A MULTI-OBJECTIVE GENETIC ALGORITHMS APPROACH FOR MODELLING OF ORDER PICKING

Because the proportion of working-age people in the EU is shrinking, it is necessary to help employers to be able to install various aids to maintain the health of employees, especially in very demanding manual jobs. Well-being is thus becoming just as important as cost reduction. One such area is man-to-goods manual order picking. The paper proposes genetic algorithms (GA) to assist logistics managers in deciding about the most optimal pattern of stacking items in storage locations in storage racks. During the peak season, it makes sense to arrange items in terms of the minimum consumption of time when taking them manually out of the shelves and in periods of lower demand in terms of minimum chances of injury to employees and their low energy consumption. Based on experimental data, several models for predicting time, health risk, and energy consumption at order picking were developed by the GA. The results showed that GA is a powerful tool for resolving the storage assignment problems in terms of optimization according to individual criteria (time spent, risk of injury, or energy consumed) or searching for a common optimal solution.