Although lot streaming scheduling is an active research field, lot streaming flexible flow lines problems have received far less attention than classical flow shops. This paper deals with scheduling jobs in lot streaming flexible flow line problems. The paper mathematically formulates the problem by a mixed integer linear programming model. This model solves small instances to optimality. Moreover, a novel artificial bee colony optimization is developed. This algorithm utilizes five effective mechanisms to solve the problem. To evaluate the algorithm, it is compared with adaptation of four available algorithms. The statistical analyses showed that the proposed algorithm significantly outperformed the other tested algorithms.

An integrated analysis methodology composed of four components: visual modeling, simulation, diagnosis and reduction of bottleneck processes of production lines has been presented in this paper. Various components are integrated to analyze the bottleneck process of production lines including process parameters and production planning. In this method, 3D dynamic modeling, which includes physical and logical modeling, is the basis to define the entire layout, the visual simulation and the diagnosis of the production line. Simulation is the most crucial part for acquiring many data to quickly diagnose the bottleneck processes in the production line. The reducing method of bottleneck processes, which includes two anti-bottleneck ways, local expansion and bypass reducing, is the premise to redesign the production line. Because the result of each following steps can be fed back to the previous steps, the bottleneck processes of the production line can be quickly diagnosed and reduced. With this method, an existing production line is virtually modeled in a computer, simulated and analyzed. Based on the simulation results, the bottleneck processes are quickly diagnosed. Some ways for reducing bottleneck processes are proposed and their simulation results are quickly achieved in the simulation environment. Results reveal that the integrated analysis method can integrate the following functions: modeling, simulation, bottleneck diagnosis and reduction of production lines, and as a result quickly diagnose the bottleneck processes, redesign the production line, and provide a preanalysis tool for the production plan.

Background and Aim: When using improper recording materials during the fabrication of dentures, the whole process could be compromised in spite of the precision administered during the rest of the procedure. The aim of this study was to compare the flow of Pars Wax, Azardent, Crystal and Asia Wax with the Dental modelling Wax Detry (Manufactured in Dentsply) according to standard indices of ADA, specification no. 24.Materials and Methods: All flow tests were conducted in accordance with the corresponding ADA specifications. Two samples of each wax (10 mm wide x 6 mm heigh) were prepared. A force of 19.6N (2kg) was applied on each specimen at three different temperatures: 23, 37 and 45°C. The geometric changes were accurately measured with a micrometer and compared to those obtained prior to the tests. The ratios of the dimensions after force application to the initial dimensions were determined. Based on these ratios, wax flows were compared to standard indices. Results: The amount of flow at 23°C and 45°C, obtained for all studied waxes was in accordance with the corresponding standard values, proposed by ADA. However, at 37°C the flow of none of the studied waxes was in the range of the ADA standards.Conclusion: Considering that the tested waxes demonstrated conflicting results at different temperatures when compared with the standard values, further investigation using different batch numbers is suggested.

Due to the stochastic nature of wind energy, allocating an appropriate investment incentive for wind generation technology (WGT) is a complicated issue. We propose an improvement on the traditional incentive, known as capacity payment mechanism (CPM), to reward the wind generators based on their performance exogenously affected by the wind energy potential of the location where the turbines are installed, and therefore, lead the investments towards locations with more generation potential. In CPM, a part of investment cost of each generator is recovered through fixed payments. However, in our proposal, wind generators are rewarded according to dynamic forecasts of the wind energy potential of the wind farm where they are located. We use an auto-regressive moving average (ARMA) model to forecast the wind speed fluctuations in long-term while capturing the auto-correlation of wind velocity variation in consecutive time intervals. Using the system dynamics (SD) modelling approach a competitive electricity market is designed to examine the efficiency of the proposed incentive. Performing a simulation analysis, we conclude that while a fixed CPM for wind generation can decrease the loss of load durations and average prices in long-term, the proposed improvement can provide quite similar results more efficiently.

This paper presents a simple and practical method for measurement and compensation of odometry errors. For a typical differential drive mobile robot, the three dominating systematic error sources unequal wheel diameters, imprecise average wheel diameter and the uncertainty about the effective wheel base, are considered. Odometric accuracy of the mobile robot calibrated with this procedure is considerably improved. Calibration coefficients are obtained by accurate measurement of wheel diameter ratios and the effective wheel base, and applied to robot through drivers controller software. The new method is distinct from previous studies performed in this subject in three aspects: -The experimental text are easily performed. -The method is valid regardless of errors magnitude, which is direct consequence of mathematics with no approximation in the modeling procedure. -Accurate relations to determine average wheel diameters that are additional outcome of this method.

Six male sterile with four male fertile sorghum, Sorghum bicolor(L.) Moench, lines were crossed in a factorial mating design in 1998. Twenty four F1 hybrids and ten parental lines were planted in a randomized complete block design in three replications, at Seed and Plant Improvement Institute, Karaj, Iran in 1999. Two cuttings forage were harvested from all genotypes (F12 and parents). Four traits including plant height, tiller no., green yield and dry matter were measured. There were significant differences among genotypes, cuttings and genotype X cutting interaction for all traits at 1% level of probability. Analysis of variance showed that, there are significant effects of genotypes, parent, parent vs. crosses (except for tiller no. at average of cuttings), lines (for plant height in 2nd cut and mean), testers and line tester interaction. There were differnces among lines and testers GCA which shows the importance of additive effects of genes for this traits, therefore, the selection methods can be used for breeding programmes. Heretiability of some traits were different in first, second and average of cuttings. Therefore, in each cutting, the heretiability should be considered in multi cutting sorghum lines. Significant differences were observed among SCA of some parents which shows the importance of dominance effects of genes for majority of traits in hybrids. Hence the hybridization method can be used for breeding programmes. The highest positive SVA was obtained for green fodder from crossing of A__ICS 84 × R__2 , A__ICS88005× R__1, A__ICS31×R__28 & A__1×R__112, and for dry matter from crossing of A__ICS88005×R__1, A__ICS__31×R__28, A__2×R__112, A__ISC84×R__112& A__ICS31× R__2 at average of two cutting. The highest yield of two cuttings was obtained for green fodder from crossing of A__ICS84×R__2 and for dry matter from crossing of A__ICS31×R__2 with 144.8 and 34.5 tha^-1 , respecticely.

In recent years, artificial neural networks (ANNs) have become one of the most promising tools in order to model complex hydrological processes such as the rainfall-runoff process. In many studies, ANNs have demonstrated superior results compared to alternative methods. ANNs are able to map underlying relationship between input and output data without prior understanding of the process under investigation. However, they have been known as black-box models due to their problem in providing insight into the relationship learned. In this study, firstly we develop a rainfall-runoff model using an ANN approach, and secondly we describe different approaches including Neural Interpretation Diagram, Garson’s algorithm, and randomization approach to understand the relationship learned by the ANN model. The results indicate that ANNs are promising tools not only in accurate modelling of complex processes but also in providing insight from the learned relationship, which would assist the modeller in understanding of the process under investigation as well as in evaluation of the model.