INTERNATIONAL JOURNAL OF APPLIED OPERATIONAL RESEARCH
Year:2013 | Volume:3 | Issue:4|Papers Count:8

This paper presents an algorithm with the help of branch and bound approach for the two stage flow shop scheduling problem including transportation time and weightage of jobs, processing times are associated with their respective probabilities. Our main objective is to find the optimal/ near optimal sequence of jobs in order to minimize the total elapsed time. An algorithm is clarified with the help of a numerical example.

The guaranty of power system stability during random disturbance requires systematically a wide knowledge of the disturbance components in one hand and their range of variation in the other hand. The major problem lies in the approach taken to the identification of the main components of this type of disturbances. Control strategy will only be effective if these disturbances are acceptably modeled.

In this article, we consider a two commodity continuous review perishable inventory system with a finite number of homogeneous sources of demands. The maximum storage capacity of Si units for the ith commodity (i=1,2). The life time of items of each commodity is assumed to be exponentially distributed with parameter gi (i=1,2). The time points of primary demand occurrences form independent quasi random distributions each with parameter li (i=1,2) A joint reordering policy is adopted with a random lead time for orders with exponential distribution. When the inventory position of both commodities are zero, any arriving primary demand enters into an orbit. The demands in the orbit send out signals to compete for their demand which is distributed as exponential. We assume that the two commodities are both way substitutable. The joint probability distribution for both commodities and number of demands in the orbit is obtained for the steady state case. Various system performance measures are derived, and the results are illustrated with numerical examples.

In this study an artificial neural network was developed to predict output the energy and GHG emission of open field (OF) and greenhouse (G) strawberry production system. Data were randomly collected from OFs and Gs in Guilan province of Iran. For both systems the best models included an input layer, two hidden layers with hyperbolic tangent algorithm and an output layer with linear hyperbolic tangent algorithm. The structures of 11-6-10-2 and 13-7-6-2 were selected as the best topologies for OF and G production systems, respectively. These topologies had the least root mean square errors (RMSE) and mean absolute errors (MAE).

The process of supply chain operations, while the presence of the bullwhip effect and anti-bullwhip effect, taking into account only the cost of the supply chain, demand deviation, this presence makes the bullwhip effect due to the limited rationality driven weakening the anti-bullwhip effect strengthened so that the upstream and downstream in the supply chain cycle will show the combined effects of the weakening of the bullwhip effect and strengthening the anti-bullwhip effect, the combined effects of individual enterprises to achieve the low average inventory costs also means that the whole supply chain inventory minimum and the overall market demand for the lowest deviation, indirectly, automatically eliminated from the overall bullwhip effect or anti-bullwhip effect, making the whole supply chain whether short-term or long-term point of view is the best long-term, but also to supply chain bring significant strategic advantage.

This paper focuses on the determine congestion with production trade-offs or, alternatively, through weights restrictions in data envelopment analysis (DEA). For this purpose, the researchers review a one-model approach to evaluate congestion Cooper et al., [1], and then briefly review the calculation of efficient targets with production trade-offs in Podinovski's procedure [2]. Therefore, the method used in this study is a hybrid of the two above-said procedures: calculation of the congestion via weight restrictions, which is then supported by an experimental example by Podinovski [3].

In this paper, we consider linear degradation in the case of multiple failure modes and obtain the graph of survival function, traumatic failure probability function, natural failure probability function by simulating data. We compare traumatic failure probability function, natural failure probability function and survival probability in different sample sizes. Also sample path is simulated by using noise.

In the most utilization of Data Envelopment Analysis (DEA), available models yield efficiency score corresponding to each Decision Making Unit (DMU). Indeed, in classical models, a DMU had its own input and outputs, and these data had direct effect on the corresponding unit. But in the model we're examining, some inputs are used for some components in common, and the whole components are used to make some outputs. However, in most of real cases, efficiency is a function of various shared resources in decision making units. In this article, the efficiency four Banks in six periods has been examined and indices in question. This article includes debt ratio, flow of capital resources ratio, average period of collection check of banks and capital efficiency. So the components of capital structure, profitability and growth have been known like the components that use the specific and common indices. At last, the relative efficiency of units has been calculated financially. In this article to calculate the efficiency of a decision making unit four components have been used until the specific efficiency of single component has been determined individually, and its efficiency without the effect of other indices have been found. Finally, the aggregate efficiency of all components has been recognized. In this method we focus on finding the inefficient components in an efficient DMU, and also we can exactly determine which of the inefficient components makes a DMU inefficient.  To continue, the preface of data envelopment analysis, ratios and methods used in banking and the models of multi components efficiency with shared resource will be presented. Finally, performing of this model will be discussed in Iranian banks.