2018
25
3
0
140
http://scientiairanica.sharif.edu/4440.html
10.24200/sci.2017.4440
The strategy of investment in the stock market using modified support vector regression model
2
2
Stock indices forecasting has become a popular research issue in recent years. Although many statistical time series models have been applied in stock indices forecasting, they are limited to certain assumptions. Accordingly, the traditional statistical time series models might not be suitable for forecasting reallife stock indices data. Hence, this paper proposes a novel forecasting model to assist investors in determining a strategy for investments in the stock market. The proposed model is called the modified support vector regression model, which is composed of the correlation coefficient method, the sliding window algorithm and the support vector regression model. The results show that the forecasting accuracy of the proposed model is more stable than the existing models in terms of average and standard deviation of the root mean square error (RMSE) and the mean absolute percentage error (MAPE). Accordingly, the proposed model would be used to assist investors in determining a strategy for investing in stocks.
2

1629
1640


ChihHua
Huang
DaYeh University
Taiwan


FengHua
Yang
Department of International Business Management, DaYeh University
Taiwan


ChienPang
Lee
Department of Maritime Information and Technology, National Kaohsiung Marine University
Taiwan
chien.pang@gmail.com
Correlation Coefficient
Support vector regression model
Hybrid model
Time series data forecasting
Stock indices
http://scientiairanica.sharif.edu/4445.html
10.24200/sci.2017.4445
An application of Genetic Algorithm and PSO in an inventory model for single deteriorating item with variable demand dependent on marketing strategy and displayed stock level
2
2
This paper deals with an inventory model for single deteriorated item considering the impact of marketing decisions and the displaced stock level on the demand. Partial backlogged shortages are allowed. Analyzing the storage capacity of the shop and demand parameters, different scenarios have been investigated. For each scenario, the corresponding problem has been formulated as a nonlinear mixed integer optimization problem and solved by real coded genetic algorithm and particle swarm optimization technique. To illustrate the inventory model, a numerical example has been solved and sensitivity analyses have been done numerically to study the effect of changes of different parameters on the optimal policies.
2

1641
1655


Asoke Kumar
Bhunia
Department of Mathematics, The University of Burdwan, Burdwan713104, India
India
bhuniaak@rediffmail.com


Ali Akbar
Shaikh
School of Engineering and Sciences, Tecnológico de Monterrey, Ave. E. Garza Sada 2501 Sur, C.P. 64849, Monterrey, Nuevo León, México
Mexico
aakbarshaikh@gmail.com


Vinti
Dhaka
Department of Mathematics & Statistics, Banasthali University, Banathali, India
India


Sarala
Pareek
Department of Mathematics & Statistics, Banasthali University, Banathali, India
India


Leopoldo Eduardo
CárdenasBarrón
School of Engineering and Sciences, Tecnológico de Monterrey, Ave. E. Garza Sada 2501 Sur, C.P. 64849, Monterrey, Nuevo León, México
Mexico
Inventory
deteriorating
variable demand
display stock level
Genetic Algorithm
particle swarm optimization
http://scientiairanica.sharif.edu/4449.html
10.24200/sci.2017.4449
Pricing and Lot sizing of a Decaying item under Group Dispatching with Timedependent Demand and Decay Rates
2
2
Determining appropriate inventory and pricing policies is an important issue in scientific and industrial researches. Here an inventory control model of a decaying item with zero lead time is studied. Two mathematical models under different assumptions are developed. In the first model deterioration rate is timedependent and demand rate is price sensitive while in the second model deterioration rate is constant and demand rate is time and pricedependent. Aim of this research is optimizing total cost by deriving decision variables such as dispatch cycle length, order quantity and wholesale price. To optimize the total cost a shipment group dispatching policy is used.
2

1656
1670


Ata Allah
Taleizadeh
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Iran
taleizadeh@ut.ac.ir


Arezu
RasuliBaghban
Department of Industrial Engineering, Islamic, Azad University, South Tehran Branch
Iran
st_a_rasouli@azad.ac.ir
Economic order quantity
Economic lot sizing
Pricing
Inventory
Deterioration
Replenishment
Group Dispatching
http://scientiairanica.sharif.edu/4446.html
10.24200/sci.2017.4446
An intuitionistic fuzzy OWATOPSIS method for collaborative network formation considering matching characteristics
2
2
Collaborative network (CN) as a new emerging paradigm can rapidly answer market demands by effective enterprise collaboration and coordination. Nowadays, it has become a potential solution for different organizations to manage their businesses effectively. Thus, selecting a suitable partner combination is critical to CN success. Matching characteristic is very important for partner combination selection in the CN formation, while it is neglected in the existing research. This paper is to propose a method and model for partner combination selection of CN considering matching utility. Firstly, the matching factors are developed from four aspects, supply capability, goal, culture and technology. And then a hybrid approach is designed to integrate intuitionistic fuzzy Ordered Weighted Averaging (IFOWA) operators into the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) procedure. And matching utility combination method amongst multipartners is advanced to establish the partner combination model. Moreover, a decision support system is applied in a practical enterprise to illustrate the advantage of the proposed method. Finally a sensitivity analysis is conducted to investigate the robustness of solutions ranking to changes in matching factor. The result shows that ranking the solutions for forming CN is relatively sensitive to its matching factor.
2

1671
1687


Tianri
Wang
College of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, P.R. China
China
wangtianri@163.com


Juan
Liu
College of Energy Engineering, Zhejiang University, Hangzhou 310027, P.R. China
China


Jizu
Li
College of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, P.R. China
China


Ye
Xue
College of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, P.R. China
China


Hong
Dai
College of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, P.R. China
China
Collaborative network
Partner combination selection
Matching utility
Intuitionistic fuzzy OWATOPSIS
Multiple attribute decision making
http://scientiairanica.sharif.edu/4443.html
10.24200/sci.2017.4443
On the njob, mmachine permutation flow shop scheduling problems with makespan criterion and rework
2
2
This paper addresses an njob, mmachine permutation flow shop scheduling problem (PFSSP) with unlimited intermediate buffers and rework activities. The concept of rework means that processing of a job on a machine may not meet a predefined quality level through its first process. Thus we have a probabilistic cycle of operations for jobs on different machines which is based on two concepts: (1) a failure probability of a job on a machine and, (2) a descent rate that reduces the processing times for rework phase. In this case, the processing times of jobs on machines become random variables with a known probability distribution. The aim of this paper is to examine possible solution approaches for generating the efficient job sequences with the least potential makespan. A wide range of simulationbased approaches are applied to address the proposed problem. These methods contain mathematical formulation, heuristic algorithms, and metaheuristics. The mechanism of the solution approaches is based on firstly using expected processing times to find a job sequence; then evaluating the obtained job sequences by several simulated trials. Using the oneway ANOVA test, these methods have been compared together, and the results show the superiority of metaheuristics, especially simulated annealing, over the other methods
2

1688
1700


Behrang
Bootaki
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
Iran
behrang.bootaki@gmail.com


Mohammad Mahdi
Paydar
Department of Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran
Iran
paydar@nit.ac.ir
Permutation flow shop
rework
Metaheurictics
Simulation
Oneway ANOVA
http://scientiairanica.sharif.edu/20422.html
10.24200/sci.2018.20422
Designing an economical acceptance sampling plan in the presence of inspection errors based on maxima nomination sampling method
2
2
One of the most useful and effective methods with an extensive application in companies with purpose of examining the quality of the raw material in addition to final products, is acceptance sampling plans. The inspection process is assumed free of errors in most design of acceptance sampling plans. However, this assumption may not be true. In this research, an optimization model for acceptance sampling plan based on the Maxima Nomination Sampling (MNS) method which is developed for single acceptance sampling plan at the presence of inspection errors is presented. What we have accomplished in this article is to propose an economical model which involves two types of inspection errors and investigates the impact of these errors from an economical point of view then it's been compared with the a classical method for proving it 's efficiency. Furthermore, the sensitivity analysis is carried out to analyze the behavior of the MNS scheme optimal solution. The numerical studies indicate that MNS method is always more economical than classical one.
2

1701
1711


M.S.
Fallahnezhad
Department of Industrial Engineering, Yazd University, Yazd, Iran
Iran
fallahnezhad@yazd.ac.ir


E.
Qazvini
Department of Industrial Engineering, Yazd University, Yazd, Iran, Qazvini.elahe@gmail.com. Pejoohesh Street, Safaieh, Yazd, Iran
Iran


M.
Abessi
Department of Industrial Engineering, Yazd University, Yazd, Iran, mabessi@yazd.ac.ir. Pejoohesh Street, Safaieh, Yazd, Iran
Iran
Acceptance sampling plan
Ranked set sampling
Maxima Nomination Sampling
Inspection
Inspection errors
http://scientiairanica.sharif.edu/4442.html
10.24200/sci.2017.4442
A new nondominated sorting genetic algorithm based to the regression line For fuzzy traffic signal optimization problem
2
2
Traffic jam is a daily problem in nearly all major cities in the world and continues to increase with population and economic growth of urban areas. Traffic lights, as one of the key components at intersections, play an important role in control of traffic flow. Hence, study and research on phase synchronization and time optimization of the traffic lights could be an important step to avoid creating congestion and rejection queues in a urban network. Here, we describe the application of NSGAII, a multiobjective evolutionary algorithm, to optimize both vehicle and pedestrian delays in an individual intersection. Results show that parameters found by improved NSGAII can be superior to those defined by a traffic engineer with respect to several objectives, including total queue length of vehicles and pedestrians. In this paper, we improve NSGAII algorithm based to the regression line to find a Paretooptimal solution or a restrictive set of Paretooptimal solutions based on our solution approaches to the problem, named PDNSGA (Nondominated Sorting Genetic Algorithm based on Perpendicular Distance). In this paper, our purpose is to present a solution methodology to obtain all Paretooptimal solutions to optimize traffic signal timing and enable the decisionmakers to evaluate a greater number of alternative solutions. The proposed algorithm has the capability of searching Pareto front of the multiobjective problem domain. Further jobs should be concerned on the signal timing optimization method for the oversaturated coordinated intersections or smallscale road network and realfield applications with the traffic signal controller. The high speed of the proposed algorithm and its quick convergence makes it desirable for large scheduling with a large number of phases. Furthermore, we have used the mean deviation from the ideal point (MDI) measure to compare the performance of the MOGA, PDNSGA, NSGAII, and WBGA by the ANOVA method. It is demonstrated that the our proposed algorithm (PDNSGA) gives better outputs than those of MOGA, NSGAII, and WBGA in traffic signal optimization problem, statistically .
2

1712
1723


H.
Asadi
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Iran
hamed_assadi2000@yahoo.com


R.
Tavakkoli Moghaddam
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Iran


N.
Shahsavari Pour
Department of Industrial Technology and Management, ValieAsr University Of Rafsanjan, Iran
Iran
shahsavari_n@alum.sharif.edu


E.
Najafi
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Iran
traffic signal systems
Genetic Algorithm
vehicle and pedestrian delays
ANOVA
http://scientiairanica.sharif.edu/4448.html
10.24200/sci.2017.4448
Incorporating demand, orders, lead time, and pricing decisions for reducing bullwhip effect in supply chains
2
2
The purpose of this paper is to mitigate bullwhip effect (BWE) in a supply chain (SC). Four main contributions are proposed. The first one is to reduce BWE through considering its multiple causes (demand, pricing, ordering, and lead time) simultaneously. The second one is to model demands, orders, and prices dynamically for reducing BWE. Demand and prices have mutual effect on each other dynamically over time. In other words, a time series model is used in a game theory method for finding the optimal prices in an SC. Moreover, the optimal prices are inserted into the time series model for forecasting price sensitive demands and orders in an SC. The third one is to use demand of each entity for forecasting its orders. This leads to drastic reduction in BWE and mean square error (MSE) of the model. The fourth contribution is to use optimal prices instead of forecasted ones for demand forecasting and reducing BWE. Finally, a numerical experiment for the auto parts SC is developed. The results show that analysing joint demand, orders, lead time, and pricing model with calculating the optimal values of prices and lead times leads to the significant reduction in BWE.
2

1724
1749


R.
Gamasaee
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran, P.O.BOX 158754413
Iran


M.H.
Fazel Zarandi
Department of Industrial engineering, Amirkabir University of Technology, Tehran, Iran
Iran
zarandi@aku.ac.ir
Supply chain
Bullwhip effect
Pricing
demand forecasting
ordering
Game theory
http://scientiairanica.sharif.edu/4447.html
10.24200/sci.2017.4447
A Lagrangian relaxation approach to fuzzy robust multiobjective facility location network design problem
2
2
This study considers a multiobjective combined budget constrained facility location/network design problem (FL/NDP) in which the system uncertainty is considered. The most obvious practical examples of the problem are territorial designing and locating of academies, airline networks, and medical service centers. In order to assure the network reliability versus uncertainty, an efficient robust optimization approach is applied to model the proposed problem. The formulation is minimizing the total expected costs, including, transshipment costs, facility location (FL) costs, fixed cost of road/link utilization as well as minimizing the total penalties of uncovered demand nodes. Then, in order to consider of several system uncertainty, the proposed model is changed to a fuzzy robust model by suitable approaches. An efficient Subgradient based Lagrangian relaxation algorithm is applied. In addition, a practical example is studied. At the following, a series of experiments, including several test problems, is designed and solved to evaluate of the performance of the algorithm. The obtained results emphasize that considering of practical factors (e.g., several uncertainties, system disruptions, and customer satisfaction) in modelling of the problem can lead to significant improvement of the system yield and subsequently more efficient utilization of the established network.
2

1750
1767


Davood
Shishebori
Department of Industrial Engineering, Yazd University, P.C.1684613114, Yazd, Iran
Iran


Abolghasem
Yousefi Babadi
School of Industrial and systems Engineering, University of Tehran, Tehran, Iran
Iran


Zohre
Noormohammadzadeh
Department of Industrial Engineering, Yazd University, P.C.1684613114, Yazd, Iran
Iran
Facility location
Network design
robust optimization
Mixed integer programming
fuzzy
Multiobjective
Sub gradient based Lagrangian relaxation
http://scientiairanica.sharif.edu/4441.html
10.24200/sci.2017.4441
Reliability analysis of 3component mixture of distributions
2
2
This article focuses on studying 3component mixtures of Exponential, Rayleigh, Pareto and Burr TypeXII distributions in relation to reliability analysis. The main purpose of this study is to derive algebraic expressions for different functions of survival time. For these 3component mixture distributions, the cumulative distribution function, hazard rate function, cumulative hazard rate function, reversed hazard rate function, mean residual life function and mean waiting time function are discussed. To study the behavior of different reliability functions, numerical results are presented for fixed values of parameters.
2

1768
1781


M.
Aslam
Department of Mathematics and Statistics, Riphah International University, Islamabad 44000, Pakistan.
Pakistan
m.aslam@riphah.edu.pk


M.
Tahir
Department of Statistics, Government College University, Faisalabad 38000, Pakistan.
Pakistan


Z.
Hussain
Department of Statistics, QuaidiAzam University, Islamabad 44000, Pakistan
Pakistan
zhlangh@yahoo.com
3Component mixture distributions
Reliability analysis
Failure rate function
Mean residual life function
Mean waiting time function