The generalized flexible job shop scheduling problem

Boyer, Vincent; Vallikavungal, Jobish; Rodríguez, Xavier Cantú; Salazar-Aguilar, M. Angélica

doi:10.1016/j.cie.2021.107542

Cited by 18 publications

(7 citation statements)

References 53 publications

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“…Some works modeled FJSP with grey processing time, [21], energy consumption [5] and multi-objective [18]. In 2021, Boyer et al [3] gave a generic model for FJSP in both mixed-integer programming (MIP) form and constraint programming (CP) form. However, these works either did not allow machines to be reconfigurable or did not consider human participation, which forms a Gap between realistic factory environments.…”

Section: Scheduling Problems In Productionmentioning

confidence: 99%

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A Generic Model for the Human and AGV-Aided Reconfigurable Flexible Job Shop Scheduling Problems

Chen,

Luo,

Zeng

et al. 2023

Advances in Transdisciplinary Engineering

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Scheduling tasks for workshops has been a critical problem in modern factories, especially when many countries promote the development of Industry 4.0. Nowadays, many factories are shifting to flexible, intelligent manufacturing systems (IMS) to improve their ability to respond to customers’ demands of variation and individualization. Previous job shop scheduling problems always simplified the production scenarios by ignoring the machines’ reconfiguration abilities and the participation of human and automated guided vehicle systems (AGVS), which are critical parts of IMS. To address these issues, this paper derives a novel mix-integer programming model for highly-flexible job shop scheduling models in human and AGV-aided IMS to minimize the makespan. A dataset is generated to examine the effectiveness of our model. Furthermore, we conducted numerical experiments on the dataset. Through experiments and analysis, we explore the interaction between optimal values and key factors, including processing time and machine numbers.

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Section: Scheduling Problems In Productionmentioning

confidence: 99%

“…Some papers [10,22] developed generic algorithms (GA) to solve FSP and HFS problems. [3] applied a meta-heuristic algorithm based on a greedy randomized search procedure in FJSP and outperformed constraint programming in large-scale instances. Some papers aimed at deriving optimal solutions.…”

Section: Scheduling Problems In Productionmentioning

confidence: 99%

A Generic Model for the Human and AGV-Aided Reconfigurable Flexible Job Shop Scheduling Problems

Chen,

Luo,

Zeng

et al. 2023

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

show abstract

“…where GD r in equation (18) shows the approximation of solutions obtained by algorithm r to the Pareto-optimal solutions. The lower value of GD r shows better convergence of algorithm r and implies solution sets of algorithm r is more approximate and distributed.…”

Section: The Performance Measuresmentioning

confidence: 99%

“…It is well known that the job shop scheduling problem is strongly NP-hard 18 . The intelligent optimization algorithm can efficiently solve this problem, realize the reasonable allocation of resources, and improve the production efficiency.…”

Section: Introductionmentioning

confidence: 99%

Minimizing the Makespan and the Carbon Emissions in Green Flexible Job Shop Scheduling Problem with Learning Effect

Chen

2022

Preprint

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One of the most difficult challenges for modern manufacturing is reducing carbon emissions. This paper focuses on the green scheduling problem in a flexible job shop system, taking into account energy consumption and worker learning effects. With the objectives to simultaneously minimze the makespan and total carbon emissions, the green flexible job shop scheduling problem (GFJSP) is formulated as a mixed integer linear multi-objective optimization model. Then the improved multi-objective sparrow search algorithm (IMOSSA) is developed for the optimal solution. Finally, this paper conducted computational experiments, including the the comparison between IMOSSA and the algortithm of GA and Jaya. The resluts demonstrate that the IMOSSA has a high precision, good convergence and excellent performance to solve the GFJSP in low-carbon manufacturing systems.

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“…It is well known that the job shop scheduling problem is strongly NP-hard 21 . An intelligent optimization algorithm can efficiently solve this problem, achieve the reasonable allocation of resources, and improve production efficiency.…”

Section: Introductionmentioning

confidence: 99%

Minimizing the makespan and carbon emissions in the green flexible job shop scheduling problem with learning effects

Li¹,

Chen²

2023

Sci Rep

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One of the most difficult challenges for modern manufacturing is reducing carbon emissions. This paper focuses on the green scheduling problem in a flexible job shop system, taking into account energy consumption and worker learning effects. With the objective of simultaneously minimizing the makespan and total carbon emissions, the green flexible job shop scheduling problem (GFJSP) is formulated as a mixed integer linear multiobjective optimization model. Then, the improved multiobjective sparrow search algorithm (IMOSSA) is developed to find the optimal solution. Finally, we conduct computational experiments, including a comparison between IMOSSA and the nondominated sorting genetic algorithm II (NSGA-II), Jaya and the mixed integer linear programming (MILP) solver of CPLEX. The results demonstrate that IMOSSA has high precision, good convergence and excellent performance in solving the GFJSP in low-carbon manufacturing systems.

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The generalized flexible job shop scheduling problem

Cited by 18 publications

References 53 publications

A Generic Model for the Human and AGV-Aided Reconfigurable Flexible Job Shop Scheduling Problems

A Generic Model for the Human and AGV-Aided Reconfigurable Flexible Job Shop Scheduling Problems

Minimizing the Makespan and the Carbon Emissions in Green Flexible Job Shop Scheduling Problem with Learning Effect

Minimizing the makespan and carbon emissions in the green flexible job shop scheduling problem with learning effects

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