Recurrent Neural Network for Bi-level Centralized Resource Allocation DEA Models
Publish place: 9th National Conference on Data Envelopment Analysis
Publish Year: 1396
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
DEA09_121
تاریخ نمایه سازی: 8 آذر 1396
Abstract:
In this paper, the common centralized DEA models are extended to the bi-level centralized resource allocation (CRA) models based on revenue efficiency. Based on the Karush–Kuhn–Tucker (KKT) conditions, the bi-level CRA model is reduced to a one-level mathematical program subject to complementarity constraints (MPCC). A recurrent neural network is developed for solving this one-level mathematical programming problem. Under a proper assumption and utilizing a suitable Lyapunov function, it is shown that the proposed neural network is Lyapunov stable and convergent to an exact optimal solution of the original problem. Finally, an illustrative example is elaborated to substantiate the applicability and effectiveness of the proposed approach.
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Authors
M Moghaddas
Department of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran, Iran; Young Researchers and Elite Club, Central Tehran Branch, Islamic Azad University, Tehran, Iran
G Tohidi
Department of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran, Iran
F Hosseinizadeh Lotfi
Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran