Infinitely repeated game based real-time scheduling for low-carbon flexible job shop considering multi-time periods

Wang, Jin; Yang, Jiahao; Zhang, Yingfeng; Ren, Shengnan; Liu, Yang

doi:10.1016/j.jclepro.2019.119093

Cited by 28 publications

(10 citation statements)

References 82 publications

(74 reference statements)

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“…Also, it reschedules after a machine failure without the incorporation of early failure detection or prevention of rescheduling due to other disturbances. A similar lack occurs in other GT-based approaches, such as [43]. Also, in [44], a GT-based approach for self-optimization and learning of modular production units is presented.…”

Section: Literature Reviewmentioning

confidence: 73%

Machine learning and optimization for production rescheduling in Industry 4.0

Carabelli

Fadda

et al. 2020

Int J Adv Manuf Technol

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Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made under uncertainty, it is still a complicated task to decide whether a rescheduling is worthwhile, which is often addressed in practice on a greedy basis. To find a tradeoff between rescheduling frequency and the growing accumulation of delays, we propose a rescheduling framework, which integrates machine learning (ML) techniques and optimization algorithms. To prove the effectiveness, we first model a flexible job-shop scheduling problem with sequence-dependent setup and limited dual resources (FJSP) inspired by an industrial application. Then, we solve the scheduling problem through a hybrid metaheuristic approach. We train the ML classification model for identifying rescheduling patterns. Finally, we compare its rescheduling performance with periodical rescheduling approaches. Through observing the simulation results, we find the integration of these techniques can provide a good compromise between rescheduling frequency and scheduling delays. The main contributions of the work are the formalization of the FJSP problem, the development of ad hoc solution methods, and the proposal/validation of an innovative ML and optimization-based framework for supporting rescheduling decisions.

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Section: Literature Reviewmentioning

confidence: 73%

Machine learning and optimization for production rescheduling in Industry 4.0

Carabelli

Fadda

et al. 2020

Int J Adv Manuf Technol

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“…Firms use backward and forward scheduling to allocate plants and resources, plan production processes, and purchase materials [27][28][29]. In addition, the benefits of production scheduling include the following: inventory reduction, leveling [30][31][32], increased production efficiency [33][34][35], accurate delivery date quotes [36][37][38], and real-time information [39][40][41][42][43]. "Manufacturing model" specifies the machine(s) or resource configuration used in the production process.…”

Section: Schedulingmentioning

confidence: 99%

Scheduling by NSGA-II: Review and Bibliometric Analysis

et al. 2022

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NSGA-II is an evolutionary multi-objective optimization algorithm that has been applied to a wide variety of search and optimization problems since its publication in 2000. This study presents a review and bibliometric analysis of numerous NSGA-II adaptations in addressing scheduling problems. This paper is divided into two parts. The first part discusses the main ideas of scheduling and different evolutionary computation methods for scheduling and provides a review of different scheduling problems, such as production and personnel scheduling. Moreover, a brief comparison of different evolutionary multi-objective optimization algorithms is provided, followed by a summary of state-of-the-art works on the application of NSGA-II in scheduling. The next part presents a detailed bibliometric analysis focusing on NSGA-II for scheduling applications obtained from the Scopus and Web of Science (WoS) databases based on keyword and network analyses that were conducted to identify the most interesting subject fields. Additionally, several criteria are recognized which may advise scholars to find key gaps in the field and develop new approaches in future works. The final sections present a summary and aims for future studies, along with conclusions and a discussion.

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“…Over 94% of the solution approaches proposed for multi-objective EEJSPs are metaheuristic. The remaining ones propose game theory [12,136,137], simulation [28,[138][139][140][141], multi-agent systems [17], constraint programming [142,143], MIP-based heuristics [144,145], and deep learning methods [124].…”

Section: Multi-objective Algorithmsmentioning

confidence: 99%

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

2022

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Energy efficiency has become a major concern for manufacturing companies not only due to environmental concerns and stringent regulations, but also due to large and incremental energy costs. Energy-efficient scheduling can be effective at improving energy efficiency and thus reducing energy consumption and associated costs, as well as pollutant emissions. This work reviews recent literature on energy-efficient scheduling in job shop manufacturing systems, with a particular focus on metaheuristics. We review 172 papers published between 2013 and 2022, by analyzing the shop floor type, the energy efficiency strategy, the objective function(s), the newly added problem feature(s), and the solution approach(es). We also report on the existing data sets and make them available to the research community. The paper is concluded by pointing out potential directions for future research, namely developing integrated scheduling approaches for interconnected problems, fast metaheuristic methods to respond to dynamic scheduling problems, and hybrid metaheuristic and big data methods for cyber-physical production systems.

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Infinitely repeated game based real-time scheduling for low-carbon flexible job shop considering multi-time periods

Cited by 28 publications

References 82 publications

Machine learning and optimization for production rescheduling in Industry 4.0

Machine learning and optimization for production rescheduling in Industry 4.0

Scheduling by NSGA-II: Review and Bibliometric Analysis

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

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