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�ݺ�ߣshows by User: hnazari2 / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: hnazari2 / Tue, 25 Nov 2014 00:40:51 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: hnazari2 Ijsom call for paper 11.2014 /slideshow/ijsom-call-for-paper-112014/41985664 ijsom-callforpaper11-141125004052-conversion-gate01
logistic and supplychain]]>
logistic and supplychain]]> Tue, 25 Nov 2014 00:40:51 GMT /slideshow/ijsom-call-for-paper-112014/41985664 hnazari2@slideshare.net(hnazari2) Ijsom call for paper 11.2014 hnazari2 logistic and supplychain <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijsom-callforpaper11-141125004052-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> logistic and supplychain

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0 Ijsom19061398886200 /slideshow/ijsom19061398886200/38295612 ijsom19061398886200-140824034424-phpapp02
This paper explains a model for analyzing and measuring the propagation of order amplifications (i.e. bullwhip effect) for a single-product supply network topology considering exogenous uncertainty and linear and time-invariant inventory management policies for network entities. The stream of orders placed by each entity of the network is characterized assuming customer demand is ergodic. In fact, we propose an exact formula in order to measure the bullwhip effect in the addressed supply network topology considering the system in Markovian chain framework and presenting a matrix of network member relationships and relevant order sequences. The formula turns out using a mathematical method called frequency domain analysis. The major contribution of this paper is analyzing the bullwhip effect considering exogenous uncertainty in supply networks and using the Fourier transform in order to simplify the relevant calculations. We present a number of numerical examples to assess the analytical results accuracy in quantifying the bullwhip effect.]]>
This paper explains a model for analyzing and measuring the propagation of order amplifications (i.e. bullwhip effect) for a single-product supply network topology considering exogenous uncertainty and linear and time-invariant inventory management policies for network entities. The stream of orders placed by each entity of the network is characterized assuming customer demand is ergodic. In fact, we propose an exact formula in order to measure the bullwhip effect in the addressed supply network topology considering the system in Markovian chain framework and presenting a matrix of network member relationships and relevant order sequences. The formula turns out using a mathematical method called frequency domain analysis. The major contribution of this paper is analyzing the bullwhip effect considering exogenous uncertainty in supply networks and using the Fourier transform in order to simplify the relevant calculations. We present a number of numerical examples to assess the analytical results accuracy in quantifying the bullwhip effect.]]> Sun, 24 Aug 2014 03:44:24 GMT /slideshow/ijsom19061398886200/38295612 hnazari2@slideshare.net(hnazari2) Ijsom19061398886200 hnazari2 This paper explains a model for analyzing and measuring the propagation of order amplifications (i.e. bullwhip effect) for a single-product supply network topology considering exogenous uncertainty and linear and time-invariant inventory management policies for network entities. The stream of orders placed by each entity of the network is characterized assuming customer demand is ergodic. In fact, we propose an exact formula in order to measure the bullwhip effect in the addressed supply network topology considering the system in Markovian chain framework and presenting a matrix of network member relationships and relevant order sequences. The formula turns out using a mathematical method called frequency domain analysis. The major contribution of this paper is analyzing the bullwhip effect considering exogenous uncertainty in supply networks and using the Fourier transform in order to simplify the relevant calculations. We present a number of numerical examples to assess the analytical results accuracy in quantifying the bullwhip effect. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijsom19061398886200-140824034424-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This paper explains a model for analyzing and measuring the propagation of order amplifications (i.e. bullwhip effect) for a single-product supply network topology considering exogenous uncertainty and linear and time-invariant inventory management policies for network entities. The stream of orders placed by each entity of the network is characterized assuming customer demand is ergodic. In fact, we propose an exact formula in order to measure the bullwhip effect in the addressed supply network topology considering the system in Markovian chain framework and presenting a matrix of network member relationships and relevant order sequences. The formula turns out using a mathematical method called frequency domain analysis. The major contribution of this paper is analyzing the bullwhip effect considering exogenous uncertainty in supply networks and using the Fourier transform in order to simplify the relevant calculations. We present a number of numerical examples to assess the analytical results accuracy in quantifying the bullwhip effect.

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0 Ijsom19051398886200 /hnazari2/ijsom19051398886200 ijsom19051398886200-140824034408-phpapp01
This paper explains a model for analyzing and measuring the propagation of order amplifications (i.e. bullwhip effect) for a single-product supply network topology considering exogenous uncertainty and linear and time-invariant inventory management policies for network entities. The stream of orders placed by each entity of the network is characterized assuming customer demand is ergodic. In fact, we propose an exact formula in order to measure the bullwhip effect in the addressed supply network topology considering the system in Markovian chain framework and presenting a matrix of network member relationships and relevant order sequences. The formula turns out using a mathematical method called frequency domain analysis. The major contribution of this paper is analyzing the bullwhip effect considering exogenous uncertainty in supply networks and using the Fourier transform in order to simplify the relevant calculations. We present a number of numerical examples to assess the analytical results accuracy in quantifying the bullwhip effect.]]>
This paper explains a model for analyzing and measuring the propagation of order amplifications (i.e. bullwhip effect) for a single-product supply network topology considering exogenous uncertainty and linear and time-invariant inventory management policies for network entities. The stream of orders placed by each entity of the network is characterized assuming customer demand is ergodic. In fact, we propose an exact formula in order to measure the bullwhip effect in the addressed supply network topology considering the system in Markovian chain framework and presenting a matrix of network member relationships and relevant order sequences. The formula turns out using a mathematical method called frequency domain analysis. The major contribution of this paper is analyzing the bullwhip effect considering exogenous uncertainty in supply networks and using the Fourier transform in order to simplify the relevant calculations. We present a number of numerical examples to assess the analytical results accuracy in quantifying the bullwhip effect.]]> Sun, 24 Aug 2014 03:44:08 GMT /hnazari2/ijsom19051398886200 hnazari2@slideshare.net(hnazari2) Ijsom19051398886200 hnazari2 This paper explains a model for analyzing and measuring the propagation of order amplifications (i.e. bullwhip effect) for a single-product supply network topology considering exogenous uncertainty and linear and time-invariant inventory management policies for network entities. The stream of orders placed by each entity of the network is characterized assuming customer demand is ergodic. In fact, we propose an exact formula in order to measure the bullwhip effect in the addressed supply network topology considering the system in Markovian chain framework and presenting a matrix of network member relationships and relevant order sequences. The formula turns out using a mathematical method called frequency domain analysis. The major contribution of this paper is analyzing the bullwhip effect considering exogenous uncertainty in supply networks and using the Fourier transform in order to simplify the relevant calculations. We present a number of numerical examples to assess the analytical results accuracy in quantifying the bullwhip effect. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijsom19051398886200-140824034408-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This paper explains a model for analyzing and measuring the propagation of order amplifications (i.e. bullwhip effect) for a single-product supply network topology considering exogenous uncertainty and linear and time-invariant inventory management policies for network entities. The stream of orders placed by each entity of the network is characterized assuming customer demand is ergodic. In fact, we propose an exact formula in order to measure the bullwhip effect in the addressed supply network topology considering the system in Markovian chain framework and presenting a matrix of network member relationships and relevant order sequences. The formula turns out using a mathematical method called frequency domain analysis. The major contribution of this paper is analyzing the bullwhip effect considering exogenous uncertainty in supply networks and using the Fourier transform in order to simplify the relevant calculations. We present a number of numerical examples to assess the analytical results accuracy in quantifying the bullwhip effect.

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0 Ijsom19041398886200 /slideshow/ijsom19041398886200/38295602 ijsom19041398886200-140824034346-phpapp01
The Shapley value, one of the most common solution concepts of cooperative game theory is defined and axiomatically characterized in different game-theoretic models. Certainly, the Shapley value can be used in interesting sharing cost/reward problems in the Operations Research area such as connection, routing, scheduling, production and inventory situations. In this paper, we focus on the Shapley value for cooperative games, where the set of players is finite and the coalition values are interval grey numbers. The central question in this paper is how to characterize the grey Shapley value. In this context, we present two alternative axiomatic characterizations. First, we characterize the grey Shapley value using the properties of efficiency, symmetry and strong monotonicity. Second, we characterize the grey Shapley value by using the grey dividends.]]>
The Shapley value, one of the most common solution concepts of cooperative game theory is defined and axiomatically characterized in different game-theoretic models. Certainly, the Shapley value can be used in interesting sharing cost/reward problems in the Operations Research area such as connection, routing, scheduling, production and inventory situations. In this paper, we focus on the Shapley value for cooperative games, where the set of players is finite and the coalition values are interval grey numbers. The central question in this paper is how to characterize the grey Shapley value. In this context, we present two alternative axiomatic characterizations. First, we characterize the grey Shapley value using the properties of efficiency, symmetry and strong monotonicity. Second, we characterize the grey Shapley value by using the grey dividends.]]> Sun, 24 Aug 2014 03:43:46 GMT /slideshow/ijsom19041398886200/38295602 hnazari2@slideshare.net(hnazari2) Ijsom19041398886200 hnazari2 The Shapley value, one of the most common solution concepts of cooperative game theory is defined and axiomatically characterized in different game-theoretic models. Certainly, the Shapley value can be used in interesting sharing cost/reward problems in the Operations Research area such as connection, routing, scheduling, production and inventory situations. In this paper, we focus on the Shapley value for cooperative games, where the set of players is finite and the coalition values are interval grey numbers. The central question in this paper is how to characterize the grey Shapley value. In this context, we present two alternative axiomatic characterizations. First, we characterize the grey Shapley value using the properties of efficiency, symmetry and strong monotonicity. Second, we characterize the grey Shapley value by using the grey dividends. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijsom19041398886200-140824034346-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> The Shapley value, one of the most common solution concepts of cooperative game theory is defined and axiomatically characterized in different game-theoretic models. Certainly, the Shapley value can be used in interesting sharing cost/reward problems in the Operations Research area such as connection, routing, scheduling, production and inventory situations. In this paper, we focus on the Shapley value for cooperative games, where the set of players is finite and the coalition values are interval grey numbers. The central question in this paper is how to characterize the grey Shapley value. In this context, we present two alternative axiomatic characterizations. First, we characterize the grey Shapley value using the properties of efficiency, symmetry and strong monotonicity. Second, we characterize the grey Shapley value by using the grey dividends.

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0 Ijsom19031398886200 /slideshow/ijsom19031398886200/38295598 ijsom19031398886200-140824034326-phpapp02
In some industries as foundries, it is not technically feasible to interrupt a processor between jobs. This restriction gives rise to a scheduling problem called no-idle scheduling. This paper deals with scheduling of no-idle open shops to minimize maximum completion time of jobs, called makespan. The problem is first mathematically formulated by three different mixed integer linear programming models. Since open shop scheduling problems are NP-hard, only small instances can be solved to optimality using these models. Thus, to solve large instances, two meta-heuristics based on simulated annealing and genetic algorithms are developed. A complete numerical experiment is conducted and the developed models and algorithms are compared. The results show that genetic algorithm outperforms simulated annealing.]]>
In some industries as foundries, it is not technically feasible to interrupt a processor between jobs. This restriction gives rise to a scheduling problem called no-idle scheduling. This paper deals with scheduling of no-idle open shops to minimize maximum completion time of jobs, called makespan. The problem is first mathematically formulated by three different mixed integer linear programming models. Since open shop scheduling problems are NP-hard, only small instances can be solved to optimality using these models. Thus, to solve large instances, two meta-heuristics based on simulated annealing and genetic algorithms are developed. A complete numerical experiment is conducted and the developed models and algorithms are compared. The results show that genetic algorithm outperforms simulated annealing.]]> Sun, 24 Aug 2014 03:43:26 GMT /slideshow/ijsom19031398886200/38295598 hnazari2@slideshare.net(hnazari2) Ijsom19031398886200 hnazari2 In some industries as foundries, it is not technically feasible to interrupt a processor between jobs. This restriction gives rise to a scheduling problem called no-idle scheduling. This paper deals with scheduling of no-idle open shops to minimize maximum completion time of jobs, called makespan. The problem is first mathematically formulated by three different mixed integer linear programming models. Since open shop scheduling problems are NP-hard, only small instances can be solved to optimality using these models. Thus, to solve large instances, two meta-heuristics based on simulated annealing and genetic algorithms are developed. A complete numerical experiment is conducted and the developed models and algorithms are compared. The results show that genetic algorithm outperforms simulated annealing. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijsom19031398886200-140824034326-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> In some industries as foundries, it is not technically feasible to interrupt a processor between jobs. This restriction gives rise to a scheduling problem called no-idle scheduling. This paper deals with scheduling of no-idle open shops to minimize maximum completion time of jobs, called makespan. The problem is first mathematically formulated by three different mixed integer linear programming models. Since open shop scheduling problems are NP-hard, only small instances can be solved to optimality using these models. Thus, to solve large instances, two meta-heuristics based on simulated annealing and genetic algorithms are developed. A complete numerical experiment is conducted and the developed models and algorithms are compared. The results show that genetic algorithm outperforms simulated annealing.

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0 Ijsom19021398886200 /slideshow/ijsom19021398886200/38295592 ijsom19021398886200-140824034308-phpapp01
The location-routing problem is a relatively new branch of logistics system. Its objective is to determine a suitable location for constructing distribution warehouses and proper transportation routing from warehouse to the customer. In this study, the location-routing problem is investigated with considering fuzzy servicing time window for each customer. Another important issue in this regard is the existence of congested times during the service time and distributing goods to the customer. This caused a delay in providing service for customer and imposed additional costs to distribution system. Thus we have provided a mathematical model for designing optimal distributing system. Since the vehicle location-routing problem is Np-hard, thus a solution method using genetic meta-heuristic algorithm was developed and the optimal sequence of servicing for the vehicle and optimal location for the warehouses were determined through an example. ]]>
The location-routing problem is a relatively new branch of logistics system. Its objective is to determine a suitable location for constructing distribution warehouses and proper transportation routing from warehouse to the customer. In this study, the location-routing problem is investigated with considering fuzzy servicing time window for each customer. Another important issue in this regard is the existence of congested times during the service time and distributing goods to the customer. This caused a delay in providing service for customer and imposed additional costs to distribution system. Thus we have provided a mathematical model for designing optimal distributing system. Since the vehicle location-routing problem is Np-hard, thus a solution method using genetic meta-heuristic algorithm was developed and the optimal sequence of servicing for the vehicle and optimal location for the warehouses were determined through an example. ]]> Sun, 24 Aug 2014 03:43:08 GMT /slideshow/ijsom19021398886200/38295592 hnazari2@slideshare.net(hnazari2) Ijsom19021398886200 hnazari2 The location-routing problem is a relatively new branch of logistics system. Its objective is to determine a suitable location for constructing distribution warehouses and proper transportation routing from warehouse to the customer. In this study, the location-routing problem is investigated with considering fuzzy servicing time window for each customer. Another important issue in this regard is the existence of congested times during the service time and distributing goods to the customer. This caused a delay in providing service for customer and imposed additional costs to distribution system. Thus we have provided a mathematical model for designing optimal distributing system. Since the vehicle location-routing problem is Np-hard, thus a solution method using genetic meta-heuristic algorithm was developed and the optimal sequence of servicing for the vehicle and optimal location for the warehouses were determined through an example. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijsom19021398886200-140824034308-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> The location-routing problem is a relatively new branch of logistics system. Its objective is to determine a suitable location for constructing distribution warehouses and proper transportation routing from warehouse to the customer. In this study, the location-routing problem is investigated with considering fuzzy servicing time window for each customer. Another important issue in this regard is the existence of congested times during the service time and distributing goods to the customer. This caused a delay in providing service for customer and imposed additional costs to distribution system. Thus we have provided a mathematical model for designing optimal distributing system. Since the vehicle location-routing problem is Np-hard, thus a solution method using genetic meta-heuristic algorithm was developed and the optimal sequence of servicing for the vehicle and optimal location for the warehouses were determined through an example.

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0 Ijsom19011398886200 /slideshow/ijsom19011398886200/38295587 ijsom19011398886200-140824034245-phpapp02
In paper (2004) Chang studied an inventory model under a situation in which the supplier provides the purchaser with a permissible delay of payments if the purchaser orders a large quantity. Tripathi (2011) also studied an inventory model with time dependent demand rate under which the supplier provides the purchaser with a permissible delay in payments. This paper is motivated by Chang (2004) and Tripathi (2011) paper extending their model for exponential time dependent demand rate. This study develops an inventory model under which the vendor provides the purchaser with a credit period; if the purchaser orders large quantity. In this chapter, demand rate is taken as exponential time dependent. Shortages are not allowed and effect of the inflation rate has been discussed. We establish an inventory model for deteriorating items if the order quantity is greater than or equal to a predetermined quantity. We then obtain optimal solution for finding optimal order quantity, optimal cycle time and optimal total relevant cost. Numerical examples are given for all different cases. Sensitivity of the variation of different parameters on the optimal solution is also discussed. Mathematica 7 software is used for finding numerical examples.]]>
In paper (2004) Chang studied an inventory model under a situation in which the supplier provides the purchaser with a permissible delay of payments if the purchaser orders a large quantity. Tripathi (2011) also studied an inventory model with time dependent demand rate under which the supplier provides the purchaser with a permissible delay in payments. This paper is motivated by Chang (2004) and Tripathi (2011) paper extending their model for exponential time dependent demand rate. This study develops an inventory model under which the vendor provides the purchaser with a credit period; if the purchaser orders large quantity. In this chapter, demand rate is taken as exponential time dependent. Shortages are not allowed and effect of the inflation rate has been discussed. We establish an inventory model for deteriorating items if the order quantity is greater than or equal to a predetermined quantity. We then obtain optimal solution for finding optimal order quantity, optimal cycle time and optimal total relevant cost. Numerical examples are given for all different cases. Sensitivity of the variation of different parameters on the optimal solution is also discussed. Mathematica 7 software is used for finding numerical examples.]]> Sun, 24 Aug 2014 03:42:45 GMT /slideshow/ijsom19011398886200/38295587 hnazari2@slideshare.net(hnazari2) Ijsom19011398886200 hnazari2 In paper (2004) Chang studied an inventory model under a situation in which the supplier provides the purchaser with a permissible delay of payments if the purchaser orders a large quantity. Tripathi (2011) also studied an inventory model with time dependent demand rate under which the supplier provides the purchaser with a permissible delay in payments. This paper is motivated by Chang (2004) and Tripathi (2011) paper extending their model for exponential time dependent demand rate. This study develops an inventory model under which the vendor provides the purchaser with a credit period; if the purchaser orders large quantity. In this chapter, demand rate is taken as exponential time dependent. Shortages are not allowed and effect of the inflation rate has been discussed. We establish an inventory model for deteriorating items if the order quantity is greater than or equal to a predetermined quantity. We then obtain optimal solution for finding optimal order quantity, optimal cycle time and optimal total relevant cost. Numerical examples are given for all different cases. Sensitivity of the variation of different parameters on the optimal solution is also discussed. Mathematica 7 software is used for finding numerical examples. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijsom19011398886200-140824034245-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> In paper (2004) Chang studied an inventory model under a situation in which the supplier provides the purchaser with a permissible delay of payments if the purchaser orders a large quantity. Tripathi (2011) also studied an inventory model with time dependent demand rate under which the supplier provides the purchaser with a permissible delay in payments. This paper is motivated by Chang (2004) and Tripathi (2011) paper extending their model for exponential time dependent demand rate. This study develops an inventory model under which the vendor provides the purchaser with a credit period; if the purchaser orders large quantity. In this chapter, demand rate is taken as exponential time dependent. Shortages are not allowed and effect of the inflation rate has been discussed. We establish an inventory model for deteriorating items if the order quantity is greater than or equal to a predetermined quantity. We then obtain optimal solution for finding optimal order quantity, optimal cycle time and optimal total relevant cost. Numerical examples are given for all different cases. Sensitivity of the variation of different parameters on the optimal solution is also discussed. Mathematica 7 software is used for finding numerical examples.

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0 Ijsom19001398886200 /slideshow/ijsom19001398886200/38295580 ijsom19001398886200-140824034226-phpapp01
This paper studies an M/G/1 repairable queueing system with multiple vacations and N-policy, in which the service station is subject to occasional random breakdowns. When the service station breaks down, it is repaired by a repair facility. Moreover, the repair facility may fail during the repair period of the service station. The failed repair facility resumes repair after completion of its replacement. Under these assumptions, applying a simple method, the probability that the service station is broken, the rate of occurrence of breakdowns of the service station, the probability that the repair facility is being replaced and the rate of occurrence of failures of the repair facility along with other performance measures are obtained. Following the construction of the long-run expected cost function per unit time, the direct search method is implemented for determining the optimum threshold N* that minimises the cost function.]]>
This paper studies an M/G/1 repairable queueing system with multiple vacations and N-policy, in which the service station is subject to occasional random breakdowns. When the service station breaks down, it is repaired by a repair facility. Moreover, the repair facility may fail during the repair period of the service station. The failed repair facility resumes repair after completion of its replacement. Under these assumptions, applying a simple method, the probability that the service station is broken, the rate of occurrence of breakdowns of the service station, the probability that the repair facility is being replaced and the rate of occurrence of failures of the repair facility along with other performance measures are obtained. Following the construction of the long-run expected cost function per unit time, the direct search method is implemented for determining the optimum threshold N* that minimises the cost function.]]> Sun, 24 Aug 2014 03:42:26 GMT /slideshow/ijsom19001398886200/38295580 hnazari2@slideshare.net(hnazari2) Ijsom19001398886200 hnazari2 This paper studies an M/G/1 repairable queueing system with multiple vacations and N-policy, in which the service station is subject to occasional random breakdowns. When the service station breaks down, it is repaired by a repair facility. Moreover, the repair facility may fail during the repair period of the service station. The failed repair facility resumes repair after completion of its replacement. Under these assumptions, applying a simple method, the probability that the service station is broken, the rate of occurrence of breakdowns of the service station, the probability that the repair facility is being replaced and the rate of occurrence of failures of the repair facility along with other performance measures are obtained. Following the construction of the long-run expected cost function per unit time, the direct search method is implemented for determining the optimum threshold N* that minimises the cost function. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijsom19001398886200-140824034226-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This paper studies an M/G/1 repairable queueing system with multiple vacations and N-policy, in which the service station is subject to occasional random breakdowns. When the service station breaks down, it is repaired by a repair facility. Moreover, the repair facility may fail during the repair period of the service station. The failed repair facility resumes repair after completion of its replacement. Under these assumptions, applying a simple method, the probability that the service station is broken, the rate of occurrence of breakdowns of the service station, the probability that the repair facility is being replaced and the rate of occurrence of failures of the repair facility along with other performance measures are obtained. Following the construction of the long-run expected cost function per unit time, the direct search method is implemented for determining the optimum threshold N* that minimises the cost function.

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0 https://cdn.slidesharecdn.com/profile-photo-hnazari2-48x48.jpg?cb=1523418786 International Journal of Supply and Operations Management (IJSOM) is a scholarly peer-reviewed international scientific journal and is committed to publishing scholarly empirical and theoretical research articles that have a high impact on the engineering & management field as a whole. All aspects of the subject in relation to Logistics, Supply and Operations Management are covered. The ultimate objective of the journal is to disseminate knowledge for improving industrial practice and to strengthen the theoretical base necessary for supporting sound decision making. IJSOM Advantages: Free of charge: Our objective is to publish the high quality researches. Therefore, publishing paper in www.IJSOM.com https://cdn.slidesharecdn.com/ss_thumbnails/ijsom-callforpaper11-141125004052-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/ijsom-call-for-paper-112014/41985664 Ijsom call for paper 1... https://cdn.slidesharecdn.com/ss_thumbnails/ijsom19061398886200-140824034424-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/ijsom19061398886200/38295612 Ijsom19061398886200 https://cdn.slidesharecdn.com/ss_thumbnails/ijsom19051398886200-140824034408-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds hnazari2/ijsom19051398886200 Ijsom19051398886200