TY - JOUR
T1 - Finding the right one
T2 - Decision support for selecting cost-efficient order picking solutions
AU - Schäfer, Fabian
AU - Lorson, Fabian
AU - Hübner, Alexander
N1 - Publisher Copyright:
© Copyright © 2024 “IISE”.
PY - 2025
Y1 - 2025
N2 - Enabled via recent technological advances coupled with the advent of new systems providers and decreased price points, automated and robotized order-picking solutions (e.g., pick-assisting autonomous mobile robots) have evolved as a surging market. Such innovative picking technologies aim to reduce labor costs, use available space more efficiently, and increase throughput rates. As implementation projects and the variety of solutions rise, managers decide which ones to select for their specific warehouse and products. However, comprehensive decision models for this strategic problem are lacking in the pertinent literature. We propose a mathematical optimization model for the novel problem that selects and sizes order-picking solutions and assigns them products and warehouse spaces. Expert interviews are used to identify the comprehensive decision-relevant costs and constraints. Specifically, we minimize setup, module, labor, and error costs while adhering to characteristics related to the area (e.g., available space), technology (e.g., throughput, handling capabilities of certain products), and product (e.g., physical dimensions). We conduct a case study and complement our findings with numerical experiments. We find significant cost reduction potential of up to 57% by selecting a mix of different order-picking solutions. Further analyses highlight the need to retain human workers and to account for maximum labor capacity.
AB - Enabled via recent technological advances coupled with the advent of new systems providers and decreased price points, automated and robotized order-picking solutions (e.g., pick-assisting autonomous mobile robots) have evolved as a surging market. Such innovative picking technologies aim to reduce labor costs, use available space more efficiently, and increase throughput rates. As implementation projects and the variety of solutions rise, managers decide which ones to select for their specific warehouse and products. However, comprehensive decision models for this strategic problem are lacking in the pertinent literature. We propose a mathematical optimization model for the novel problem that selects and sizes order-picking solutions and assigns them products and warehouse spaces. Expert interviews are used to identify the comprehensive decision-relevant costs and constraints. Specifically, we minimize setup, module, labor, and error costs while adhering to characteristics related to the area (e.g., available space), technology (e.g., throughput, handling capabilities of certain products), and product (e.g., physical dimensions). We conduct a case study and complement our findings with numerical experiments. We find significant cost reduction potential of up to 57% by selecting a mix of different order-picking solutions. Further analyses highlight the need to retain human workers and to account for maximum labor capacity.
KW - Automated warehouse
KW - decision support
KW - distribution center
KW - facilities planning
KW - flexible automation
KW - material handling equipment
KW - order picking system
KW - robotic systems
KW - smart warehouse
KW - warehouse design
UR - http://www.scopus.com/inward/record.url?scp=85182836790&partnerID=8YFLogxK
U2 - 10.1080/24725854.2023.2284317
DO - 10.1080/24725854.2023.2284317
M3 - Article
AN - SCOPUS:85182836790
SN - 2472-5854
VL - 57
SP - 75
EP - 89
JO - IISE Transactions
JF - IISE Transactions
IS - 1
ER -