Robots’ picking efficiency and pickers’ energy expenditure: the item storage assignment policy in robotic mobile fulfillment system

Abstract

The item storage assignment problem (ISAP) in a robotic mobile fulfillment system (RMFS) is addressed in the paper. Recently, most ISAP studies have concentrated on improving the robots’ picking efficiency while ignoring the fact that RMFS is a human-robot coordinated system. In ISAP, we also need to take human factors into account. This research investigates ISAP by considering both the robots’ picking efficiency and the pickers’ energy expenditure. The bi-objective mixed-integer programming model is developed to maximize the items’ similarities in the pods and minimize the pickers’ energy expenditure. The improved knee point-driven evolution algorithm (IKnEA) is proposed to solve the model. Experimental results demonstrate that IKnEA has superior distribution and convergence speed than NSGA-II and KnEA. IKnEA’s hypervolume (HV) indicator operates better than NSGA-II and KnEA in various warehouse scales. More layers of pods contribute to less travel distance for robots but more energy expenditure for pickers. Five Pareto solutions are presented to assist the decision-makers in selecting the optimal ISAP solutions given various preferences.