S. Molaei; K.M. Cyrus
Volume 3, Issue 1 , May 2014, , Pages 39-48
Abstract
Preventive maintenance is a broad term that encompasses a set of activities aimed at improving the overall reliability and availability of a system. Designers of the preventive maintenance schedules attempt to minimize the overall cost of system operation. There is no substitute for perfection in maintenance ...
Read More
Preventive maintenance is a broad term that encompasses a set of activities aimed at improving the overall reliability and availability of a system. Designers of the preventive maintenance schedules attempt to minimize the overall cost of system operation. There is no substitute for perfection in maintenance to ensure zero breakdowns in machine; therefore it is necessary to get a machinery breakdown insurance against the risks that might occur at business. Previous researches didn’t consider the effect of engineering insurance on maintenance scheduling while it affect the cost function of maintenance scheduling seriously. Engineering insurance pays for all repair costs of machinery, therefore the cost function of maintenance scheduling is affected. This paper presents a new cost function for maintenance scheduling by considering the effects of engineering insurance. Due to the uncertainty in the cost parameters related to the cost function which are very common in application, the paper proposed the application of the scenario-based approach for robust design of maintenance scheduling. Then, genetic algorithm is developed for obtaining the optimal solution of the proposed robust model and the effectiveness of this model is illustrated through a numerical example.
H. Abbasimehr; S. Alizadeh
Volume 2, Issue 4 , December 2013, , Pages 1-14
Abstract
Customer churn has become a critical problem for all companies in particular for those that are operating in service-based industries such as telecommunication industry. Data mining techniques have been used for constructing churn prediction models. Past research in churn prediction context have mainly ...
Read More
Customer churn has become a critical problem for all companies in particular for those that are operating in service-based industries such as telecommunication industry. Data mining techniques have been used for constructing churn prediction models. Past research in churn prediction context have mainly focused on the accuracy aspect of the constructed churn models. However, in addition to the accuracy, comprehensibility aspect should be considered in evaluating a churn prediction model. Being comprehensible, a model can reveal the main reasons for customer churn; thereby mangers can use such information for effective decisions making about marketing actions. In this paper, we demonstrate the application of a genetic-algorithm (GA) method for building accurate and comprehensible churn prediction model. The proposed method, GA-based method uses a wrapper based feature selection approach for choosing the best feature subset. The key advantage of this method, is taking into account the comprehensibility measure (measured as the number of rules extracted from C4.5 decision tree) in evaluating the performance of a candidate model. The GA-based method is compared to the two filter feature selection methods including Chi-squared based and Correlation based feature selection using two telecommunication churn datasets. The results of experiments indicated that the GA-based method performs better than the two filter methods in terms of both accuracy and comprehensibility
H. Karimi; A.A. Najafi
Volume 2, Issue 2 , June 2013, , Pages 35-46
Abstract
In this paper, we consider a reliability redundancy optimization problem in a series-parallel type system employing the redundancy strategy of cold-standby. The problem consists of two parts component selection and determination of redundancy level of each component—which need to be solved so that ...
Read More
In this paper, we consider a reliability redundancy optimization problem in a series-parallel type system employing the redundancy strategy of cold-standby. The problem consists of two parts component selection and determination of redundancy level of each component—which need to be solved so that the mean lifetime of the system can be maximized. The redundancy allocation problem is nondeterministic polynomial-time hard and is solved by a combined genetic algorithm - simulation approach. Finally, this algorithm is tested on 33 benchmark problems.
N. Shahsavari Pour; M.H. Abolhasani Ashkezari; H. Mohammadi Andargoli
Volume 2, Issue 1 , February 2013, , Pages 20-29
Abstract
Considering flow shop scheduling problem with more objectives, will help to make it more practical. For this purpose, we have intended both the makespan and total due date cost simultaneously. Total due date cost is included the sum of earliness and tardiness cost. In order to solve this problem, a genetic ...
Read More
Considering flow shop scheduling problem with more objectives, will help to make it more practical. For this purpose, we have intended both the makespan and total due date cost simultaneously. Total due date cost is included the sum of earliness and tardiness cost. In order to solve this problem, a genetic algorithm is developed. In this GA algorithm, to further explore in solution space a Tabu Search algorithm is used. Also in selecting the new population, is used the concept of elitism to increase the chance of choosing the best sequence. To evaluate the performance of this algorithm and performing the experiments, it is coded in VBA. Experiments results and comparison with GA is indicated the high potential of this algorithm in solving the multi-objective problems.
A. Jafari; P. Chiniforooshan; F. Zabihi
Volume 2, Issue 1 , February 2013, , Pages 45-62
Abstract
This paper investigates the problem of designing an integrated production-distribution system which supports strategic and tactical decision levels in supply chain management. This overall optimization is achieved using mathematical programming for modeling the supply chain functions such as location, ...
Read More
This paper investigates the problem of designing an integrated production-distribution system which supports strategic and tactical decision levels in supply chain management. This overall optimization is achieved using mathematical programming for modeling the supply chain functions such as location, production, and distribution functions. Our model intends to minimize the total cost including production, location, transportation, and inventory holding costs. In view of the NP-hard nature of the problem, this paper provides a hybrid algorithm incorporates Genetic Algorithm into Lagrangian Relaxation method (namely HLRGA) to update the lagrangian multipliers and improve the performance of LR method. The effectiveness of HLRGA has been investigated by comparing its results with those obtained by CPLEX, hybrid genetic algorithm, and simulated annealing on a set of supply chain network problems with different sizes. Finally, an industrial case demonstrates the feasibility of applying the proposed model and algorithm to the real-world problem in a supply chain network.
M. Seyedrezaei; S.E. Najafi; A. Aghajani; H. Bagherzadeh Valami
Volume 1, Issue 2 , July 2012, , Pages 40-57
Abstract
Distribution centers (DCs) play an important key role in supply chain. Delivering the right items to the right customers at the right time, at the right cost is a critical mission of the DCs. Today, customer satisfaction is an important factor for supplier companies in order to gain more profits. Optimizing ...
Read More
Distribution centers (DCs) play an important key role in supply chain. Delivering the right items to the right customers at the right time, at the right cost is a critical mission of the DCs. Today, customer satisfaction is an important factor for supplier companies in order to gain more profits. Optimizing the number of fulfilled orders (An order that the required quantity of all items in that order are available from the inventory and can be send to the customer) in a time period may lead to delay some major orders; and consequently lead to dissatisfaction of these customers, ultimately loss them and lead to lower profits. In addition, some inventory may remain in the warehouse in a time-period and over the time become corrupt. It also leads to reduce the benefit of supplier companies in the supply chain. Therefore, in this paper, we will present a dynamic mathematical model to flow process /storage process of goods for order picking planning problem (OPP) in DCs. And we will optimize the number of fulfilled orders in this problem with regard to a) the coefficient of each customer, b) to meet each customer's needs in the least time c) probabilistic demand of customers, and d) taking inventory to send to customers at the earliest opportunity to prevent their decay. After presenting the mathematical model, we use Lingo software to solve small size problems. Complexity of the mathematical model will intensify by increasing the numbers of customers and products in distribution center, Therefore Lingo software will not able to solve these problems in a reasonable time. Therefore, we will develop and use a genetic algorithm (GA) for solving these problems.
