Simulation Aided Anticipatory Congestion Avoidance for Warehouses

Abstract

There is a sudden surge in the number of people ordering items online that are facilitated through warehouses. Modern-day warehouses use robots for most of the tasks including picking and sorting. Efficiency is of prime concern in any warehouse, while current efforts in the literature are restricted to solving the optimization of warehouse processes as an operational research problem using fixed travel costs. As the number of orders and robots increases, congestion is witnessed in the warehouse that invalidates fixed travel costs assumptions. To facilitate research in warehousing we first propose a modular simulator for the warehouse that simulates the business operations of order generation, order fulfillment scheduling, item picking, and sorting. The simulator also models the robots for traveling within the warehouse network, scheduling charging, intersection management, and congestion management. With the increasing demand, the warehouses increase the number of robots making the transportation network operate beyond capacity. In this direction, we analyze the performance of the warehouse from a transportation perspective using fundamental diagrams. The warehouses contain only controllable entities (robots) that enable predicting the congestion levels for solving the planning problem. The results show improvements of around 5% in the order fulfillment time and the number of picks that can significantly increase the profitability of the warehouse.