2020-11-29T18:23:19Z
http://scientiairanica.sharif.edu/?_action=export&rf=summon&issue=1033
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2018
25
3
The strategy of investment in the stock market using modified support vector regression model
Chih-Hua
Huang
Feng-Hua
Yang
Chien-Pang
Lee
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 real-life 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.
Correlation Coefficient
Support vector regression model
Hybrid model
Time series data forecasting
Stock indices
2018
06
01
1629
1640
http://scientiairanica.sharif.edu/article_4440_2329a1e5d86f9b229afb9f05003ece1c.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2018
25
3
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
Asoke Kumar
Bhunia
Ali Akbar
Shaikh
Vinti
Dhaka
Sarala
Pareek
Leopoldo Eduardo
Cárdenas-Barrón
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.
Inventory
deteriorating
variable demand
display stock level
Genetic Algorithm
particle swarm optimization
2018
06
01
1641
1655
http://scientiairanica.sharif.edu/article_4445_90e8a2c39f06482a0cbb7bad0605c322.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2018
25
3
Pricing and Lot sizing of a Decaying item under Group Dispatching with Time-dependent Demand and Decay Rates
Ata Allah
Taleizadeh
Arezu
Rasuli-Baghban
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 time-dependent and demand rate is price sensitive while in the second model deterioration rate is constant and demand rate is time- and price-dependent. 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.
Economic order quantity
Economic lot sizing
Pricing
Inventory
Deterioration
Replenishment
Group Dispatching
2018
06
01
1656
1670
http://scientiairanica.sharif.edu/article_4449_516efb2cb04c9dfd8e04908af1019019.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2018
25
3
An intuitionistic fuzzy OWA-TOPSIS method for collaborative network formation considering matching characteristics
Tianri
Wang
Juan
Liu
Jizu
Li
Ye
Xue
Hong
Dai
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 multi-partners 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.
Collaborative network
Partner combination selection
Matching utility
Intuitionistic fuzzy OWA-TOPSIS
Multiple attribute decision making
2018
06
01
1671
1687
http://scientiairanica.sharif.edu/article_4446_716c8b9893c1263f2abf0a6773865945.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2018
25
3
On the n-job, m-machine permutation flow shop scheduling problems with makespan criterion and rework
Behrang
Bootaki
Mohammad Mahdi
Paydar
This paper addresses an n-job, m-machine 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 simulation-based 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 one-way ANOVA test, these methods have been compared together, and the results show the superiority of metaheuristics, especially simulated annealing, over the other methods
Permutation flow shop
rework
Metaheurictics
Simulation
One-way ANOVA
2018
06
01
1688
1700
http://scientiairanica.sharif.edu/article_4443_920bdb0c90b3cb3b5b736de9e53d5839.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2018
25
3
Designing an economical acceptance sampling plan in the presence of inspection errors based on maxima nomination sampling method
M.S.
Fallahnezhad
E.
Qazvini
M.
Abessi
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.
Acceptance sampling plan
Ranked set sampling
Maxima Nomination Sampling
Inspection
Inspection errors
2018
06
01
1701
1711
http://scientiairanica.sharif.edu/article_20422_e54a3b53d9cf7149c5e0ae3ce17c53a6.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2018
25
3
A new nondominated sorting genetic algorithm based to the regression line For fuzzy traffic signal optimization problem
H.
Asadi
R.
Tavakkoli Moghaddam
N.
Shahsavari Pour
E.
Najafi
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 NSGA-II, a multi-objective evolutionary algorithm, to optimize both vehicle and pedestrian delays in an individual intersection. Results show that parameters found by improved NSGA-II 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 NSGA-II algorithm based to the regression line to find a Pareto-optimal solution or a restrictive set of Pareto-optimal solutions based on our solution approaches to the problem, named PDNSGA (Non-dominated Sorting Genetic Algorithm based on Perpendicular Distance). In this paper, our purpose is to present a solution methodology to obtain all Pareto-optimal solutions to optimize traffic signal timing and enable the decision-makers to evaluate a greater number of alternative solutions. The proposed algorithm has the capability of searching Pareto front of the multi-objective problem domain. Further jobs should be concerned on the signal timing optimization method for the oversaturated coordinated intersections or small-scale road network and real-field 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, NSGA-II, and WBGA by the ANOVA method. It is demonstrated that the our proposed algorithm (PDNSGA) gives better outputs than those of MOGA, NSGA-II, and WBGA in traffic signal optimization problem, statistically .
traffic signal systems
Genetic Algorithm
vehicle and pedestrian delays
ANOVA
2018
06
01
1712
1723
http://scientiairanica.sharif.edu/article_4442_e75c1431a9fcff5150839be1610229c7.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2018
25
3
Incorporating demand, orders, lead time, and pricing decisions for reducing bullwhip effect in supply chains
R.
Gamasaee
M.H.
Fazel Zarandi
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.
Supply chain
Bullwhip effect
Pricing
demand forecasting
ordering
Game theory
2018
06
01
1724
1749
http://scientiairanica.sharif.edu/article_4448_8fed9aca273919b302a893d65ee91941.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2018
25
3
A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem
Davood
Shishebori
Abolghasem
Yousefi Babadi
Zohre
Noormohammadzadeh
This study considers a multi-objective 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 Sub-gradient 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.
Facility location
Network design
robust optimization
Mixed integer programming
fuzzy
Multi-objective
Sub- gradient based Lagrangian relaxation
2018
06
01
1750
1767
http://scientiairanica.sharif.edu/article_4447_dc93de4ed08928a49de89ce6c8694e1b.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2018
25
3
Reliability analysis of 3-component mixture of distributions
M.
Aslam
M.
Tahir
Z.
Hussain
This article focuses on studying 3-component mixtures of Exponential, Rayleigh, Pareto and Burr Type-XII 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 3-component 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.
3-Component mixture distributions
Reliability analysis
Failure rate function
Mean residual life function
Mean waiting time function
2018
06
01
1768
1781
http://scientiairanica.sharif.edu/article_4441_09c331f2b7a12c453da49a08c9085d1e.pdf