Omid Amirtaheri; Mostafa Zandieh; Behrouz Dorri
Abstract
In this paper, we investigate a decentralized manufacturer-distributer supply chain. In addition to global advertisement, the manufacturer participates in the local advertising expenditures of distributer. Bi-level programming approach is applied to model the relationship between the manufacturer and ...
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In this paper, we investigate a decentralized manufacturer-distributer supply chain. In addition to global advertisement, the manufacturer participates in the local advertising expenditures of distributer. Bi-level programming approach is applied to model the relationship between the manufacturer and retailer under two power scenarios of stackelberg game framework and the optimal policies in pricing, advertising, inventory management and logistics are identified. Two hierarchical genetic algorithms are proposed to solve the bi-level programming models. Based on collected data from Iranian automotive spare parts aftermarket, several numerical experiments are carried out to evaluate the validity of proposed models as well as the efficiency and effectiveness of solution procedures.
Abas Fadaei; Masood Rabieh; Mostafa Zandieh
Abstract
Considering that the active companies in the field of oil, gas,petrochemical and other energies are project-based and also theincrease of gas applicants who have taken policy of replacing the gasinstead of other fossil fuels, have imposed certain condition onorganizations and project managers in the ...
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Considering that the active companies in the field of oil, gas,petrochemical and other energies are project-based and also theincrease of gas applicants who have taken policy of replacing the gasinstead of other fossil fuels, have imposed certain condition onorganizations and project managers in the Gas Company.One of themost important problem in the issue of project management is projectportfolio selection which is defined one of the most importantactivities in many organization such as gas organization. In this studyat first the effective indicators on projects are extracted by using theliterature and interviews with the experts of gas industry then themathematical robust multi objective model is provided by consideringthe uncertainty and unreliability in some parameters of model. Thismodel is solved by using Non-dominate Sorting Genetic Algorithmfor 20 degree of risk-taking decision Gama ( , Ct Bt ).At the end forhelping in decision making the TOPSIS technique is used forproviding a specific answer in Pareto Front .
Amir Daneshvar; Mostafa Zandieh; Jamshid Nazemi
Volume 13, Issue 39 , January 2016, , Pages 1-34
Abstract
Outranking based models as one of the most important multicriteria decision methods need the definition of large amount of preferential information called “parameters” from decision maker. Because of the multiplicity of parameters, their confusing interpretation in problem context and the ...
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Outranking based models as one of the most important multicriteria decision methods need the definition of large amount of preferential information called “parameters” from decision maker. Because of the multiplicity of parameters, their confusing interpretation in problem context and the imprecise nature of data, Obtaining all these parameters simultaneously specially in large scale realistic credit problems which requires real time decision making is very complex and time-consuming.Preference Disaggregation approach infers these parameters from the holistic judgements provided by decision maker. This approach within multicriteria decision methods is equivalent to machine learning in artificial intelligence discipline.Under this approach this paper proposes a new learning method in which Genetic Algorithm(GA) in an evolutionary process induces all , ELECTRE TRI model parameters from training set then at the end of this process, classification is done on testing set by inferred parameters. Experimental analysis on credit data shows high quality and competitive results compared with some standard classification methods.
Seyed Habib A Rahmati; Mostafa Zandieh
Volume 10, Issue 27 , January 2012, , Pages 118-143
Abstract
In this paper, to make flexible job shop scheduling problem (FJSP)more realistic, an operational factor is considered in its model. Thisfactor, which is called optimizing total consumed electric power permonth, is known as the most important factor in calculation of theelectric cost of the industries. ...
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In this paper, to make flexible job shop scheduling problem (FJSP)more realistic, an operational factor is considered in its model. Thisfactor, which is called optimizing total consumed electric power permonth, is known as the most important factor in calculation of theelectric cost of the industries. Considering this factor, specificallyafter subsides elimination of the country, has became more important.In addition to this objective, two other common objectives, calledcomplementation time and critical work load of machines, areconsidered. To solve the multi-objective model, two algorithms,called multi-objective biogeography-based optimization algorithm(MOBBO) and multi-objective harmony search algorithm (MOHS),are developed and introduced to scheduling area for the first time.Finally, by developing some famous libraries of the problem,performance of the algorithms is compared statistically
Soheila Khishtandar; Farhad Farzad; Mostafa Zandieh
Volume 8, Issue 20 , March 2011, , Pages 81-99
Abstract
Different inventory control systems try to determine how much and when to order at the least relevant cost while maintaining a desirable service level for customers. In this article, a continuous review stochastic inventory system, with three objectives, is optimized. In this model, contrary to the traditional ...
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Different inventory control systems try to determine how much and when to order at the least relevant cost while maintaining a desirable service level for customers. In this article, a continuous review stochastic inventory system, with three objectives, is optimized. In this model, contrary to the traditional inventory models, customer service is not considered a shortage cost in the objective function. But the frequency of stock out occasions and the number of items stocked out annually are to be minimized. For determining the Pareto optimal set, multi-objective evolutionary algorithms are used. First, NSGA-II, MOGA, VEGA, RWGA are developed. Then some improvements in NSGA-II mechanisms are made and R-NSGA-II is developed. Subsequently, these algorithms are examined for some criteria such as set coverage and spacing, and the best algorithms for each criteria arc presented. The Result shows that R-NSGA-II has good scores for most criteria. Afterwards, Pareto optimal set is ranked using the method of global criteria.
Ashkan Ayough; Mostafa Zandyeh; Haide Mottaghi
Volume 6, Issue 16 , June 2007, , Pages 29-54
Abstract
In this paper we develop the concept of boring caused by doing the same jobs to two types of boring, negative or undesirable and positive or desirable, which are felt by operators because of doing similar jobs and not only due to doing the same ones. Based on this new concept, the flexible model has ...
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In this paper we develop the concept of boring caused by doing the same jobs to two types of boring, negative or undesirable and positive or desirable, which are felt by operators because of doing similar jobs and not only due to doing the same ones. Based on this new concept, the flexible model has been proposed by which jobs will be scheduled to minimize the total cost of assignment including the cost of doing the jobs by operators and the boring cost so that job scheduled with respect to their similarities in the smallest time period as well as dissimilarities in the biggest given time period. For the reason that the proposed job rotation scheduling model has a multi-period assignment structure and formulated as an integer non-linear model, it is recognized as a combinatorial optimization problem. So applying the metaheuristic algorithms to overcome the complexity of such a problem is required. We use the genetic and imperialist competitive algorithms to do that and verify their efficiency in comparison to that of Lingo software which solves the small integer nonlinear problems. It is also shown that the quality of imperialist competitive algorithm solutions is better than those of genetic algorithm for the proposed model.
