s. e. najafi

Data Envelopment Analysis (DEA) is a non-parametric mathematical programming technique widely used for evaluating the relative efficiency of homogeneous decision making units (DMUs) that use multiple inputs to produce multiple outputs. The DMUs may consist of several sub-processes that interact and perform various operations. The conventional DEA treats DMUs as "black boxes" and internal structure of the DMU is not taken into consideration. The Network DEA (NDEA) methods are capable of reflecting accurately the DMUs’ internal structure and considered the DMU as a network of interconnected sub-units. On the other hand, many real-world applications face with uncertain data which the optimal solutions of models may even become infeasible and the ranking of DMUs can be invalid. Robust DEA (RDEA) is the last uncertain DEA approach that is applied for performance assessment of DMUs in the presence of uncertain data.In this study we propose a new Robust Network DEA (RNDEA) model. We calculate the efficiency of this model by considering a series of intermediate measures and robust constraints. We present a case study in the dairy industry with three series stages to exhibit the efficacy of the approach and demonstrate the applicability of the proposed model.

Contracts have been used for coordination in many supply chain alliances among businesses. Because bilateral contracts are significantly more successful and profitable than uni-contracts, In this article, the issues of implementing bilateral contracts are investigated with the approach of game theory and government intervention to increase bilateral interaction between members of co-production and co-distribution in the supply chain. By adopting the game theory model between these two members of the chain and intervention government, this research seeks to increase production and distribution by making maximum use of the excess capacity of production and distribution in the chain. In this way, the producer uses his surplus capacity in two ways: one is produced directly by the producer and enters the market by the distributor, and the other is an order that the distributor gives to the producer, which is different from the product that the producer produces. It is produced directly and given by the distributor. The purpose of this research is to investigate and analyze the amounts and profits resulting from the participation of production and distribution with government intervention in the supply chain. According to this research, governments should provide an environment for supply chain members to have more cooperation with each other because, in the case of cooperation among supply chain members, the profits of the chain and the members will increase.

In some lateral alliances, firms coordinate their interactions in Supply Chain Management (SCM) via contracts. Successful implementation of contracts in lateral alliances remains challenging in practice because of the incomplete identification of implementation barriers by firms involved in the alliance. This paper investigates the implementation issues of lateral contracts. To identify the barriers, the literature and interview experts on the subject matter are reviewed. By adopting the novel Fuzzy Measurement of Alternatives and Ranking according to the Compromise Solution (FMARCOS) prioritization method, we evaluate the main barriers that firms face in the successful implementation of contracts discovered in our identification phase. A sensitivity analysis is conducted to demonstrate the stability and robustness of our proposed method. To check the reliability of the proposed model, a case study is solved with three methods of Multi-Criteria Decision-Making (MCDM) methods. The results show that they do not differ much from each other, which indicates the validity of this model. To validate the findings, a list of barriers is applied to assess a set of firms in the Iranian car industry, and more prepared firms are located as future partners of potential lateral alliances. The results are consistent with the common intuition toward these sample firms in the case study. The main contributions of this work include the application of the FMARCOS method in the study of the bidirectional implementation barriers, the consideration of novel aspects of implementation barriers unaddressed in the extant literature, and a real-case study in the Iranian car industry.

Benchmarking is a tool for evaluating organizational performance with a learning approach from others. The importance of benchmarking in every industry is clear for anyone. In the automotive industry, the performance of after-sales service agencies in Iran is evaluated every year by Iran Standard and Quality Inspection company. One of the ways to continuously improve in after-sales service agencies is benchmarking of successful and efficient examples in the network. In this paper, a benchmarking model is developed considering that the repair index and customer satisfaction are interdependent. To improve the accuracy and operationality of benchmarking, some constraints have been added to the model with the opinion of experts. Considering the dependent parameters, a data envelopment analysis model has been proposed and this model has been implemented to benchmark 20 after-sales service agencies of a car company. By solving the model and comparing it with the results of the original model, it was observed that the considered conditions changed the benchmarking and increased the accuracy. This paper discusses the concept of the impact and importance of dependent parameters in benchmarking, and with this concept, a benchmarking model for automotive after-sales service agencies is presented.

In this paper, a novel method is proposed to predict the cost of short-term hourly electrical energy based on combined neural networks. In this method, the influential parameters that play a key role in the accuracy of these systems are identified and the most prominent ones are selected. Due to the fluctuations of electricity prices during various seasons and days, these parameters do not adhere to the same pattern. In the proposed method, initially, using the SOM network, similar days are placed in close clusters. In the next stage, the temperature parameter and prices pertaining to similar days are trained separately in two MLP neural networks because of their differences concerning the range of changes and their nature. Finally, the two networks are merged with another MLP network. In the proposed hybrid method, an evolutionary search method is used to provide an appropriate initial weight for neural network training. Given the price data changes, the price amidst the previous hour has a significant effect on the prediction of the current state. In this vein, in the proposed method, the predicted data in the previous hour is considered as one of the inputs of the next stage.

In this paper, a new method of combining ANN and DEA (ANN-DEA) presented in which the input and output values for a large number of DMUs determined as neural network inputs. We have also compared the new model with the existing approach of ANN-DEA. To illustrate the ability of the proposed methodology some case studies are used, including a set of 500 Iranian bank branches.

In recent years, many industries in developed countries have integrated the important process of reverse logistics into their supply chain for different reasons, including growing environmental concerns. Given fish as perishable food, re-employing unused products and waste in each step of the chain constitute a major concern for the decision-makers. The present study is conducted to maximize responsiveness to customer demand and minimize the cost of the fish closed-loop supply chain (CLSC) by proposing a novel mathematical model. To solve this model, the epsilon-constraint method and Lp-metric were employed. Then, the solution methods were compared with each other based on the performance metrics and a statistical hypothesis. The superior method is ultimately determined using the TOPSIS method. The model application is tested on a case study of the trout CLSC in the north of Iran by performing a sensitivity analysis of demand. This analysis showed the promising results of using the proposed solution method and model.

In this paper, a three-stage network with optimal desirable and undesirable inputs and outputs has been taken into consideration by us. This network comprises of a leader and two followers. Four diverse models of Data Envelopment Analysis (DEA) to measure the efficiency or the performance, of this three-stage network have been taken under contemplation; these are namely, a Black Box Model and three Stackelberg Game (Theory) Models. A multiplicative DEA, with a double-frontier approach, to measure the efficiency of the entire system and the performances of the decision making units (DMUs), from both the optimistic and pessimistic views have been utilized. In this paper attempts have been made to present the goals of the managers in the models. Hence, aspects of goal programming have been manipulated so as to define cooperation between the leader and followers, such that, we are able to include the objectives of the managers in the models. In actual fact, a non-cooperative collaboration is deliberated upon. In addition to which, in the second and third scenarios, the leader-follower, nonlinear models are present. Thereby, a heuristic approach is suggested to convert the nonlinear models into linear ones.

Given the complexities of the electricity market, various factors, such as uncertainties, the ways upon which the markets are set, how the debts are settled, the market structure and regulations, production prices, constraints governing the units and networks, etc. are influential in determining the optimal pricing strategies. Various methods and models have been presented to resolve the pricing issue in the competitive electricity industry. The most prominent of which include pricing methods are based on the prediction of competitors’ behavior; also pricing methods based on the forecasts of market price, methods based on the game theory and lastly, pricing methods based on the intelligent algorithms. Therefore, this study was conducted to provide an optimal strategy in order to forecast the electricity market price set in the competitive Iranian electricity market (based on the data collected). In this paper, the proposed method uses a compound network based on the neural networks. The analyzed data include the amount of the consumed energy as well as temperature (if applicable) and the price set for the past days and weeks. The self-organizing map (SOM) network was used for the input clustering based on the similar days. A number of multilayer perceptron (MLP) neural networks were used to combine the extracted data consisting of the energy levels, the price set, and temperature (if possible). The results showed improvements in the performance of the smart systems based on the neural networks in predicting the electricity prices.