A New Artificial Intelligence Algorithm for Solving the Integrated Model of Aggregate Production Planning and Scheduling Problem (Journal of Engineering, 2026)
In recent years, the integration of production planning and control scheduling has become a significant factor in complex and dynamic manufacturing environments. Therefore, this paper addresses this challenge by introducing an integrated model that simultaneously optimizes both aggregate production planning and single machine scheduling. The primary objectives of the developed model are to minimize overall production cost, makes pan, setup costs, and product delivery delays as key performance indicators. To solve the integrated model, two hybrid metaheuristic algorithms are applied with a novel hybridization ratio (40%-60%): the Hybrid Whale Algorithm with the Fruit Fly optimization algorithm and the Whale Algorithm with the Grey Wolves Optimization Algorithm. Large set of computational experiments are conducted on 10 instances with 20 to 200 jobs and 100 planning periods, with 30 independent runs. The results show that both hybrid algorithms are highly effective in finding near optimal solutions to the integrated complex problem. Comparative analysis demonstrates that the hybrid algorithms outperform common algorithms (Whale, Genetic, Fly, and grey wolf) and the dynamic programming method in terms of solution quality, convergence speed, and computational stability in most tested scenarios. These findings provide practitioners with robust decision-support tools to improve operational efficiency in modern manufacturing systems.