal landscapes. This work serves as a valuable resource for both educators and practitioners, offering insights into the potential of GAs to enhance optimization processes in engineering, economics, and interdisciplinary applications. Visual aids and pedagogical recommendations further illustrate the algorithm’s utility, making this study a significant contribution to the computational optimization literature. Additionally, the optimization process using genetic algorithms is presented in a step-by-step manner, with accompanying visual graphs that enhance comprehension and demonstrate the method’s effectiveness in solving mathematical problems, as validated by the study’s results.

The assembly line balancing problem (ALBP) has been an attractive research area for decades; however, the industrial application of the research findings remains limited. This can be attributed to the complexity of solution methods, restrictive assumptions, and the numerous variants of the problem in the real-world settings. This article describes using the precedence matrix as the basis for developing simplified, more practical analysis frameworks for the ALBP. We introduce algorithms to construct the precedence matrix from basic assembly problem data and implement it within a spreadsheet model, and to utilize this matrix in assigning the assembly tasks to workstations while ensuring assignment feasibility. The structure and potential uses of the precedence matrix-based framework are presented in detail. Furthermore, we compare selected line balancing priority rules using the proposed framework to demonstrate its effectiveness. The results suggest that the precedence matrix model is a straightforward, efficient tool for line balancing analysis, that can offer substantial support to both industry practitioners and academic researchers.