Sharif University of TechnologyScientia Iranica1026-309825320180601The strategy of investment in the stock market using modified support vector regression model16291640444010.24200/sci.2017.4440ENChih-Hua HuangDa-Yeh UniversityFeng-Hua YangDepartment of International Business Management, Da-Yeh UniversityChien-Pang LeeDepartment of Maritime Information and Technology, National Kaohsiung Marine UniversityJournal Article20150421Stock 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.<br /> http://scientiairanica.sharif.edu/article_4440_2329a1e5d86f9b229afb9f05003ece1c.pdfSharif University of TechnologyScientia Iranica1026-309825320180601An application of Genetic Algorithm and PSO in an inventory model for single deteriorating item with variable demand dependent on marketing strategy and displayed stock level16411655444510.24200/sci.2017.4445ENAsoke Kumar BhuniaDepartment of Mathematics, The University of Burdwan, Burdwan-713104, IndiaAli Akbar ShaikhSchool of Engineering and Sciences, Tecnológico de Monterrey, Ave. E. Garza Sada 2501 Sur, C.P. 64849, Monterrey, Nuevo León, México0000-0001-6479-7002Vinti DhakaDepartment of Mathematics & Statistics, Banasthali University, Banathali, IndiaSarala PareekDepartment of Mathematics & Statistics, Banasthali University, Banathali, IndiaLeopoldo Eduardo Cárdenas-BarrónSchool of Engineering and Sciences, Tecnológico de Monterrey, Ave. E. Garza Sada 2501 Sur, C.P. 64849, Monterrey, Nuevo León, MéxicoJournal Article20150422This 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.http://scientiairanica.sharif.edu/article_4445_90e8a2c39f06482a0cbb7bad0605c322.pdfSharif University of TechnologyScientia Iranica1026-309825320180601Pricing and Lot sizing of a Decaying item under Group Dispatching with Time-dependent Demand and Decay Rates16561670444910.24200/sci.2017.4449ENAta Allah TaleizadehSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranArezu Rasuli-BaghbanDepartment of Industrial Engineering, Islamic, Azad University, South Tehran BranchJournal Article20150707Determining 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.<strong> </strong>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.http://scientiairanica.sharif.edu/article_4449_516efb2cb04c9dfd8e04908af1019019.pdfSharif University of TechnologyScientia Iranica1026-309825320180601An intuitionistic fuzzy OWA-TOPSIS method for collaborative network formation considering matching characteristics16711687444610.24200/sci.2017.4446ENTianri WangCollege of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, P.R. ChinaJuan LiuCollege of Energy Engineering, Zhejiang University, Hangzhou 310027, P.R. ChinaJizu LiCollege of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, P.R. ChinaYe XueCollege of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, P.R. ChinaHong DaiCollege of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, P.R. ChinaJournal Article20150808Collaborative 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.http://scientiairanica.sharif.edu/article_4446_716c8b9893c1263f2abf0a6773865945.pdfSharif University of TechnologyScientia Iranica1026-309825320180601On the n-job, m-machine permutation flow shop scheduling problems with makespan criterion and rework16881700444310.24200/sci.2017.4443ENBehrang BootakiDepartment of Industrial Engineering, Mazandaran University of Science and Technology, Babol, IranMohammad Mahdi PaydarDepartment of Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran0000-0002-0941-247XJournal Article20150809This paper addresses an <em>n</em>-job, <em>m</em>-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 methodshttp://scientiairanica.sharif.edu/article_4443_920bdb0c90b3cb3b5b736de9e53d5839.pdfSharif University of TechnologyScientia Iranica1026-309825320180601Designing an economical acceptance sampling plan in the presence of inspection errors based on maxima nomination sampling method170117112042210.24200/sci.2018.20422ENM.S. FallahnezhadDepartment of Industrial Engineering, Yazd University, Yazd, IranE. QazviniDepartment of Industrial Engineering, Yazd University, Yazd, Iran, Qazvini.elahe@gmail.com. Pejoohesh Street, Safa-ieh, Yazd, IranM. AbessiDepartment of Industrial Engineering, Yazd University, Yazd, Iran, mabessi@yazd.ac.ir. Pejoohesh Street, Safa-ieh, Yazd, IranJournal Article20151201One 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.http://scientiairanica.sharif.edu/article_20422_e54a3b53d9cf7149c5e0ae3ce17c53a6.pdfSharif University of TechnologyScientia Iranica1026-309825320180601A new nondominated sorting genetic algorithm based to the regression line For fuzzy traffic signal optimization problem17121723444210.24200/sci.2017.4442ENH. AsadiDepartment of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranR. Tavakkoli MoghaddamSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranN. Shahsavari PourDepartment of Industrial Technology and Management, Vali-e-Asr University Of Rafsanjan, Iran0000-0002-8671-7672E. NajafiDepartment of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranJournal Article20151202Traffic 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.<br /> 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 .http://scientiairanica.sharif.edu/article_4442_e75c1431a9fcff5150839be1610229c7.pdfSharif University of TechnologyScientia Iranica1026-309825320180601Incorporating demand, orders, lead time, and pricing decisions for reducing bullwhip effect in supply chains17241749444810.24200/sci.2017.4448ENR. GamasaeeDepartment of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran, P.O.BOX 15875-4413M.H. Fazel ZarandiDepartment of Industrial engineering, Amirkabir University of Technology, Tehran, IranJournal Article20160203The 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.http://scientiairanica.sharif.edu/article_4448_8fed9aca273919b302a893d65ee91941.pdfSharif University of TechnologyScientia Iranica1026-309825320180601A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem17501767444710.24200/sci.2017.4447ENDavood ShisheboriDepartment of Industrial Engineering, Yazd University, P.C.1684613114, Yazd, IranAbolghasem Yousefi BabadiSchool of Industrial and systems Engineering, University of Tehran, Tehran, IranZohre NoormohammadzadehDepartment of Industrial Engineering, Yazd University, P.C.1684613114, Yazd, IranJournal Article20160526This 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. <br /> <strong> </strong>http://scientiairanica.sharif.edu/article_4447_dc93de4ed08928a49de89ce6c8694e1b.pdfSharif University of TechnologyScientia Iranica1026-309825320180601Reliability analysis of 3-component mixture of distributions17681781444110.24200/sci.2017.4441ENM. AslamDepartment of Mathematics and Statistics, Riphah International University, Islamabad 44000, Pakistan.M. TahirDepartment of Statistics, Government College University, Faisalabad 38000, Pakistan.Z. HussainDepartment of Statistics, Quaid-i-Azam University, Islamabad 44000, PakistanJournal Article20151103This article focuses on studying 3-component mixtures of Exponential, Rayleigh, Pareto and Burr Type-<br />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.http://scientiairanica.sharif.edu/article_4441_09c331f2b7a12c453da49a08c9085d1e.pdf