دانشگاه آزاد اسلامی واحد لاهیجان
Journal of Operational Research In Its Applications ( Applied Mathematics ) - Lahijan Azad University
2251-7286
2251-9807
16
3
2019
10
1
An efficient modified neural network for solving nonlinear programming problems with hybrid constraints
1
20
FA
A.
Nazemi
Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran
N
S.
Sukhtsaraee
Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran
N
M.
Mortezaee
Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran
Y
This paper presents the optimization techniques for solving convex programming problems with hybrid constraints. According to the saddle point theorem, optimization theory, convex analysis theory, Lyapunov stability theory and LaSalleinvariance principle, a neural network model is constructed. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed network model is stable in the Lyapunov sense and it is globally convergent to an exact optimal solution of the original problem. Several practical examples are provided to show the feasibility and the efficiency of the method.
Convex programming, Lyapunov stability, Hybrid constraints, Neural network, Globally convergent
http://jamlu.liau.ac.ir/article-1-1632-en.html
http://jamlu.liau.ac.ir/article-1-1632-en.pdf
دانشگاه آزاد اسلامی واحد لاهیجان
Journal of Operational Research In Its Applications ( Applied Mathematics ) - Lahijan Azad University
2251-7286
2251-9807
16
3
2019
10
1
Developing a multi objective possibilistic programming model for portfolio selection problem
21
36
FA
M.
Farrokh
Department of Operation Management, Kharazmi University, Tehran, Iran
Y
M. M.
Fallah
University of Yazd
N
Portfolio selection problem is one of the most important issues in the area of financial management in which is attempted to allocate wealth to different assets with controlling the return and risk. The aim of this paper is to obtain the optimum portfolio with regard to the cardinality and threshold constraints. In this paper, a novel multi-objective possibilistic programming model is developed for considering the fuzzy return of the portfolio that can maximize mean return and upside risk and minimize the downside risk. Two different approaches are applied for converting the model to a single objective one. The performance of the proposed model was evaluated by using historical data introduced by Markowitz and data of Tehran Stock Exchange. The results show that the model is able to propose an appropriate portfolio for investors with optimizing the return and risk, simultaneously.
Portfolio, Possibilistic Programming, Multi Objective Programming, Fuzzy Return, Risk.
http://jamlu.liau.ac.ir/article-1-1508-en.html
http://jamlu.liau.ac.ir/article-1-1508-en.pdf
دانشگاه آزاد اسلامی واحد لاهیجان
Journal of Operational Research In Its Applications ( Applied Mathematics ) - Lahijan Azad University
2251-7286
2251-9807
16
3
2019
10
1
Developing Robust Model in a Three-Level Supply Chain with Uncertain and Random Parameters
(Case Study: Thaghdis Porcelain Company)
37
53
FA
F.
Madrdanian Shahri
Department of Industrial Management, Persian Gulf University, Bushehr, Iran
N
G.
Jamali
Department of Industrial Management, Persian Gulf University, Bushehr, Iran
Y
This paper aims to develop a three-objective robust optimization model in Thaghdis Porcelain supply chain. Objectives weighted using AHP technique so the problem converts to a single-objective model. Then the problem turned to the robust optimization model through Bertsimas and Sim (2004) methodology. Research data were Prances and Flat 30-pieces type in a certain period, three supplier source include demand, quality and supplier capacity revealed as robustness sources. Thaghdis supply chain include three levels with 8 suppliers, 6 raw materials (a supplier for 4 materials, 2 suppliers for 1 material and 3 suppliers for 3 materials), a factory and 13 customers. Variables relationships were linear and uncertain parameters were random. Company set a Make To Order (MTO) strategy. The model was solved by LINGO16 software. The results show that the conservatism level increase from 1 to 5, the objective function was worse off, however the quantity of each product increases. So, the uncertainty increases the quantity of products that should be produced.
Mathematical Programming, Robust Optimization, Porcelain Industry, Supply Chain Management
http://jamlu.liau.ac.ir/article-1-1493-en.html
http://jamlu.liau.ac.ir/article-1-1493-en.pdf
دانشگاه آزاد اسلامی واحد لاهیجان
Journal of Operational Research In Its Applications ( Applied Mathematics ) - Lahijan Azad University
2251-7286
2251-9807
16
3
2019
10
1
Some Duality Results in Grey Linear Programming Problem
55
68
FA
D.
Darvishi salokolaei
Department of Mathematics, Payame Noor University, Tehran, Iran
Y
Different approaches are presented to address the uncertainty of data and appropriate description of uncertain parameters of linear programming models. One of them is to use the grey systems theory in modeling such problem. Especially, recently, grey linear programming has attracted many researchers. In this paper, a kind of linear programming with grey coefficients is discussed. Introducing the dual grey linear programming problem, using the practical concept of whitening of grey numbers, some relations between primal and dual problems are presented. Also, the parametric model and dual grey linear programming were compared. The relationships between the primal and dual linear programming solutions in the grey environment are investigated and some of the results are reported with an example.
Interval grey number, Grey linear programming, Dual grey linear programming, Whitenized number, Uncertainty
http://jamlu.liau.ac.ir/article-1-1599-en.html
http://jamlu.liau.ac.ir/article-1-1599-en.pdf
دانشگاه آزاد اسلامی واحد لاهیجان
Journal of Operational Research In Its Applications ( Applied Mathematics ) - Lahijan Azad University
2251-7286
2251-9807
16
3
2019
10
1
The Optimization of Forecasting ATMs Cash Demand of Iran Banking Network Using LSTM Deep Recursive Neural Network
69
88
FA
S.
Poorzaker Arabani
Kashan University, Artificial Intelligent, Kashan, Isfahan, Iran
Y
H.
Ebrahimpour Komleh
Kashan University, Computer department, Kashan, Isfahan, Iran
N
One of the problems of the banking system is cash demand forecasting for ATMs (Automated Teller Machine). The correct prediction can lead to the profitability of the banking system for the following reasons and it will satisfy the customers of this banking system. Accuracy in this prediction are the main goal of this research. If an ATM faces a shortage of cash, it will face the decline of bank popularity and in turn will have some costs; and the bank will encounter decreasing the customers use of these systems. On the other hand, if the bank faces cash trapping at an ATM, regarding to inflation in Iran, it will have a negative impact on bank profitability. The aim of this study is to predict accurately to eliminate the posed double costs. Since the information related to the amount of cash is daily, each ATM will have a behavior as time series; and also because the aim of this study is to predict the demand for cash forecasting from all of the ATMs, we are facing data from the type of panel. The methods that are used for forecasting ATM cash demand in this research include: Forecasting by statistical method, MLP neural network method and LSTM deep recurrent neural network. We will compare the results of these methods and show that LSTM deep recurrent neural network method has the best accuracy in forecasting.
Statistical Forecasting, Intelligent Forecasting, MLP Artificial Neural network, LSTM Deep Recurrent Neural Network, ATM Cash Demand
http://jamlu.liau.ac.ir/article-1-1762-en.html
http://jamlu.liau.ac.ir/article-1-1762-en.pdf
دانشگاه آزاد اسلامی واحد لاهیجان
Journal of Operational Research In Its Applications ( Applied Mathematics ) - Lahijan Azad University
2251-7286
2251-9807
16
3
2019
10
1
The Impact of Forecasting Methods Combination for Reducing Bullwhip Effect in a Four-level Supply Chain under Variable Demand
89
109
FA
M.
Najafi
Department of Industrial Engineering, Payam-e-Noor University, Tehran Shemiranat, Tehran, Iran
N
M.
Daneshmand-Mehr
Department of Industrial Engineering, Islamic Azad University, Lahijan Branch, Lahijan, Iran
Y
R.
Sadeghian
Department of Industrial Engineering, Payam-e-Noor University, Tehran Shemiranat, Tehran, Iran
N
Bullwhip effect in a supply chain, makes inefficiencies such as excess inventory and overdue orders during the chain. These problems can be reduced by appropriate predictions. Forecasting must be done in all levels of a supply chain. This research addresses the problem of optimal combination of forecasting to reduce the bullwhip effect in a four-level supply chain when demand is variable. For this purpose, a four-level supply chain has been considered. Moving average, exponential smoothing, linear regression and multilayer perceptron artificial neural network can be considered for predicting in each level. First, the desired supply chain is simulated for this means. The different combinations of aforementioned forecasting methods are calculated. Then a combination of forecasting methods which minimizes bullwhip effects is selected. Finally, the results are analyzed by variance analysis model. One combination has the lowest bullwhip effects. Moving average, neural networks, exponential smoothing and linear regression are determined for levels: retailer, wholesaler, manufacturer and supplier respectively. Other combinations have less utility
Variable Demand, Forecasting Methods, Four-level Supply Chain, Order Point System, Bullwhip Effect
http://jamlu.liau.ac.ir/article-1-1513-en.html
http://jamlu.liau.ac.ir/article-1-1513-en.pdf
دانشگاه آزاد اسلامی واحد لاهیجان
Journal of Operational Research In Its Applications ( Applied Mathematics ) - Lahijan Azad University
2251-7286
2251-9807
16
3
2019
10
1
A Grey Transportation Problem in Fuzzy Environment
111
122
EN
H.
Nasseri
Department of Mathematics, University of Mazandaran, Babolsar, Iran
Y
B.
Khabiri
Department of Mathematics, University of Mazandaran, Babolsar, Iran
N
In classical transport models, it is always assumed that parameters such as the distance from each supply node to each demand node or the cost of transferring goods from one node to another, as well as quantities such as supply and demand are definite and definite amounts. But in real matters, considering these assumptions is not logical. On the other hand, there may be a real problem in mathematical modeling with a variety of ambiguities in data simultaneously. In this paper, we consider a transport problem in which two types of fuzzy and grey data appear simultaneously. In the model under study, it is assumed that the coefficients of the grey objective function and the fuzzy supply and demand quantities are assumed. In this paper, we prove that by whitening grey numbers and defuzzification of the fuzzy numbers, the original problem can be turned into a crisp transport problem. Finally, with a numerical example, we describe the proposed method.
Transportation Problem, Grey Numbers, Fuzzy Numbers, Modeling, uncertainty, Whitening
http://jamlu.liau.ac.ir/article-1-1371-en.html
http://jamlu.liau.ac.ir/article-1-1371-en.pdf
دانشگاه آزاد اسلامی واحد لاهیجان
Journal of Operational Research In Its Applications ( Applied Mathematics ) - Lahijan Azad University
2251-7286
2251-9807
16
3
2019
10
1
Optimizing of an Integrated Production-Distribution System with Probabilistic Parameters in a Multi-Level Supply Chain Network Considering the Backorder
123
145
FA
H.
Vanaei
Industrial management, Islamic Azad University, UAE, Dobay, UAE.
N
M.
Sharifi
Department of Industrial Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran
Y
R.
Radfar
Department of Management & Economy, Islamic Azad University, Science & research of Tehran, Tehran, Iran
N
F.
Hosseinzadeh Lotfi
Department of Basic sciences, Islamic Azad University, Science & Research Branch, Tehran, Iran
N
A.
Toloei Ashlaghi
Department of Management & Economy, Islamic Azad University, Science & research Branch, Tehran, Iran
N
One of the main arguments in the supply chain is integrated production-distribution planning. Integrated production and distribution of products in a supply chain plays an important role in reducing the costs of the chain. In this paper, a mathematical model for the integrated production-distribution problem in a three-level supply chain, including manufacturing plants, distribution centers and customers with the several product types and over several time periods is provided. The model objective is to minimize the total costs of the supply chain according to the exiting capacities. To consider the uncertainties involved in real problems, in the investigated problem in this study, some parameters such as costs using the Markowitz model is assumed non-deterministic. Finally the model with probabilistic parameters using the genetic algorithm will be solved. The initial model is linear, but after becoming deterministic parameters to the stochastic parameters, the model will be nonlinear. In this study, to randomize the model parameters, first by considering a normal distribution with certain mean and variance, each of the existing costs became a stochastic function. Finally, using the Markowitz model, the deterministic model with definite parameters will become a stochastic model.
Supply Chain, Integrated Production-Distribution, Markowitz Model, Genetic Algorithms
http://jamlu.liau.ac.ir/article-1-1773-en.html
http://jamlu.liau.ac.ir/article-1-1773-en.pdf