scholarly journals Automated Contact and Non-Contact Constraint Generation for Disassembly Feasibility and Planning

Procedia CIRP ◽  
2019 ◽  
Vol 80 ◽  
pp. 548-553
Author(s):  
Header Alrufaifi ◽  
Bubnish Kumar ◽  
Jeremy L. Rickli
Keyword(s):  
Constraint Generation ◽  
Contact Constraint

Application of the Finite Element Method in Screening of Body Turn up Heights for Radial Truck Tires

2001 ◽  
Vol 29 (3) ◽  
pp. 186-196 ◽  
Author(s):  
X. Yan
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Material Model ◽  
The Finite Element Method ◽  
Truck Tires ◽  
Contact Constraint ◽  
Nonlinear Mechanical ◽  
Critical Regions ◽  
Constraint Method ◽  
Element Method

Abstract A method is described to predict relative body turn up endurance of radial truck tires using the finite element method. The elastomers in the tire were simulated by incompressible elements for which the nonlinear mechanical properties were described by the Mooney-Rivlin model. The belt, carcass, and bead were modeled by an equivalent orthotropic material model. The contact constraint of a radial tire structure with a flat foundation and rigid rim was treated using the variable constraint method. Three groups of tires with different body turn up heights under inflation and static footprint loading were analyzed by using the finite element method. Based on the detail analysis for stress analysis parameters in the critical regions in the tires, the relative body turn up edge endurance was predicted.

Single Depth View Based Real-Time Reconstruction of Hand-Object Interactions

2021 ◽  
Vol 40 (3) ◽  
pp. 1-12
Author(s):  
Hao Zhang ◽  
Yuxiao Zhou ◽  
Yifei Tian ◽  
Jun-Hai Yong ◽  
Feng Xu
Keyword(s):  
Real Time ◽  
Synthetic Data ◽  
Real Data ◽  
Depth Image ◽  
Real Time System ◽  
The Real ◽  
Time Performance ◽  
Contact Constraint ◽  
Object Shapes ◽  
Object Interactions

Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.

scholarly journals A column-and-constraint generation algorithm for two-stage stochastic programming problems

Top ◽  
2021 ◽  
Author(s):  
Denise D. Tönissen ◽  
Joachim J. Arts ◽  
Zuo-Jun Max Shen
Keyword(s):  
Stochastic Programming ◽  
Benders Decomposition ◽  
Equivalent Model ◽  
Computational Time ◽  
Two Stage ◽  
Generation Algorithm ◽  
Constraint Generation ◽  
Deterministic Equivalent ◽  
Location Routing ◽  
Relative Tolerance

AbstractThis paper presents a column-and-constraint generation algorithm for two-stage stochastic programming problems. A distinctive feature of the algorithm is that it does not assume fixed recourse and as a consequence the values and dimensions of the recourse matrix can be uncertain. The proposed algorithm contains multi-cut (partial) Benders decomposition and the deterministic equivalent model as special cases and can be used to trade-off computational speed and memory requirements. The algorithm outperforms multi-cut (partial) Benders decomposition in computational time and the deterministic equivalent model in memory requirements for a maintenance location routing problem. In addition, for instances with a large number of scenarios, the algorithm outperforms the deterministic equivalent model in both computational time and memory requirements. Furthermore, we present an adaptive relative tolerance for instances for which the solution time of the master problem is the bottleneck and the slave problems can be solved relatively efficiently. The adaptive relative tolerance is large in early iterations and converges to zero for the final iteration(s) of the algorithm. The combination of this relative adaptive tolerance with the proposed algorithm decreases the computational time of our instances even further.

scholarly journals Oracle-based algorithms for binary two-stage robust optimization

2020 ◽  
Vol 77 (2) ◽  
pp. 539-569
Author(s):  
Nicolas Kämmerling ◽  
Jannis Kurtz
Keyword(s):  
Robust Optimization ◽  
Branch And Bound ◽  
Optimization Problems ◽  
Capital Budgeting ◽  
Hub Location Problem ◽  
Two Stage ◽  
Generation Algorithm ◽  
Constraint Generation ◽  
Deterministic Problem ◽  
Objective Uncertainty

Abstract In this work we study binary two-stage robust optimization problems with objective uncertainty. We present an algorithm to calculate efficiently lower bounds for the binary two-stage robust problem by solving alternately the underlying deterministic problem and an adversarial problem. For the deterministic problem any oracle can be used which returns an optimal solution for every possible scenario. We show that the latter lower bound can be implemented in a branch and bound procedure, where the branching is performed only over the first-stage decision variables. All results even hold for non-linear objective functions which are concave in the uncertain parameters. As an alternative solution method we apply a column-and-constraint generation algorithm to the binary two-stage robust problem with objective uncertainty. We test both algorithms on benchmark instances of the uncapacitated single-allocation hub-location problem and of the capital budgeting problem. Our results show that the branch and bound procedure outperforms the column-and-constraint generation algorithm.

scholarly journals Generalized and Sub-Optimal Bipartite Constraints for Conflict-Based Search

2020 ◽  
Vol 34 (05) ◽  
pp. 7277-7284
Author(s):  
Thayne T. Walker ◽  
Nathan R. Sturtevant ◽  
Ariel Felner
Keyword(s):  
Bipartite Graph ◽  
Continuous Time ◽  
State Of The Art ◽  
Main Idea ◽  
Constraint Generation ◽  
Empirical Results ◽  
Generation Technique ◽  
Unit Costs ◽  
Previous State ◽  
Multi Agent

The main idea of conflict-based search (CBS), a popular, state-of-the-art algorithm for multi-agent pathfinding is to resolve conflicts between agents by systematically adding constraints to agents. Recently, CBS has been adapted for new domains and variants, including non-unit costs and continuous time settings. These adaptations require new types of constraints. This paper introduces a new automatic constraint generation technique called bipartite reduction (BR). BR converts the constraint generation step of CBS to a surrogate bipartite graph problem. The properties of BR guarantee completeness and optimality for CBS. Also, BR's properties may be relaxed to obtain suboptimal solutions. Empirical results show that BR yields significant speedups in 2k connected grids over the previous state-of-the-art for both optimal and suboptimal search.

The IFBMDA: A Model for Mechanical Design Automation

1992 ◽  
Author(s):  
Dongming Lu ◽  
Shouqian Sun ◽  
Zhijun He
Keyword(s):  
Design Process ◽  
Information Flow ◽  
Design Automation ◽  
Flow Model ◽  
Mechanical Design ◽  
Cognitive Model ◽  
Knowledge Processing ◽  
Constraint Generation ◽  
Process Stage ◽  
Information Flow Model

Abstract The IFBMDA is an Information-Flow-Based model for Mechanical Design Automation. This paper first analyzes the mechanical design process from the views of design methodology and cognitive model. Then, two essential assumptions about mechanical design behavior are provided. Based on the analysis and fundamental assumptions, this paper thoroughly describes five submodels which constitute the automation model IFBMDA. They are Information Flow model, Knowledge Processing model, Non-monotonic Expansion Search model, Iterative Constraint Generation and Solution model and Design Process Stage model. Then, this paper also evaluates the model in both practical and theoretical aspects and shows that it is well-developed in both aspects. Finally, the perspective of further mechanical design automation research is outlined.

scholarly journals Robust Day-Ahead Dispatch for Integrated Power-Heat-Gas Microgrid considering Wind Power Uncertainty

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Yang Liu ◽  
Yanli Ye ◽  
Xianbang Chen ◽  
Huaqiang Li ◽  
Yuan Huang
Keyword(s):  
Wind Power ◽  
Deterministic Model ◽  
Economic Dispatch ◽  
Two Stage ◽  
Worst Case ◽  
Constraint Generation ◽  
Second Stage ◽  
Robust Model ◽  
Operation Cost ◽  
Made In

Wind power generation has been widely deployed in the modern power system due to the issues of energy crisis and environment pollution. Meanwhile, the microgrid is gradually regarded as a feasible way to connect and accommodate the distributed wind power generations. Recently, more research studies also focus on incorporating various energy systems, for example, heat and gas into the microgrid in terms of satisfying different types of load demands. However, the uncertainty of wind power significantly impacts the economy of the integrated power-heat-gas microgrid. To deal with this issue, this paper presents a two-stage robust model to achieve the optimal day-ahead economic dispatch strategy considering the worst-case wind power scenarios. The first stage makes the initial day-ahead dispatch decision before the observation of uncertain wind power. The additional adjustment action is made in the second stage once the wind power uncertainty is observed. Based on the duality theory and Big-M approach, the original second-stage problem can be dualized and linearized. Therefore, the column-and-constraint generation algorithm can be further implemented to achieve the optimal day-ahead economic dispatch strategy for the integrated power-heat-gas microgrid. The experimental results indicate the effectiveness of the presented approach for achieving operation cost reduction and promoting wind power utilization. The robustness and the economy of the two-stage robust model can be balanced, of which the performances significantly outperform those of the single-stage robust model and the deterministic model.

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