A case study of energy-efficient industrial warehousing with digital tools: Energy-efficient industrial warehousing

Kimi Haapalainen; Marko Jurmu; Mikko Heiskanen; Eero Anttila; Juha Maunula; Andrea Fernández Martínez; Santiago Muiños-Landin; Heikki Ailisto

A case study of energy-efficient industrial warehousing with digital tools: Energy-efficient industrial warehousing

Kimi Haapalainen
, Marko Jurmu
, Mikko Heiskanen
, Eero Anttila
, Juha Maunula
, Andrea Fernández Martínez
, Santiago Muiños-Landin
, Heikki Ailisto

Pesmel Oy
Asociación De Investigación Metalúrgica Del Noroeste (AIMEN)

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

Abstract

In the domain of industry warehousing, automated storage and retrieval systems (ASRS) consist of warehouse racks and stacker cranes that operate in a cartesian coordinate system to achieve a desired product flow. These systems maximize their throughput when the devices are operated at their maximum velocity and acceleration constraints. From a product lifecycle management viewpoint, operations with adaptive parameters could reduce energy consumption while meeting temporal constraints. In this paper, we investigate the applicability of two AI tools for sustainable lifecycle management of a stacker crane. We formulate parts of the lifecycle management as a data and AI problem.
We divide warehouse management to two categories: Resource allocation and operation optimization. Finally, we perform empirical validation through a warehouse management simulation environment. Our findings benefit lifecycle management strategies and operations of ASRS, contributing to more sustainable industry practices.

Original language	English
Title of host publication	Proceedings of 3rd European Symposium on Artificial Intelligence in Manufacturing
Publisher	Springer
Number of pages	8
Publication status	Accepted/In press - 2025
MoE publication type	A4 Article in a conference publication
Event	3rd European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2025 - Donostia-San Sebastian, Spain Duration: 8 Oct 2025 → 8 Oct 2025

Publication series

Series	Lecture Notes in Mechanical Engineering
ISSN	2195-4356

Conference

Conference	3rd European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2025
Country/Territory	Spain
City	Donostia-San Sebastian
Period	8/10/25 → 8/10/25

Funding

This work has been supported by the project “Digital assets and tools for Circular value chains and manufacturing products” (DaCapo), which has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No. ID: 101091780. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union o HADEA. Neither the European Union nor the granting authority can be held responsible for them.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure

Keywords

sustainability
data
product lifecycle
automated warehousing

Cite this

@inproceedings{cfd989a43f1f46bf90d2b1103967c3a6,

title = "A case study of energy-efficient industrial warehousing with digital tools: Energy-efficient industrial warehousing",

abstract = "In the domain of industry warehousing, automated storage and retrieval systems (ASRS) consist of warehouse racks and stacker cranes that operate in a cartesian coordinate system to achieve a desired product flow. These systems maximize their throughput when the devices are operated at their maximum velocity and acceleration constraints. From a product lifecycle management viewpoint, operations with adaptive parameters could reduce energy consumption while meeting temporal constraints. In this paper, we investigate the applicability of two AI tools for sustainable lifecycle management of a stacker crane. We formulate parts of the lifecycle management as a data and AI problem. We divide warehouse management to two categories: Resource allocation and operation optimization. Finally, we perform empirical validation through a warehouse management simulation environment. Our findings benefit lifecycle management strategies and operations of ASRS, contributing to more sustainable industry practices.",

keywords = "sustainability, data, product lifecycle, automated warehousing",

author = "Kimi Haapalainen and Marko Jurmu and Mikko Heiskanen and Eero Anttila and Juha Maunula and \{Fern{\'a}ndez Mart{\'i}nez\}, Andrea and Santiago Mui{\~n}os-Landin and Heikki Ailisto",

year = "2025",

language = "English",

series = "Lecture Notes in Mechanical Engineering",

publisher = "Springer",

booktitle = "Proceedings of 3rd European Symposium on Artificial Intelligence in Manufacturing",

address = "Germany",

note = "3rd European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2025 ; Conference date: 08-10-2025 Through 08-10-2025",

}

Haapalainen, K , Jurmu, M , Heiskanen, M, Anttila, E, Maunula, J, Fernández Martínez, A, Muiños-Landin, S & Ailisto, H 2025, A case study of energy-efficient industrial warehousing with digital tools: Energy-efficient industrial warehousing. in Proceedings of 3rd European Symposium on Artificial Intelligence in Manufacturing. Springer, Lecture Notes in Mechanical Engineering, 3rd European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2025, Donostia-San Sebastian, Spain, 8/10/25.

A case study of energy-efficient industrial warehousing with digital tools: Energy-efficient industrial warehousing. / Haapalainen, Kimi ; Jurmu, Marko ; Heiskanen, Mikko et al.
Proceedings of 3rd European Symposium on Artificial Intelligence in Manufacturing. Springer, 2025. (Lecture Notes in Mechanical Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - A case study of energy-efficient industrial warehousing with digital tools

T2 - 3rd European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2025

AU - Haapalainen, Kimi

AU - Jurmu, Marko

AU - Heiskanen, Mikko

AU - Anttila, Eero

AU - Maunula, Juha

AU - Fernández Martínez, Andrea

AU - Muiños-Landin, Santiago

AU - Ailisto, Heikki

PY - 2025

Y1 - 2025

N2 - In the domain of industry warehousing, automated storage and retrieval systems (ASRS) consist of warehouse racks and stacker cranes that operate in a cartesian coordinate system to achieve a desired product flow. These systems maximize their throughput when the devices are operated at their maximum velocity and acceleration constraints. From a product lifecycle management viewpoint, operations with adaptive parameters could reduce energy consumption while meeting temporal constraints. In this paper, we investigate the applicability of two AI tools for sustainable lifecycle management of a stacker crane. We formulate parts of the lifecycle management as a data and AI problem. We divide warehouse management to two categories: Resource allocation and operation optimization. Finally, we perform empirical validation through a warehouse management simulation environment. Our findings benefit lifecycle management strategies and operations of ASRS, contributing to more sustainable industry practices.

AB - In the domain of industry warehousing, automated storage and retrieval systems (ASRS) consist of warehouse racks and stacker cranes that operate in a cartesian coordinate system to achieve a desired product flow. These systems maximize their throughput when the devices are operated at their maximum velocity and acceleration constraints. From a product lifecycle management viewpoint, operations with adaptive parameters could reduce energy consumption while meeting temporal constraints. In this paper, we investigate the applicability of two AI tools for sustainable lifecycle management of a stacker crane. We formulate parts of the lifecycle management as a data and AI problem. We divide warehouse management to two categories: Resource allocation and operation optimization. Finally, we perform empirical validation through a warehouse management simulation environment. Our findings benefit lifecycle management strategies and operations of ASRS, contributing to more sustainable industry practices.

KW - sustainability

KW - data

KW - product lifecycle

KW - automated warehousing

M3 - Conference article in proceedings

T3 - Lecture Notes in Mechanical Engineering

BT - Proceedings of 3rd European Symposium on Artificial Intelligence in Manufacturing

PB - Springer

Y2 - 8 October 2025 through 8 October 2025

ER -

A case study of energy-efficient industrial warehousing with digital tools: Energy-efficient industrial warehousing

Abstract

Publication series

Conference

Funding

UN SDGs

Keywords

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Cite this