On the Feasibility of Using a High-Level Solver within Robotic Mobile Fulfillment Systems

Abstract

A Robotic Mobile Fulfillment System (RMFS) is a collaborative environment in which a robot delivers products to human for fulfilling orders. However, it is a computationally complex optimization problem. In this work, we analyze the feasibility of using high-level solvers for selecting suitable low-level methods. To this end, we generate 111 instances distributed into two datasets. Moreover, we implement two kinds of high-level solvers. The first one is a set of handcrafted rules. The second approach uses a decision tree. Our data reveals that it is possible to construct high-level solvers that benefit from the different strengths of the low-level methods by selecting which one to apply. The rules produced by hand and the decision trees high-level solvers are competitive concerning the best individual performer in terms of two standard metrics for this problem: throughput time and orders completed.