@article { author = {Abbasi, Seyedeh Elaheh and Aghaie, Abdollah and Fazlali, Mahboubeh}, title = {Applying Mahalanobis –Tagouchi System in Detection of High Risk Customers –A case-based study in an Insurance Company}, journal = {Advances in Industrial Engineering}, volume = {45}, number = {Special Issue}, pages = {1-12}, year = {2011}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {}, abstract = {The organizations use all appropriate tools to improve their service to the customers. The detection of especial customers and the forecast of their behavior undoubtedly can play an important role in improvement of service. In this paper, a new statistical method called the Mahalanobis Taguchi system has been used for this purpose. This method is used for the analysis of real data of an insurance company and five big cities in Iran are considered. There are seven initial factors which is important in the occurrence of accidents and losses. These factors are reduced to four. Customer's behavior is analyzed case by case by the Mahalanobis–distance concept. In fact with using this new method, demand of customers case by case was analyzed and it is an important outcome in analyzing behavior of customers. Devising ways to prevent the accidents and damages will need the recognition of Customer's behavior. The neural networks method is used to recognize the high–risk customers, and the results of this method are compared with the results of Mahalanobis–Taguchi system. The results show that Mahalanobis–Taguchi system with its abnormality scale has a great capability in recognizing high-risk customer. To recognize the customer by the Mahalanobis Taguchi system is more accurate in comparison with the neural networks method.}, keywords = {High - risk customers,Mahalanobis distance,Mahalanobis – Taguchi system,neural networks,Vehicle insurance}, url = {https://aie.ut.ac.ir/article_23321.html}, eprint = {https://aie.ut.ac.ir/article_23321_71c62221a8a463a1f39f6651ef5166f7.pdf} } @article { author = {Bashiri, Mahdi and B. Kazemzade, Reza and C. Atkinson, Anthony and Karimi, Hossein Karimi}, title = {Metaheuristic Based Multiple Response Process Optimization}, journal = {Advances in Industrial Engineering}, volume = {45}, number = {Special Issue}, pages = {13-23}, year = {2011}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {}, abstract = {The simultaneous optimization of multiple responses is an important problem in the design of industrial processes in order to achieve improved quality. In this paper, we present a new metaheuristic approach including Simulated Annealing and Particle Swarm Optimization to optimize all responses simultaneously. For the purpose of illustration and comparison, the proposed approach is applied to two problems taken from the literature. The results of our study show that the proposed approach outperforms the other approaches and can find better solutions. Finally, in both cases, we present the results of a sensitivity analysis incorporating experimental design.}, keywords = {Desirability function,Multiple response optimization,particle swarm optimization,Simulated Annealing}, url = {https://aie.ut.ac.ir/article_23322.html}, eprint = {https://aie.ut.ac.ir/article_23322_1533189b6bd6df953c94f62303adb71a.pdf} } @article { author = {Fasanghari, Mehdi Fasanghari and Keramati, abbas}, title = {Customer Churn Prediction Using Local Linear Model Tree for Iranian Telecommunication Companies}, journal = {Advances in Industrial Engineering}, volume = {45}, number = {Special Issue}, pages = {25-37}, year = {2011}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {}, abstract = {For winning in global competition, companies need to recognition and monitoring of customer's behavior to forecast their behavior and desires earlier than competitors. This research tries to recognize the attributes which lead to customer churn. For this, behavior of 3150 subscribers of an Iranian mobile operator, has observed during one year and trends of them has analyzed by a customized LLNF model. For this purpose, the application of the locally linear model tree (LOLIMOT) algorithm, which integrates the advantage of neural networks, tree model and fuzzy modeling, was experimented. Results suggest that dissatisfaction of customer, his/her usage from services and demographic attributes have significant effect on decision to churn or retention. Furthermore, the active or inactive subscriber situation has mediation effect on his/her retention.}, keywords = {customer churn,Fuzzy logic,LLNF,LOLIMOT,Mobile service provider,Neural Network,prediction}, url = {https://aie.ut.ac.ir/article_23323.html}, eprint = {https://aie.ut.ac.ir/article_23323_f2c182a2f0f4bfa0eebddbd0e7b2e748.pdf} } @article { author = {Gholipour, Yaghoob and Mohammad Zadeh, Parviz and Shirazi, Mohadeseh Sadat Shirazi}, title = {Global Meta-Model for System Level Multidisciplinary Design Optimization}, journal = {Advances in Industrial Engineering}, volume = {45}, number = {Special Issue}, pages = {39-49}, year = {2011}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {}, abstract = {This paper presents an efficient global meta-model building technique for solving high fidelity multidisciplinary design optimization (MDO) problems. The main difficulties associated with MDO are often characterized by interdisciplinary couplings, high computational cost of an analysis in individual disciplines and a large number of design variables and constraints. These issues result in very high overall computational cost limiting applications of MDO to complex industrial design problems. To address these issues a combination of global meta-model using moving least squares (MLSM) and the trust region strategy is introduced. A global meta-model is used to identify the feasible and infeasible regions and the trust region strategy is used for a detailed search of the feasible region. The technique is demonstrated on a test problem and the effectiveness of the method for modeling and system level collaborative optimization using high fidelity models is studied. The results show that meta-model based on MLSM provide a high degree of accuracy whilst achieving a considerable reduction in computational cost.}, keywords = {Collaborative optimization,Meta-model,Moving least squares method,Multidisciplinary design optimization,Trust region strategy}, url = {https://aie.ut.ac.ir/article_23324.html}, eprint = {https://aie.ut.ac.ir/article_23324_7eeb13a785701c9c5216b18b22437388.pdf} } @article { author = {gholipour, yaghob and Shahbazi, Mohammad Mehdi}, title = {Resource-Constrained Scheduling of Construction Projects Using the Harmony Search Algorithm}, journal = {Advances in Industrial Engineering}, volume = {45}, number = {Special Issue}, pages = {51-60}, year = {2011}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {}, abstract = {During the implementation, construction projects usually encounter situation that considerably affects the project scheduling and cost. This study aims at using an improved version of the harmony search algorithm (HSA) to schedule resource constrained construction projects. This model is formulated as a global optimization problem. It will determine the duration of each activity to minimize the project total cost. The algorithm tries to find the best duration for each activity so that it leads to the total consumption of the corresponding resource. This may cause some activities to start simultaneously. The improvements have been made to increase the convergence rate and to lower the cost and shorten duration of the project. A numerical example has been proposed to evaluate the efficacy of the algorithm. This algorithm also addresses issues such as multi-resource, resource combination, and resource limit. Two scenarios have been considered for the test problem. The former scenario shows the project scheduled using the minimum duration list and the latter scenario schedules the project using the optimization algorithm. A comparison between the two scenarios shows the effectiveness of the proposed algorithm in decreasing the total cost and duration of the projects.}, keywords = {Cash flow management,Construction projects,Project cost flow optimization,Project scheduling}, url = {https://aie.ut.ac.ir/article_23325.html}, eprint = {https://aie.ut.ac.ir/article_23325_851d2127647f0d741b3ad8165967cee0.pdf} } @article { author = {Hasani Doughabadi, Marziyeh and Bahrami, Hossein and Kolahan, Farhad}, title = {Evaluating the Effects of Parameters Setting on the Performance of Genetic Algorithm Using Regression Modeling and Statistical Analysis}, journal = {Advances in Industrial Engineering}, volume = {45}, number = {Special Issue}, pages = {61-68}, year = {2011}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {}, abstract = {Among various heuristics techniques, Genetic algorithm (GA) is one of the most widely used techniques which has successfully been applied on a variety of complex combinatorial problems. The performance of GA largely depends on the proper selection of its parameters values; including crossover mechanism, probability of crossover, population size and mutation rate and selection percent. In this paper, based on Design of Experiments (DOE) approach and regression modeling, the effects of tuning parameters on the performance of genetic algorithm have been evaluated. As an example, GA is applied to find a shortest distance for a well-known travelling salesman problem with 48 cities. The proposed approach can readily be implemented to any other optimization problem. To develop mathematical models, computational experiments have been carried out using a 4-factor 5-level Central Composite Design (CCD) matrix. Three types of regression functions models have been fitted to relate GA variables to its performance characteristic. Then, statistical analyses are performed to determine the best and most fitted model. Analysis of Variance (ANOVA) results indicate that the second order function is the best model that can properly represent the relationship between GA important variables and its performance measure (solution quality).}, keywords = {ANOVA,Design of experiments,Genetic algorithm,optimization,Regression modeling}, url = {https://aie.ut.ac.ir/article_23326.html}, eprint = {https://aie.ut.ac.ir/article_23326_fe8efb10463392851e8650d37a9d8099.pdf} } @article { author = {Jolai, Fariborz and Gheisariha, Elmira and Nojavan, Farnaz}, title = {Inventory Control of Perishable Items in a Two-Echelon Supply Chain}, journal = {Advances in Industrial Engineering}, volume = {45}, number = {Special Issue}, pages = {69-77}, year = {2011}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {}, abstract = {In this paper, we develop an inventory model for perishable items with random lifetime in a two-echelon production-distribution system. There is a manufacturer at the first stage that produces its product with a constant rate. Deterioration in this stage is modeled via a two-parameter Weibull distribution. At the second stage, the retailer places the order and receives the product instantly. The deterioration rate at this stage is a three-parameter Weibull distribution, which its initial value depends on the time the product has spent in the first stage before being transferred. The behavior of different key parameters of the model is analyzed using numerical studies.}, keywords = {Deteriorating items,Inventory Control,Production-distribution systems,Two-echelon supply chains}, url = {https://aie.ut.ac.ir/article_23327.html}, eprint = {https://aie.ut.ac.ir/article_23327_8b8e8539d5cc8c6c720423625b279b17.pdf} } @article { author = {Khajvand, Mahboubeh and Tarokh, Mohammad Jafar}, title = {Analyzing Customer Segmentation Based on Customer Value Components (Case Study: A Private Bank) (Technical note)}, journal = {Advances in Industrial Engineering}, volume = {45}, number = {Special Issue}, pages = {79-93}, year = {2011}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {}, abstract = {Studying about the customer segmentation and begetting customer ranking plan diverts more attention in recent years. In this regard, this study tries on providing a methodology for segmenting customers based on their value driver parameters which extracted from transaction data and then ranks customers with regard to their customer lifetime value (CLV) score. Discovering hidden pattern between customers ranking result the other data such as customer product ownership data and socio-demographic information is the other work which addressed in this paper. Achieving this, we used data mining techniques such as different classification and clustering approaches, and implemented them on real data from an Iranian private bank. Current study can provide good insights for marketing and CRM department of the organization in identifying different segments of customer for designing future strategic program.}, keywords = {customer lifetime value,customer segmentation,Data Mining,Decision rule,RFM analysis}, url = {https://aie.ut.ac.ir/article_23328.html}, eprint = {https://aie.ut.ac.ir/article_23328_0b63816b0de6dd205e4131701d16dcc4.pdf} } @article { author = {Makui, Ahmad and Sadjadi, Seyed Jafar and Karampour, Nazli}, title = {The Impact of Bullwhip Effect in a Highly Volatile Market}, journal = {Advances in Industrial Engineering}, volume = {45}, number = {Special Issue}, pages = {95-102}, year = {2011}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {}, abstract = {The bullwhip effect plays an important role in supply chain management especially in a highly volatile market where prices change due to many unexpected reasons brought about by different phenomenon such as global warming. Traditionally, one may expect a reduction on demand when there is a significant move on market price. However, the recent changes on global economy imply that the demand for a particular product may significantly increase as the price goes up in short time and it will come down in long run. There are many evidences to confirm this theory and as an example we could study the behaviour of price and demand for rice in September, 2008 in Iran’s economy. We present a mathematical model where demand is not only affected by price but also is influenced by the speed of price changes. Our model behaves identical the traditional demand model, where demand is only a function of price and price elasticity, when price rise is sluggish. However, in the event that there is a big shock in market price, the model has completely different attitude. The proposed model examines the bullwhip effect using the Lyapunov exponent.}, keywords = {Bullwhip Effect,Lyapunov exponent,Price fluctuation,Supply Chain}, url = {https://aie.ut.ac.ir/article_23329.html}, eprint = {https://aie.ut.ac.ir/article_23329_ed859c433032b84bbb35ba427f94e176.pdf} } @article { author = {Mohabbatdar, Samira and Esmaeili, Maryam}, title = {Optimal Selling Price, Marketing Expenditure and Order Quantity with Backordering}, journal = {Advances in Industrial Engineering}, volume = {45}, number = {Special Issue}, pages = {103-112}, year = {2011}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {}, abstract = {Demand is assumed constant in the classical economic order quantity (EOQ) model. However, in the real world, the demand is dependent on many factors such as the selling price, warranty of product and marketing effort. In addition pricing and ordering quantity decisions are interdependent for a seller when demand for the product is price sensitive in the inventory models. These types of models are very popular in the literature as joint pricing and order quantity models. Many researchers consider these models under some conditions such as quantity discount, trade credit and marketing effort. In this paper, we propose a new inventory model for the seller who conducts marketing effort. The marketing effort is the process of performing market research, selling products and/or services to customers and promoting them via advertising to further enhance sales. It is used to identify the customer, to satisfy the customer, and to keep the customer. This process will happen during the planning horizon; therefore the product will be demanded increasingly as time passes. This increasing in the demand leads to the backorder condition in the model. Since the marketing effort as a decision variable is dependent of the time, in this paper, the marketing effort is assumed a linear function of time which has an effect on the demand in addition of price in our model. The model would be included the backorder cost due to raising the shortage of inventory in addition, the purchasing, ordering and holding costs. An algorithm for finding the optimal solution for the selling price, marketing expenditure and the time length of positive stock are obtained when the seller’s pro?t is maximized. To clarify the model more, numerical examples presented in this paper, including sensitivity analysis of some key parameter- the cost parameters and non-cost parameters- that will compare the obtained results of proposed model.}, keywords = {Backlog,Backorder,Inventory Control,Marketing effort,pricing,Shortage}, url = {https://aie.ut.ac.ir/article_23330.html}, eprint = {https://aie.ut.ac.ir/article_23330_b2296dc5f443fb44679ac14c8292493a.pdf} } @article { author = {Omidbakhsh, Maryam and Seifbarghy, Mahdi}, title = {Solving Quadratic Assignment Problem (QAP) Using Invasive Weed Optimization Algorithm}, journal = {Advances in Industrial Engineering}, volume = {45}, number = {Special Issue}, pages = {113-125}, year = {2011}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {}, abstract = {A new powerful optimization algorithm inspired from colonizing weeds is utilized to solve the well-known quadratic assignment problem (QAP) which is of application in a large number of practical areas such as plant layout, machinery layout and so on. A set of reference numerical problems from QAPLIB is taken in order to evaluate the efficiency of the algorithm compared with the previous ones which had been applied to solve the addressed problem. The results indicate that the algorithm outperforms the competitive ones for a sizable number of the problems as the problems’ dimensions increase.}, keywords = {Invasive weed optimization,Meta heuristic algorithms,Quadratic assignment problem,Weed colonization}, url = {https://aie.ut.ac.ir/article_23331.html}, eprint = {https://aie.ut.ac.ir/article_23331_b88ba4bb2d5ae64992dce9ed834cfcd7.pdf} } @article { author = {Razmi, Jafar and Seifoory, Maryam and Pishvaee, Mir Saman}, title = {A Fuzzy Multi-Attribute Decision Making Model for Selecting the Best Supply Chain Strategy: Lean, Agile or Leagile}, journal = {Advances in Industrial Engineering}, volume = {45}, number = {Special Issue}, pages = {127-142}, year = {2011}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {}, abstract = {During recent years, determining appropriate strategy in the supply chain has become an important strategic issue. However, the nature of these decisions usually is complex and unstructured. To determine the best supply chain strategy, many quantitative and qualitative attributes such as cost, responsiveness and flexibility can be taken into account. In order to approximate the human subjective evaluation process, it would be desirable to apply a fuzzy MADM model. In this paper a fuzzy multi-attribute decision making (FMADM) model is developed to deal with strategy selection problem in a supply chain. A case study is used to validate the proposed model and the corresponding results show the power of the proposed model in handling subjective data in multi-attribute decision making process.}, keywords = {Fuzzy multi-attribute decision making,Strategy selection,Supply Chain Management}, url = {https://aie.ut.ac.ir/article_23332.html}, eprint = {https://aie.ut.ac.ir/article_23332_e7c71b00008ca2d3f8c8f4aa960243ae.pdf} }