A Pareto-based two-stage evolutionary algorithm for flexible job shop scheduling problem with worker cooperation flexibility

Luo, Qiang; Deng, Qianwang; Xie, Guanhua; Gong, Guiliang

doi:10.1016/j.rcim.2023.102534

Cited by 27 publications

(5 citation statements)

References 56 publications

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“…In particular, NSGA-II [10] and NSGA-III [23] are two evolutionary algorithms that have shown promising results for the SFJSSP. NSGA-II is often combined with other methods like local search and meta-heuristics to improve it [24,25]. NSGA-III, which differs from NSGA-II by its selection mechanisms, has also shown good results [5].…”

Section: Resolution Methods For Multi-objective Problemsmentioning

confidence: 99%

NSGA-II for Solving a Multi-objective, Sustainable and Flexible Job Shop Scheduling Problem

Destouet,

Tlahig,

Bettayeb

et al. 2023

IFIP Advances in Information and Communication Technology

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Recently, sustainable scheduling has emerged to make the trade-off between economic, environmental and social factors. While classical scheduling research focuses on economic and environmental indicators such as makespan and energy consumption, modern human-centred manufacturing systems consider additional important factors such as workers' well-being and ergonomic risks. This study proposes a multiobjective mathematical model that jointly optimizes the makespan, the total energy consumption, and the OCRA (Occupational Repetitive Actions) index (a measure of ergonomic risk) in the case of a flexible job shop. The model has the originality of also taking into account the travel times of operators and products between machines. We use the NSGA-II method to solve it. The first results allow us to analyze the mutual influences of the criteria and demonstrate the advantage of such a method to obtain a good compromise between the three objectives in a reasonable resolution time.

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Section: Resolution Methods For Multi-objective Problemsmentioning

confidence: 99%

NSGA-II for Solving a Multi-objective, Sustainable and Flexible Job Shop Scheduling Problem

Destouet,

Tlahig,

Bettayeb

et al. 2023

IFIP Advances in Information and Communication Technology

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“…2 Cases produced by random seeds on the same exponential distribution. 3 The makespans solved under robot breakdowns.…”

Section: Generalization To Robot Breakdownsmentioning

confidence: 99%

“…Based on the thorough survey of [2] and papers related to JSP published after 2022 (e.g., [3][4][5][6][7][8][9][10]), we classified the works related to FJSP into 23 categories by the factors considered (shown in Table 1), such as machine availability, routing, varied transportation time, etc. It can be concluded that there is a lack of FJSP studies that take all the characteristics above into account.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Reinforcement Learning for Extended Flexible Job Shop Scheduling

Peng,

Xiong,

Yang

et al. 2023

Machines

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An extended flexible job scheduling problem is presented with characteristics of technology and path flexibility (dual flexibility), varied transportation time, and an uncertain environment. The scheduling can greatly increase efficiency and security in complex scenarios, e.g., distributed vehicle manufacturing, and multiple aircraft maintenance. However, optimizing the scheduling puts forward higher requirements on accuracy, real time, and generalization, while subject to the curse of dimension and usually incomplete information. Various coupling relations among operations, stations, and resources aggravate the problem. To deal with the above challenges, we propose a multi-agent reinforcement learning algorithm where the scheduling environment is modeled as a decentralized partially observable Markov decision process. Each job is regarded as an agent that decides the next triplet, i.e., operation, station, and employed resource. This paper is novel in addressing the flexible job shop scheduling problem with dual flexibility and varied transportation time in consideration and proposing a double Q-value mixing (DQMIX) optimization algorithm under a multi-agent reinforcement learning framework. The experiments of our case study show that the DQMIX algorithm outperforms existing multi-agent reinforcement learning algorithms in terms of solution accuracy, stability, and generalization. In addition, it achieves better solution quality for larger-scale cases than traditional intelligent optimization algorithms.

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“…In this transformative context, where the goal is to fully exploit the capabilities of machines, the precise planning of all production tasks becomes imperative, aiming to minimize machine idle time. Consequently, the scheduling problem has been the subject of extensive study in recent decades [3][4][5]. In its simplest form, this scheduling challenge is commonly referred to as the job shop scheduling (JSS) problem [6].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems

Gaiardelli,

Carra,

Spellini

et al. 2024

Applied Sciences

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Efficiently managing resource utilization is critical in manufacturing systems to optimize production efficiency, especially in dynamic environments where jobs continually enter the system and machine breakdowns are potential occurrences. In fully automated environments, co-ordinating the transport system with other resources is paramount for smooth operations. Despite extensive research exploring the impact of job characteristics, such as fixed or variable task-processing times and job arrival rates, the role of the transport system has been relatively underexplored. This paper specifically addresses the utilization of a conveyor belt as the primary mode of transportation among a set of production machines. In this configuration, no input or output buffers exist at the machines, and the transport times are contingent on machine availability. In order to tackle this challenge, we introduce a randomized heuristic approach designed to swiftly identify a near-optimal joint schedule for job processing and transfer. Our solution has undergone testing on both state-of-the-art benchmarks and real-world instances, showcasing its ability to accurately predict the overall processing time of a production line. With respect to our previous work, we specifically consider the case of the arrival of a dynamic job, which requires a different design approach since there is a need to keep track of partially processed jobs, jobs that are waiting, and newly arrived jobs. We adopt a total rescheduling strategy and, in order to show its performance, we consider a clairvoyant scheduling approach, in which job arrivals are known in advance. We show that the total rescheduling strategy yields a scheduling solution that is close to optimal.

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A Pareto-based two-stage evolutionary algorithm for flexible job shop scheduling problem with worker cooperation flexibility

Cited by 27 publications

References 56 publications

NSGA-II for Solving a Multi-objective, Sustainable and Flexible Job Shop Scheduling Problem

NSGA-II for Solving a Multi-objective, Sustainable and Flexible Job Shop Scheduling Problem

Multi-Agent Reinforcement Learning for Extended Flexible Job Shop Scheduling

Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems

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