Green smart manufacturing: energy-efficient robotic job shop scheduling models

ABSTRACT

Smart manufacturing has boosted the wide application of mobile robots in robotic cells for automated material delivery. However, the mismatching between machine production process and robot movement process causes extensive energy waste. Nevertheless, most existing robotic job-shop scheduling (RJSP) studies mainly focus on minimising makespan but overlook the low energy efficiency problem faced by robotic cells. Motivated by the importance of green smart manufacturing, in this study, we innovatively propose to achieve robotic cell energy saving through coordinating the machine production process and robot movement process. Specifically, both machines and the mobile robot can flexibly adjust operating speeds with a V-scale speed framework. Two novel energy-efficient RJSP approaches (i.e. the RJSP-E and the RJSP-EM) are thus proposed. The RJSP-E focuses on minimising energy consumption, while the RJSP-EM simultaneously considers makespan (i.e. productivity) and energy consumption. Through computational experiments, the RJSP-E demonstrates superior performances in reducing energy consumption (15% on average), at a loss of productivity (20% on average). On the other hand, the RJSP-EM can select the most suitable energy-saving operating speeds without much sacrifice in productivity. Notably, the RJSP-EM can reduce energy consumption by a mean of 10% even without increasing makespan. The RJSP-EM also demonstrates higher solution efficiency.

KEYWORDS:

• Green production
• smart manufacturing
• robotic job-shop scheduling
• energy saving
• mixed integer linear programming

Acknowledgements

This work was supported by a grant from the Research Committee of The Hong Kong Polytechnic University under the Project ID P0036042, and under the Account Code RH8P.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article and the Supplementary Online Appendix.

Notes

1 Details are at: https://www.grandviewresearch.com/industry-analysis/autonomous-mobile-robots-market#:∼:text=The%20global%20autonomous%20mobile%20robots%20market%20size%20was,manufacturing%20and%20distribution%20facilities%20without%20any%20manual%20intervention

2 In a robotic cell, the energy consumed is generally electricity. In this study, we use ‘energy’ and ‘electricity’ interchangeably.

3 A robot full-blocking refers to the situation that the robot must wait at the machine for the whole operation process to deliver the same job (as instructed by the optimal schedule that minimizes the makespan while avoiding deadlocks).

4 The robotic job-shop scheduling problem (RJSP) aims to identify the optimal production schedule for machines and the optimal delivery route for robots, while the makespan is usually minimized to improve productivity (Brucker, Burke, and Groenemeyer 2012).

5 A unit distance is the distance moved in a minute of the robot.

6 Due to the word limit imposed by the journal, the numerical illustration example is moved to online appendix.

Additional information

Funding

This work was supported by Research Committee, The Hong Kong Polytechnic University: [Grant Number P0036042, RH8P].

Notes on contributors

Xin Wen

Xin Wen is currently a Research Assistant Professor in Department of Industrial and Systems Engineering at The Hong Kong Polytechnic University. She has published in journals such as IEEE Transactions on Systems, Man, and Cybernetics – Systems, International Journal of Production Research, International Journal of Production Economics, and Transportation Research – Part E. Her current research interest is on transportation and logistics engineering.

Yige Sun

Yige Sun is currently a PhD student in Department of Industrial and Systems Engineering at The Hong Kong Polytechnic University. She has published in journals such as International Journal of Production Research, Transportation Research – Part E, and Journal of Air Transport Management. Her current research interest is on production scheduling optimisation.

Hoi-Lam Ma

Hoi-Lam Ma, PhD, is an Assistant Professor in Supply Chain and Information Management at The Hang Seng University of Hong Kong. She received her PhD degree from The Hong Kong Polytechnic University. Her research interests are in airline operations, container terminal operations, logistics and supply chain management, and scheduling optimisation.

Sai-Ho Chung

Sai-Ho Chung (Nick), PhD, is an Associate Head and Associate Professor in the Industrial and Systems Engineering at the Hong Kong Polytechnic University. He obtained his PhD degree from the University of Hong Kong. His research interests include operations research in logistics and supply chain management, aviation operations, production operations, container terminal operations, etc. He has published over 100 SCI journal papers. He is an editorial board member of Transportation Research Part E and severed as guest editor for several SCI journals.