Abstract
Concerns about environmental degradation and fossil fuel depletion have led to the advent of energy-aware manufacturing and material handling processes in factories and warehouses. However, as the transition to eco-friendly material handling by electric material handling vehicles (EMV) is progressing, the use of electric forklifts (EFs) remains a challenge, as these EMVs are recognized only as energy consumers. In this paper, we develop an integrated dynamic algorithm for solving the EF routing problem with battery charging constraints in which EFs' picking or put-away routes, EFs' battery charging schedules, and the number of EFs operated are simultaneously determined while considering electricity consumption in a warehouse. Time series of electricity-usage penalty estimated by predicted energy consumption in a warehouse facility and equipment level play key roles in establishing EF battery charging schedules. Dynamic models for the arrival processes in material handling and EF battery charging jobs in multiple EF queues are developed and implemented as core engines in the proposed dynamic control algorithm. Operational performance and energy performance of the proposed dynamic algorithm are examined using real energy and operational parameters of the Toyota 9BRU23/16.5 reach truck and compared to those of the metaheuristic approach, called adaptive large neighborhood search. Experimental results of large-size instances with uniformly distributed job locations show that an average 5.6% better performance is achieved by the proposed dynamic algorithm. An additional experiment with the proposed approach and clustered job locations results in 8.9% lower energy-related costs and 1.2% shorter EF travel distances, demonstrating the competitiveness of the proposed energy-aware EF operations strategy for warehouse administration.
