scholarly journals Towards Energy Efficient Scheduling and Rescheduling for Dynamic Flexible Job Shop Problem

2018 ◽  
Vol 51 (11) ◽  
pp. 1275-1280 ◽  
Author(s):  
M. Nouiri ◽  
A. Bekrar ◽  
D. Trentesaux
2018 ◽  
Vol 11 (1) ◽  
pp. 179 ◽  
Author(s):  
Zhongwei Zhang ◽  
Lihui Wu ◽  
Tao Peng ◽  
Shun Jia

Nowadays, manufacturing industry is under increasing pressure to save energy and reduce emissions, and thereby enhancing the energy efficiency of the machining system (MS) through operational methods on the system-level has attracted more attention. Energy-efficient scheduling (ES) has proved to be a typical measure suitable for all shop types, and an energy-efficient mechanism that a machine can be switched off and back on if it waits for a new job for a relatively long period is another proven effective energy-saving measure. Furthermore, their combination has been fully investigated in a single machine, flow shop and job shop, and the improvement in energy efficiency is significant compared with only applying ES for MS. However, whether such two energy-saving measures can be integrated in a flexible job shop environment is a gap in the existing study. To address this, a scheduling method applying an energy-efficient mechanism is proposed for a flexible job shop environment and the corresponding mathematical model, namely the energy-efficient flexible job shop scheduling (EFJSS) model, considering total production energy consumption (EC) and makespan is formulated. Besides, transportation as well as its impact on EC is taken into account in this model for practical application. Furthermore, a solution approach based on the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted, which can avoid the interference of subjective factors and help select a suitable machine for each operation and undertake rational operation sequencing simultaneously. Moreover, experimental results confirm the validity of the improved energy-efficient scheduling approach in a flexible job shop environment and the effectiveness of the solution.


2012 ◽  
Vol 9 ◽  
pp. 2020-2023 ◽  
Author(s):  
Wojciech Bożejko ◽  
Zdziław Hejducki ◽  
Mariusz Uchroński ◽  
Mieczysław Wodecki

2021 ◽  
Vol 13 (23) ◽  
pp. 13016
Author(s):  
Rami Naimi ◽  
Maroua Nouiri ◽  
Olivier Cardin

The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance.


Sign in / Sign up

Export Citation Format

Share Document