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Application of Neural Network Models for Mathematical Programming Problems: A State of Art Review

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Abstract

Artificial neural networks or neural networks (NN) are new computational models based on the working of biological neurons of human body. A NN model consists of an interactive system through which external or internal information flows. Nowadays, NN models are being used to deal with complex real problems. On the other hand, mathematical programming problems (MPPs) are a particular class of optimization problems with mathematical structure of objective function(s) and set of constraints. Use of NN models in solving MPPs is a complex area of research and researchers have tried to contribute to apply NN models on different mathematical programming problems. This paper describes classification of MPPs, different neural network models and the detailed literature review on application of NN models for solving different MPPs along with comprehensive analysis on references. Some new research issues and scopes are also discussed on the use of different NN models on MPPs. This paper aims to present the state of art literature review on the use of NNs for solving MPPs with constructive analysis to elaborate future research scope and new directions in this area for future researchers.

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  1. Department of Applied Science, National Institute of Technical Teachers Training and Research, Chandigarh, 160 019, India

    Kailash Lachhwani

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Lachhwani, K. Application of Neural Network Models for Mathematical Programming Problems: A State of Art Review. Arch Computat Methods Eng 27, 171–182 (2020). https://doi.org/10.1007/s11831-018-09309-5

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