Energy minimizing order picker forklift routing problem

The material handling systems used in warehouses involve important operations such as the usage of order picker forklifts. These order picker forklifts provide efficient utilization of the storage space by their ability in moving in narrow aisles and picking items from high level racks. Routing the order picker forklifts to pick ordered items belongs to the operational decision level and is done in high frequency. Therefore, finding an energy-efficient route for an order picker forklift can yield significant savings in the energy consumption in warehouses and the resulting CO2 emission. In this paper, we introduce and study the energy minimizing order picker forklift routing problem (EMFRP) which aims to find an energy-efficient route for an order picker forklift to pick a given list of items. To our knowledge, this is the first study that considers the order picker forklifts in the context of the order picking problem. We calculate the forklift's energy consumption in both horizontal and vertical moves considering the effects of fric-tion forces, the acceleration and deceleration of the forklift, and its load. A mixed integer programming formulation and a dynamic programming approach are developed to solve small size instances of the EMFRP exactly. To solve larger instances, we provide tour construction and tour improvement heuristics and integrate them into a single solution approach. Computational results show that the proposed heuris-tic approach finds high quality solutions. Moreover, it is observed that significant energy savings can be achieved by solving the EMFRP instead of the classical distance minimization problem.& COPY; 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces the energy minimizing order picker forklift routing problem (EMFRP) in the context of warehouse operations. It aims to find an energy-efficient route for order picker forklifts to pick a given list of items, which can significantly reduce energy consumption and CO2 emission. The study proposes mathematical formulations, dynamic programming approaches, and heuristic algorithms to solve the EMFRP and achieve high-quality solutions.