Elif Yıldırım

Small and medium-sized enterprises (SMEs) constitute a substantial share of industrial production. However, their operational performance is frequently constrained by delivery delays caused by inefficiencies in workforce scheduling and task sequencing. These limitations reduce overall competitiveness, particularly in project-based manufacturing environments where task heterogeneity and multi-skill variability are prominent. To address this challenge, this study develops an artificial intelligence based workforce planning framework tailored to capital-constrained manufacturing settings. The new proposed hybrid system integrates a Genetic Algorithm (GA), Monte Carlo Simulation (MCS), and Taguchi methodology to generate robust, uncertainty-aware labor assignments. The framework is validated through 18-month deployments in two manufacturing facilities with differing levels of technological maturity, demonstrating consistent improvements in operational outcomes. Furthermore, specific weekly examples were validated against the solutions of exact mixed integer linear programming solvers on the deterministic core to assess the optimality gap and ensure constant solution quality. Across the deployments, the system achieved 13% and 15% reduction in task completion times. The resulting GA–MCS–Taguchi pipeline operates efficiently on standard SMEs hardware, requires only short historical performance windows for calibration, and exhibits high user adoption in real industrial settings, which indicates strong operational viability and practical deployability.

Scheduling and sequencing problems in manufacturing are complex and challenging to solve. Effective process planning is fundamental to optimizing production time and resource utilization in process-type manufacturing environments such as tire manufacturing. This research focuses on an existing tire manufacturing process. The scheduling problem in the compound mixing stage, which is considered the most challenging and vital stage of tire manufacturing, has been solved in this study. Adaptive Moment Estimation Optimizer (ADAM Optimizer), Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) are selected as solution methodologies. A comparative analysis is performed to evaluate the effectiveness of these algorithms based on critical performance metrics, including completion times, machine utilization, and setup numbers. The results of this study show that ADAM and algorithmic methods optimize machine utilization by 1.28% and save 32.6% production time, outperforming the traditional manual allocation strategies mainly used by industrial companies, as well as GWO and GA.

Storage operations, order-picking, and product-handling processes have become increasingly important in today’s industrial environment. These operations are a huge burden for businesses in terms of time and cost, but they often do not add direct value to products or services. Therefore, it has become essential to improve the storage operations to the highest quality, reduce the costs arising from storage, and increase customer satisfaction. This study compared genetic algorithm (GA) and simulated annealing (SA) methods with existing real results and operations in order to minimize the distance traveled by the picker in order-picking systems, optimize routes, and increase operational efficiency in the medical textile industry. In the analyses conducted on product-based, list-based, and order-based strategies, real data sets were used to examine the performance of both methods in detail. The study results revealed that GA reduced the total travel distance by 50% and reduced the total number of tours from 51 to 32. In addition, the SA method provided efficient results in certain scenarios, but GA showed superior performance in terms of minimizing the distance and number of tours. While the product-based strategy provided the best results regarding travel distance and number of tours, the list-based approach showed a balanced performance. The study offers significant improvement potential in logistics operations by reducing distances by up to 37% and increasing operational efficiency by up to 50% in order-picking processes.