نشریه تصمیم گیری و تحقیق در عملیات
سال دوم شماره 3 (زمستان 1396)

Uncertainty is one of the most important factors which affect transportation models. As the value of most of the parameters in real-word problems are not clear, this paper represent a cost-based transportation problem with type-2 fuzzy parameters. Applying possibility theory, the fuzzy objective function and fuzzy constraints are formulated by a credibility measure. In addition, type-2 fuzzy variables are crisped using possibillistic critical value reduction method, in order to convert the main model into two mixed-integer sub-models which are solvable by a parametric programming approach. A numerical example including crisp demand and cost values but fixed and variable probability distributions is solved by the proposed approach. The results prove the effectiveness and flexibility of the proposed approach.

Armero and Bayarri get Bayesian estimation from traffic intensity in M/M/1 model in 1994. Sharma and Kumar get Bayesian and classical estimations of different parameters of M/M/1 model under loss function in 1999. Furthermore, use of classical methods to estimate unknown parameters of previous distribution is suggested by Mises for the first time in 1943. In this paper, Bayesian estimation and empirical Bayes of traffic intensity parameter are assessed in the M/M/1 queuing model. Estimation of the parameters of this model is presented by methods of Bayes, likelihood, and moment. The characteristics and applications of both estimators are discussed in numerical results. The quadratic theory has many uses in communication theory, computer design, etc. The statistical deduction in quadratic process and quadrant process estimation, such as rate of entry, service rates and traffic jams, has attracted researchers in the past few years. Suppose that the M / M / 1 queue system with an average log rate λ, as well as an average service rate of 1 / μ

While the Grid systems are distributing geographically, the heterogeneity and dynamic features of their resources are increasing. One of the important issues in this systems is adaptation the discovery services with more scalable and dynamic environments to improve the discovery performance. In this paper, we concentrate on the Decentralized mechanisms based on the Peer-to-Peer (P2P) networks and classify the discovery approaches into two main categories: the Unstructured and Super-Peer models. We review the major development of these categories and provide discussion about the efficiency, scalability, and dynamic terms.

In recent years, a new approach which has been dominant over operation management is the “supply chain management approach”. Supply chain management has attracted most researcher's attentions in recent years. This is the way to improve the economic, social and environmental performance. Therefore, the evaluation of SSCM is an important task for all types of organizations. Among the methods of evaluation SSCM, it seems that data envelopment analysis (DEA) is an appropriate approach. Some of decision-making units composed of several sections or stages that make a network of sub-processes. In order to evaluate such units, data envelopment analysis (DEA) is applied. This paper presents two approaches to calculate supply chain management as a network process. In the first approach, we consider the generalization of weighted sum model to calculate efficiency and returns to scale (RTS) of supply chain with two-stage process as single-objective approach. In the second one, we introduce the weighted sum model to calculate efficiency and returns to scale (RTS) of supply chain with two-stage process as multi-minded approach, so we try to make it to be single-objective approach and then calculate the efficiency of its total production process, according to the decision-maker's ideas and interests. In the next section of this paper according to the two above approaches, we determine the percent of returns to scale (RTS) of supply chain. Suggested ideas are used to evaluate the efficiency and returns to scale (RTS) of supply chain in resin production companies.

In this paper, solution space of interval linear programming (ILP) models that is a NP-hard problem, has been considered. In all of the solving methods of the ILP, feasibility condition has been only considered. Best-worst case (BWC) is one of the methods for solving the ILP models. Some of the solutions obtained by the BWC may result in an infeasible space. To guarantee that solution is completely feasible, improved two-step method (ITSM) is proposed. By using a new approach, we introduce a space for solving ILP models in which by two tests, feasibility and optimality of the obtained space has been guaranteed.