An Order-Picking Problem in a Medical Facility Using Genetic Algorithm

Abstract

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.