Journal of Operational Research in its Applications
Year:2014 | Volume:11 | Issue:1 (40)|Papers Count:9

One of the most important decisions is a problem in supply chain efficiency and effectiveness of this chain with an uncertainty of parameters and variables. Uncertainty in the supply chain leads to non-optimal decisions made with certainty. Hence in this research a model with robust approach and integrated three-stage using for conformity with uncertainty in supply chain, in first stage key factors are identified in supplier selection, next with fuzzy analytical hierarchy process, according to identified criteria of the supplier that obtain minimum score inter robust and two-phase integer multi period multi product stochastic model for allocation of short time demand with a certainty to products. Results show models have high power and lead to conformity demand with uncertainty.

Supplier selection process, the most important variable in the effective management of supply chain network is modern. For achieving high quality products and customer satisfaction, an effective supplier selection models and powerful analytical tools for decision support capability of balancing multiple subjective and objective criteria are needed. It is also worth mentioning that most of the manufacturing organization s with more than one criterion to choose encountered of their own supply. In this article, the AHP approach to prioritization of client characteristics and use of quality models in two homes QFD, a new method is proposed to select the best supplier. Finally, to evaluate the effectiveness of the proposed method, the Zam Zam Company has been exemplified.

For manufacturing industries, the coordination of demand and price decisions with other aspects of the supply chain such as production and distribution is not only useful, but is essential. The coordination of these decisions mean an approach that optimizes the system rather than individual elements, improving both the efficiency of the supply chain and of the firm. In this paper, we present a multi-objective inventory model so that with Maximizing of retailer's profit and the service level with stochastic lead-time, selling price, lot size and reorder point are determined. problem for uniform and exponential lead time are studied. Since pricing is important and influence demand, the demand is considered as a general function of price. Concerning the properties of non-linear model, Genetic Algorithm (GA) is used to find the optimal solutions. Finally, numerical examples with sensitivity analysis of key parameters are presented to illustrate the model.

Nonlinear programming is one of the most common models in optimization which is useful for solving many of the problems in the real world. On the other hand, in formulating engineering problems we face some practical kinds of nonlinear optimization which is called geometric programming. In fact, geometric program is a method for solving nonlinear problems with nonlinear or linear constraints. Many applications of geometric programming are engineering design problems in which some of the problems parameters are estimates of actual values. Intuitively, when the parameters are imprecise values, the derived objective value is imprecise as well. The aim of this paper is to develop a solution procedure for solving geometric programming with fuzzy triangular numbers.

In recent years, use of recommendation systems has been more. Existing challenges in recommendation system datasets are interesting for researchers. Using matrix decompositions and then tensor decompositions in recommendation system algorithms have less error and results in that most of new algorithms use them. In this paper we present a new algorithm that uses tensor decomposition and have three dimensions user-item-time. Our algorithm has better result in comparison with matrix decomposition with 2 dimension user-item. In this method we use time as an independent dimension and 3rd dimension. Experiments show that using time in decompositions has less error.

The main objective of this research is to propose a novel framework to approximate a discrete decision space, using optimization models in order to obtain an exact criterion for comparing the performance of Multi Attribute Decision Making (MADM) models. The framework, which is intact, an integration of qualitative and quantitative approach, is applied in a real world decision problem in context of Iran Insurance Company. Considering a multi attribute decision problem, the approximation method embeds the problem in an optimization problem to evaluate the proportional efficiency of decision alternatives, which can be considered as both output (if the criteria is positive) and input (if the criteria is negative) of the method. To evaluate the decision units' efficiency, an integration of Data Envelopment Analysis (DEA) and Analytical Hierarchy Process (AHP) is utilized. The output of the integrated method is, in fact, an exact solution in a continuous space which can be used as a reliable criterion to compare decision making techniques. Using the weights produced by Monte Carlo Simulation, the performance of decision techniques is evaluated and the best techniques are identified. In the last step of the research, the sensitivity analysis of AHP is examined on the basis of Monte Carlo results. It is shown that AHP is the most appropriate techniques which produce the closest results to DEA.

Nowadays attending to opportunities and threats in the industry and commerce and evaluation ability of industries and corporations envisage to existed risks and uncertainty is necessary and management of supply chain risks is very important. Management of risk is a process of identifying risks, assessment and scheduled for reduction of adverse effects. Risk assessment is one of the most important parts in risk management and according to the many risks and the need to expend optimize resources in the supply chain is very important. Risk assessment and ranking, determined the superiority of risk based on relevant criteria and offered the opportunity to provide the appropriate response for each risk. In this paper we present a comprehensive hierarchical model for risk assessment, identification of the main risks of supply chain based on the risk breakdown structure method and determination of measurements criteria, also a comprehensive questionnaire was developed based on the relative importance of each risk in the steel supply chain and we discussed the Esfahan Steel Company as a case study using taxonomy method as a multi-criteria decision making. Therefore, the risk associated with procurement and suppliers as the most critical risks has been identified in the company.

In this paper, we evaluate the aggregation of Farrell's efficiency indices. Due to this fact that the industry minimum cost is equal to the sum of minimum unit costs, we find conditions on this aggregation. Some conditions are introduced under which the industry efficiency can be obtained from the efficiency of individual firms.

Recently, supply chain management and supplier selection process seem to have attracted many researchers and scholars, and many articles and studies have been done on the evaluation and selection of suppliers. However, the subject of evaluation and selection of distributors have been little of attention. Distributors in the supply chain, not only have relationship with the manufacturer and the final consumer of product or service; also it is the first part that is heard of the customer's voice and we understand changes in demand. So it is necessary to evaluate them. Therefore the primary aim of this paper is performance evaluation of distributors in the supply chain using data envelopment analysis. To this at first related goals and criteria in distributors evaluation were identified. Then, important criteria were identified. In the following, distributors efficiency scores were calculated by using data envelopment analysis. Finally, manner of distributing products between distributors, according to existing goal and maximizing the efficiency in a case study in the auto industry was determined by an integrated model.