A hybrid approach integrating genetic algorithm and machine learning to solve the order picking batch assignment problem considering learning and fatigue of pickers

Abstract

Modeling human behaviors has become increasingly relevant to improving the performance of manual order-picking systems. However, although a vast corpus of literature has recently started to consider the human factors in these systems, several gaps remain uncovered. Specifically, mental and physical human factors, like learning and fatigue, and quantitative and spatial features of picking orders have never been considered jointly to estimate the time a human order picker requires to execute a specific picking mission. Furthermore, little attention has been given to assigning and sequencing orders to pickers to minimize the picking time acting on their individual learning and fatigue characteristics. This study thus proposes a novel approach integrating machine learning and genetic algorithms to solve the problem. A non-linear machine learning-based predictive model has been adopted to predict the picking time of batches of orders based on quantitative and spatial features of batches and learning and fatigue indicators of pickers. These predictions have thus been adopted to guide a genetic algorithm to find the best assignment of future planned batches of orders to pickers. One year of picking data collected from the warehouse of a grocery retailer has been adopted to investigate the potential of the proposed approach. Furthermore, multiple comparisons have been performed. First, the advantages of predicting the batch-picking time with the proposed non-linear model have been compared with predictions executed based on linear models. In addition, an ablation analysis has been performed to investigate the advantages of predicting the batch picking time while simultaneously considering the quantitative and spatial features of batches and the learning and fatigue indicators of pickers. Moreover, the advantages of the proposed batch assignment strategy, which considers learning and fatigue indicators, have been compared with an assignment strategy that does not optimize these elements. Lastly, an explainability analysis of the predictive model has been performed to understand how and how much quantitative and spatial features of batches and learning and fatigue indicators of pickers affect the batch picking time.