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1 [CT158]

[CT158]

Session Time & Room

CT158 (1/1) : 3C @D501 [Chair: Andrea Pizzuti]

Classification

CT158 (1/1) : Operations research, mathematical programming (90-)

[01182] Optimizing the manufacturing process of a cutting machine in iron industry

Session Time & Room : 3C (Aug.23, 13:20-15:00) @D501
Type : Contributed Talk
Abstract : In this talk we discuss a multi-criteria optimization framework designed for the cooperation with a prominent company, leader in the production of automatic machines employed in iron manufacturing. The optimized machine process counts several steps, starting by cutting the bars at specified lengths and moving them into two temporary buffers. Afterwards, bars are relocated through pliers to parallel depots through a lengthwise movement and gathered by order to facilitate the subsequent transfer to downstream steps.
Classification : 90-08, 90-10, 90-04
Format : Talk at Waseda University
Author(s) :
- Andrea Pizzuti (Università Politecnica delle Marche)
- Fabrizio Marinelli (Università Politecnica delle Marche)

[00765] V-KEMS: Tackling industrial and COVID problems via virtual study groups

Session Time & Room : 3C (Aug.23, 13:20-15:00) @D501
Type : Industrial Contributed Talk
Abstract : During the pandemic a group of UK mathematicians formed the Virtual Forum for Knowledge Exchange in the Mathematical Sciences (V-KEMS). This ran many online virtual study groups (VSGs), using mathematics to tackle urgent societal challenges. These varied from keeping shops, workplaces, and universities safe, to advising the transport, healthcare, and leisure industries. VSGs were so effective that they informed government policy. My talk will describe how VSGs work, and plans for their future development.
Classification : 90-10, 35Mxx, 34Fxx
Format : Talk at Waseda University
Author(s) :
- Chris Budd (University of Bath)

[00954] Dynamic Modeling and Optimization of Mixed Hydrogen-Natural Gas Flow in Pipeline Networks

Session Time & Room : 3C (Aug.23, 13:20-15:00) @D501
Type : Contributed Talk
Abstract : We present a dynamic model for the mixing and transport of hydrogen-natural gas blends in a pipeline network. The dynamic model is derived by lumping the partial differential equations to yield a differential algebraic system. The derived system accommodates spatio-temporally heterogeneous gas injections, and is more complex and numerically ill-conditioned than the case of a single gas. Multiple reformulations for the nonlinear and non-smooth equations of mixing are compared using standard optimization solvers.
Classification : 90-10, 37N40, 90C30, Dynamic and Nonlinear Programming
Format : Talk at Waseda University
Author(s) :
- Saif Kazi (Los Alamos National Laboratory)
- Anatoly Zlotnik (Los Alamos National Laboratory)
- Kaarthik Sundar (Los Alamos National Laboratory)
- Shriram Srinivasan (Los Alamos National Laboratory)

[02512] Incentive design for electric vehicles charging station management

Session Time & Room : 3C (Aug.23, 13:20-15:00) @D501
Type : Contributed Talk
Abstract : We propose a new bilevel optimization model to determine optimal pricing of Electric Vehicle (EV) changing stations. The goal is to define incentives to decrease the energy grid peaks while integrating the behaviour of the EV users through preference lists. The bilevel optimization model is reformulated as a single level one based on a rank pricing approach. The model is solved through a cutting plane approach. Numerical results are discussed
Classification : 90-10, 90c11
Format : Talk at Waseda University
Author(s) :
- Luce Brotcorne (INRIA)
- Luce Brotcorne (INRIA)
- Miguel Anjos (University of Edinburgh)
- Gaël Guillot (INRIA)

[02468] Intuitionistic fuzzy proximal twin svm with fuzzy hyperplane

Session Time & Room : 3C (Aug.23, 13:20-15:00) @D501
Type : Contributed Talk
Abstract : Twin support vector machine (TWSVM) is a contemporary machine learning technique for classification and regression problems. However, TWSVM is sensitive to noises as it ignores the positioning of the input data samples and hence fails to distinguish between support vector and noises. To overcome this issue, we propose a novel Intuitionistic fuzzy proximal twin svm with fuzzy hyperplane (IFTPSVM-FH). Instead of addressing two quadratic programming problems like in TWSVM, two non-parallel classifiers are obtained by solving two systems of linear equations which makes the model more efficient. The two major features of the proposed approach are that it gives an intuitionistic fuzzy number based on the relevance to each data vector and that the parameters for the hyperplane, such as the components of the normal vector and the bias term, are fuzzified variables. With the use of fuzzy variables, the proposed fuzzy hyperplane effectively captures the ambiguous character of real-world categorization tasks by representing vagueness in the training data. The proposed approach uses local neighbourhood information among the data points and also uses both membership and non-membership weights to reduce the effect of noise and outliers. By incorporating nonlinear kernel functions into the feature space, the method can be used to detect complex patterns or non-linearity in the dataset. We have applied our method on real-world classification tasks and concluded that it performs incredibly well in comparison to other approaches. In order to demonstrate the practical application of the proposed model, we use it for the predict the trends of the stock market.
Classification : 90-08, 90C30, 90C25
Format : Online Talk on Zoom
Author(s) :
- Yash Arora (IIT Roorkee)
- Shiv Kumar Gupta (IIT Roorkee)