A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Sun, Yuting; Tu, Jiachen; Bragin, Mikhail A.; Zhang, Liang

doi:10.1002/amp2.10141

Cited by 4 publications

(2 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Limited machine capacity brings a layer of difficulty since multiple "individual-job" subproblems are considered together competing for limited resources (machines). Another difficulty arises because of uncertainties, including processing times [34][35][36][37][38][39] and scrap [40][41][42] . Re-manufacturing of one part may affect and disrupt the overall schedule within the entire job shop, thereby leading to unexpectedly high delays in production.…”

Section: Demonstration Of Convergence Of Multipliers Based On a Small...mentioning

confidence: 99%

Surrogate “Level-Based” Lagrangian Relaxation for mixed-integer linear programming

Bragin

Tucker

2022

Sci Rep

View full text Add to dashboard Cite

Mixed-Integer Linear Programming (MILP) plays an important role across a range of scientific disciplines and within areas of strategic importance to society. The MILP problems, however, suffer from combinatorial complexity. Because of integer decision variables, as the problem size increases, the number of possible solutions increases super-linearly thereby leading to a drastic increase in the computational effort. To efficiently solve MILP problems, a “price-based” decomposition and coordination approach is developed to exploit 1. the super-linear reduction of complexity upon the decomposition and 2. the geometric convergence potential inherent to Polyak’s stepsizing formula for the fastest coordination possible to obtain near-optimal solutions in a computationally efficient manner. Unlike all previous methods to set stepsizes heuristically by adjusting hyperparameters, the key novel way to obtain stepsizes is purely decision-based: a novel “auxiliary” constraint satisfaction problem is solved, from which the appropriate stepsizes are inferred. Testing results for large-scale Generalized Assignment Problems demonstrate that for the majority of instances, certifiably optimal solutions are obtained. For stochastic job-shop scheduling as well as for pharmaceutical scheduling, computational results demonstrate the two orders of magnitude speedup as compared to Branch-and-Cut. The new method has a major impact on the efficient resolution of complex Mixed-Integer Programming problems arising within a variety of scientific fields.

show abstract

Section: Demonstration Of Convergence Of Multipliers Based On a Small...mentioning

confidence: 99%

Surrogate “Level-Based” Lagrangian Relaxation for mixed-integer linear programming

Bragin

Tucker

2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Limited machine capacity brings a layer of difficulty since multiple "individual-job" subproblem are considered together competing for limited resources (machines). Another difficulty arises because of uncertainties, including processing times [23][24][25][26][27][28] and scrap [29][30][31].…”

Section: Example 2: Stochastic Job-shop Scheduling With the Considera...mentioning

confidence: 99%

Surrogate "Level-Based" Lagrangian Relaxation for Mixed-Integer Linear Programming

Bragin

Tucker

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Mixed-Integer Linear Programming (MILP) plays an important role across a range of scientific disciplines and within areas of strategic importance to society. The MILP problems, however, suffer from combinatorial complexity. Because of integer decision variables, as the problem size increases, the number of possible solutions increases super-linearly thereby leading to a drastic increase in the computational effort. To efficiently solve MILP problems, a “price-based” decomposition and coordination approach is developed to exploit 1. the super-linear reduction of complexity upon the decomposition and 2. the geometric convergence potential inherent to Polyak’s step-sizing formula for the fastest coordination possible to obtain near-optimal solutions in a computationally efficient manner. Unlike all previous methods to set step-sizes heuristically by adjusting hyper-parameters, the key novel way to obtain step-sizes is purely decision-based: a novel “auxiliary” constraint satisfaction problem is solved, from which the appropriate step-sizes are inferred. Testing results for large-scale Generalized Assignment Problems (GAP) demonstrate that for the majority of instances, certifiably optimal solutions are obtained. For stochastic job-shop scheduling as well as for pharmaceutical scheduling, computational results demonstrate the two orders of magnitude speedup as compared to frequently used Branch-and-Cut (B&C). SLBLR has a major impact on the efficient resolution of complex Mixed-Integer Programming (MIP) problems arising within a variety of scientific fields.

show abstract

Construction of a virtual simulation practical teaching system for intelligent manufacturing under the background of new engineering

Wang,

Liu

2024

Comp Applic In Engineering

View full text Add to dashboard Cite

To adapt to the new direction of engineering education reform in China under the background of new engineering, and to promote the transformation and upgrading of intelligent manufacturing professionals, a virtual simulation practice teaching system based on Internet technology is constructed. Relying on “online + offline” resources, experimental modules are designed, a course resource library is built, a digital cloud platform is developed, and a “One Body, Two Wings” virtual simulation teaching and training system is established. Practice has shown that the virtual simulation system has the advantages of immersion, interaction, imagination, and reality, which can meet the basic needs of users and provide a safe and immersive experimental operation experience. The comparative data on practical effectiveness shows that virtual simulation teaching has significantly improved students' theoretical knowledge, practical skills, and innovative ability, making graduates highly desirable by enterprises. The virtual simulation system not only enhances user experience, but also improves students' learning effect, professional cognitive ability, and professional practice ability. The system provides strong technical support for the cultivation of intelligent manufacturing professionals, and is an efficient and practical tool in the field of teaching and training, with broad application prospects.

show abstract

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Cited by 4 publications

References 55 publications

Surrogate “Level-Based” Lagrangian Relaxation for mixed-integer linear programming

Surrogate “Level-Based” Lagrangian Relaxation for mixed-integer linear programming

Surrogate "Level-Based" Lagrangian Relaxation for Mixed-Integer Linear Programming

Construction of a virtual simulation practical teaching system for intelligent manufacturing under the background of new engineering

Contact Info

Product

Resources

About