Do you want to publish a course? Click here

Interactive Cutting-Plane Method for Multiobjective Programming with Fuzzy Parameters

طريقة المستوي القاطع التفاعلية للبرمجة المتعددة الأهداف بوسائط ضبابية

1137   0   11   0 ( 0 )
 Publication date 2013
and research's language is العربية
 Created by Shamra Editor




Ask ChatGPT about the research

This paper presents an interactive solution method for treating multi objective mathematical programming problems with fuzzy parameters in the objective functions and in the constraints. Theses fuzzy parameters are characterized by fuzzy numbers. For such problems, the concept of a-Pareto optimality introduced by extending the ordinary Pareto optimality based on the a-level sets of fuzzy numbers. The proposed solution method is based on cutting planes, which are based on local trade off ratios between the objective functions as prescribed by the decision maker at each iterate generated by the method. An illustrative numerical example is given to clarity this method.

References used
DUBOIS, D.; PRADE, H. Operations on fuzzy numbers. International Journal of Systems Science, 9, 1978, 613-626
DUBOIS, D.; PRADE, H. Fuzzy sets and systems: theory and application. Academic Press, New York, 1980, 393
DYER, J. S. A time sharing computer program for the solution of the multiple criteria problem. Management Science, 19, 1973, 1379-1383
GEOFFRION, A. M.; DYER, J. S.; FEINBERG, A. An interactive approach for multicriterion optimization with an application to the operation of an academic department. Management Science, 19, 1972, 357-368
rate research

Read More

In this paper we offer a new interactive method for solving Multiobjective linear programming problems. This method depends on forming the model for reducing the relative deviations of objective functions from their ideal standard, and dealing with the unsatisfying deviations of objective functions by reacting with decision maker. The results obtained from using this method were compared with many interactive methods as (STEM Method[6] – Improvement STEM Method[7] – Matejas-peric Method[8]). Numerical results indicate that the efficiency of purposed method comparing with the obtained results by using that methods at initial solution point and the other interactive points with decision maker.
Despite the increasingly good quality of Machine Translation (MT) systems, MT outputs require corrections. Automatic Post-Editing (APE) models have been introduced to perform these corrections without human intervention. However, no system has been a ble to fully automate the Post-Editing (PE) process. Moreover, while numerous translation tools, such as Translation Memories (TMs), largely benefit from translators' input, Human-Computer Interaction (HCI) remains limited when it comes to PE. This research-in-progress paper discusses APE models and suggests that they could be improved in more interactive scenarios, as previously done in MT with the creation of Interactive MT (IMT) systems. Based on the hypothesis that PE would benefit from HCI, two methodologies are proposed. Both suggest that traditional batch learning settings are not optimal for PE. Instead, online techniques are recommended to train and update PE models on the fly, via either real or simulated interactions with the translator.
We present a set of assignments for a graduate-level NLP course. Assignments are designed to be interactive, easily gradable, and to give students hands-on experience with several key types of structure (sequences, tags, parse trees, and logical form s), modern neural architectures (LSTMs and Transformers), inference algorithms (dynamic programs and approximate search) and training methods (full and weak supervision). We designed assignments to build incrementally both within each assignment and across assignments, with the goal of enabling students to undertake graduate-level research in NLP by the end of the course.
Interactive machine reading comprehension (iMRC) is machine comprehension tasks where knowledge sources are partially observable. An agent must interact with an environment sequentially to gather necessary knowledge in order to answer a question. We hypothesize that graph representations are good inductive biases, which can serve as an agent's memory mechanism in iMRC tasks. We explore four different categories of graphs that can capture text information at various levels. We describe methods that dynamically build and update these graphs during information gathering, as well as neural models to encode graph representations in RL agents. Extensive experiments on iSQuAD suggest that graph representations can result in significant performance improvements for RL agents.
We have in this research study of the forces that allow the iterative approximation calculation method of eigenvalue as well as the eigenvector associated with it. Also studied the way the reverse repetitive forces that also allow to get closer t o a eigenvector has intrinsic value known approximate. It has also been described QR method which allows calculation of all eigenvalues in an effective manner, then we have created an algorithm for this method.
comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا