جستجوی مقالات مرتبط با کلیدواژه "double-frontier" در نشریات گروه "صنایع"

Classic Data Envelopment Analysis methods ignore the internal interactions and consider the systems as a ‘black box’. Most of the network analysis models are nonlinear and it is feasible that these point may cause a considerable amount of modification to occur in the efficiency results. Amidst which, models, such as, the Wang Model, takes the intermediate variables into account, but in order to prevent intricacies in resolving models, it has an inconclusive approach, between two outlooks, of the black box and the network. Hence, in this paper, we consider a three-stage network with additional, desirable and undesirable inputs and outputs and the three abovementioned approaches are analyzed by contemplating on the optimistic and pessimistic views. The goals of this paper are to put together the results of the three mentioned approaches in order to attain the final conclusions. We generalize and use of Wang’s approach for the three levels, with additional inputs and outputs, as well as a heuristic solution to solve the network’s view. Finally, this paper considers a genuine world example, in the form of a dynamic network, for model application and analyzes it from three perspectives.

The efficiency evaluation of a network opens the “black box” and deliberates on the internal structures and innermost interactions of the system. In this paper, we have made efforts to consider a three-stage system, comprising of six sub-DMUs, in combination with additional inputs and undesirable outputs. The proposed models are simulations of a factory, with a production area and three warehouses for goods and two delivery points. Hence, an authentic example, in concern with production planning and inventory control in a factory for a year and within duration of 24 periods, was taken under consideration, as a dynamic structure. In this simulation, all costs are considered, including, production costs, setup cost, maintenance costs of the products, warehouse reservation costs, transportation costs, delay penalty costs and the profit obtained from the sale of products. We utilized the multiplicative DEA with a double-frontier approach to measure the efficiency of a general system and improve the accuracy of efficiencies. Moreover, a heuristic technique was used to convert non-linear models into linear models. The ranking results amongst the 24 time periods, which, indicate that the time periods 24 and 1, are the best and poorest periods in terms of efficiency, respectively. Finally, we suggest to use a k-means method to cluster DMUs into several groups with similar characteristics based on double-frontier Standpoint.

In this paper, we consider a three-stage network comprised of a leader and two followers in respect to the additional desirable and undesirable inputs and outputs. We utilize the non-cooperative approach multiplicative model to measure the efficiency of the overall system and the performances of decision-making units (DMUs) from both, the optimistic and pessimistic views. Moreover, we utilize the concept of a goal programming and define a kind of cooperation between the leader and followers, so that the objectives of the managers are capable of being inserted in the models. In actual fact, a kind of collaboration is considered in a non-cooperative game. The non-cooperative models from these view cannot be converted into linear models. Therefore, a heuristic method is proposed to convert the nonlinear models into linear models. After obtaining the efficiencies based on the double-frontier view, the DMUs are ranked and classified into three clusters by the k-means algorithm. Finally, this paper considers a genuine world example, in relevance to production planning and inventory control, for model application and analyzes it from the double-frontier view. The proposed models are simulations of a factory in a real world, with a production area as leader and a warehouse and a delivery point as two followers. This factory has been regarded as a dynamic network with a time period of 24 intervals.