JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL
Year:2011 | Volume:7 | Issue:15|Papers Count:8

In today’s business transactions, it is frequently observed that a customer is allowed some grace period (permissible delay in payment) before settling the account with the supplier or producer.This policy is advantageous both for the supplier and customer since supplier attracts more customers and customer does not have to pay any interest during this fixed period either. In this paper, the researchers generalize Goyal’s model (1985) with permissible delays in payment depending on the ordered quantity and shortage is the combination of backlogged and lost sales. The researchers then establish a proper mathematical model, and propose an algorithm to solve model easily. Finally, a numerical example is given to illustrate the algorithm and the theoretical results.

This paper uses integrated Data Envelopment Analysis (DEA) models to rank all extreme and non-extreme efficient Decision Making Units (DMUs) and then applies integrated DEA ranking method as a criterion to modify Genetic Algorithm (GA) for finding Pareto optimal solutions of a Multi-Objective Programming (MOP) problem. The researchers have used ranking method as a shortcut way to modify GA to decrease the iterations of GA. The modified algorithm reduces the computational efforts to find Pareto optimal solutions of MOP problem and can be used to find Pareto optimal solutions of MOP with convex and non-convex efficient frontiers. An example is given to illustrate the modified algorithm.

Computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting models proposed in several past decades, it is widely recognized that exchange rates are extremely difficult to forecast. Artificial Neural Networks (ANNs) are one of the most accurate and widely used forecasting models that have been successfully applied for exchange rate forecasting. In this paper, a hybrid model is proposed based on the basic concepts of artificial neural networks in order to yield more accurate results than the traditional ANNs in short span of time situations. Three exchange rate data sets—the British pound, the United States dollar, and the Euro against the Iran rial-are used in order to demonstrate the appropriateness and effectiveness of the proposed model. Empirical results of exchange rate forecasting indicate that hybrid modelis generally better than artificial neural networks and other models presented for exchange rate forecasting, in cases where inadequate historical data are available. Therefore, our proposed model can be a suitablealternative model for financial markets to achieve greater forecasting accuracy, especiallyin incomplete data situations.

Remanufacturing is an industrial process that makes used products reusable. Remanufacturing is a way to establish a closed-loop supply chain. One of the important aspects in both reverse logistics and remanufacturing is pricing of returned and remanufactured products (called cores) that it has been noticed in this paper. In addition, in this paper the researchers have tried to present a mathematical model that indicates prices and inventories in a closed-loop supply chain in an exclusive market. This model has argued on acquisition price of cores and remanufactured cores. Also, in the following the researchers essay discuss about acquisition price of cores in the competitive market via fuzzy rules. Numerical results demonstrate that appropriate values of the prices are obtained by these models.

This paper presents an approach composed from Data Envelopment Analysis (DEA) model and a multivariate statistical method, Principle Component Analysis (PCA), considering undesirable input and output variables. PCA is used to improve discrimination power of DEA and making variables as independent as possible to avoid overlapping of Decision Making Units (DMU’s) information. The advantage of the proposed approach is considering undesirable output and input variables simultaneously in PCA-DEA method; furthermore it was applied for performance assessment of different province’s road safety level in Iran.

Supplier selection is one of the most critical activities of purchasing management in supply chain and managers increasingly face sourcing decisions of how to selected suppliers. This paper illustrates the development of a sourcing decision that provides support for the buyer firm in supply chain.The models developed here, involved selecting between single and dual sourcing. Outside and local suppliers are considered in our model. To encourage the buyer for purchasing from the local supplier, he gives the credit period to the buyer that is determined according to the partner’s opportunity costs. In this paper first the researchers model and explain this problem. Then the model is solved and critical decision values are identified. These are the base values used for sourcing selection. Finally, numerical examples are solved to show the model and illustrate numerical sensitivity analysis.

Determination a sequence of extracting ore is one of the most important problems in mine annual production scheduling. Production scheduling affects mining performance especially in a polymetallic open pit mine with considering the imposed operational and physical constraints mandated by high levels of reliability in relation to the obtained actual results. One of the important operational constraints for optimization is the uniformity of metallic minerals grade after the blending process. This constraint directly affects the performance of the mineral processing plant. The sequence of extracting ore is usually determined by the order of pushbacks, which should be mined. Metallic minerals’ grade in each pushback is stochastic in nature that comes from some statistical errors committed by the sampling. In such situations, decision making about the order of pushbacks for extraction as an exact defined process is not possible. Moreover, the decision-maker should maximize the total Net Present Value NPV as the main objective of mining operations by considering the high performance of mineral processing plant. To deal with such cases, this research proposes a model based on the chance-constrained one-sided goalprogramming and the obtained results from this procedure confirms the model’s reliability and correctness.

The Traveling Salesman Problem (TSP) is one of the most important and famous combinational optimization problems that aim to find the shortest tour. In this problem, the salesman starts to move from an arbitrary place called depot and after visiting all nodes, finally comes back to depot. Solving this problem seems hard despite of program statement is simple and leads this problem belonging to NP-hard problems. In this paper, the researchers present a modified Elite Ant System (EAS) which is different from common EAS. There is a linear function used here for increasing coefficient pheromone of the best route activated when a better solution is achieved. This process avoids the premature convergence and makes better solutions. The results on several standard instances show that this new algorithm gains more efficient solutions compared to other algorithms.