Developing a new data envelopment analysis methodology for supplier selection in the presence of both undesirable outputs and imprecise data

Supplier selection plays a key role in an organization because the cost of raw material constitutes the main cost of the final product. Selecting an appropriate supplier is now one of the most important decisions of the purchasing department. This decision generally depends on a number of different criteria. The objective of this paper is to propose a data envelopment analysis methodology that considers both undesirable outputs and imprecise data simultaneously. The proposed model is applied in supplier selection problem. A numerical example demonstrates the application of the proposed method.     

Simultaneous sensing and actuation with a piezoelectric tube scanner

Piezoelectric tube scanners with quartered external electrodes are the most widely used nanopositioning technology in modern scanning probe microscopes. There has been increasing interest in utilizing feedback control techniques to improve bandwidth and accuracy of these nanopositioners. The use of feedback requires a sensor to be incorporated into the nanopositioning device. Noncontact displacement sensors, e.g., capacitive and inductive sensors, have been used for this purpose. However, their measurements contain a significant noise component if operated over large bandwidths. The piezoelectric voltage induced in a tube nanopositioner has been proposed recently as an alternative measure of displacement with a much improved noise figure, up to three orders of magnitude better than capacitive sensors. In this arrangement, an electrode is used to actuate the tube, while the opposite electrode is used as a sensor. This approach has two drawbacks: (i) the operating range of the tube is reduced to half and (ii) the tube is not driven symmetrically, thus the opposite sides of the tube experience asymmetric stresses, i.e., in this mode of operation, the scanner is not a perfectly collocated system. In this paper, we present a new electrode pattern for piezoelectric tube scanners which addresses the above problems and allows simultaneous sensing and actuation of the tube in an efficient way.     

An optimization method for radial forging process using ANN and Taguchi method

In this study, the artificial neural network (ANN) and the Taguchi method are employed to optimize the radial force and strain inhomogeneity in radial forging process. The finite element analysis of the process verified by the microhardness test (to confirm the predicted strain distribution) and the experimental forging load published by the previous researcher are used to predict the strain distribution in the final product and the radial force. At first, a combination of process parameters are selected by orthogonal array for numerical experimenting by Taguchi method and then simulated by FEM. Then the optimum conditions are predicted via the Taguchi method. After that, by using the FEM results, an ANN model was trained and the optimum conditions are predicted by means of ANN (using genetic algorithm as global optimization procedure) and compared with those achieved by the Taguchi method. The optimum conditions are verified by FEM, and good agreement is found between the two sets of results.     

An improved mixed integer linear formulation and lower bounds for minimizing makespan on a flow shop with batch processing machines

This paper considers a flow shop scheduling problem with batch processing machines. Each batch processing machine has a limited capacity and can process a group of jobs, each of them having a different known capacity requirement, simultaneously. Job processing time on each machine is known and arbitrary. The processing time of a batch on each machine is the longest processing time of all jobs in the batch. We improve the only existing mixed integer linear formulation (MILF) of the problem through significant reduction in size complexity of the model. Results justify that the improved MILF is clearly more efficient in reducing the required time for obtaining optimal makespan of small-size problems, in comparison with the existing MILF. Motivated by relaxing variety of the problem assumptions, several valid lower bounds on the optimal makespan are also proposed that can furtheraccelerate obtaining optimal solution through proposed MILF. Robustness evaluation of each bound under the different problem settings is reported through computations.     

Flow shop scheduling with two batch processing machines and nonidentical job sizes

A batch processing machine can process several jobs simultaneously. In this research, we consider the problem of a two-stage flow shop with two batch processing machines to minimize the makespan. We assume that the processing time of a batch is the longest processing time among all the jobs in that batch and the sizes of the jobs are nonidentical. There is a limitation on batch sizes and the sum of job sizes in a batch must be less than or equal to the machine capacity. Since this problem is strongly nondeterministic polynomial time hard, we propose two heuristic algorithms. The first one is knowledge-based and the other is based on the batch first fit heuristic proposed previously. To further enhance the solution quality, two different simulated annealing (SA) algorithms based on the two constructive heuristics is also developed. Since heuristic methods for this problem has not been proposed previously, a lower bound is developed for evaluating the performance of the proposed methods. Several test problems have been solved by SAs and lower bound method and the results are compared. Computational studies show that both algorithms provide good results but the first SA (ARSA) algorithm considerably outperforms the second one (FLSA). In addition, the results of ARSA algorithm, optimal solutions, and lower bounds are compared for several small problems. The comparisons show that except for one instance, the ARSA could find the optimal solutions and the proposed lower bound provides small gaps comparing with the optimal solutions.     

New dispatching rules to minimize rejection and tardiness costs in a dynamic flexible flow shop

This paper studies a flexible flow shop system considering dynamic arrival of jobs and the ability of acceptance and rejection of new jobs. The problem objective is to determine a schedule that minimizes sum of the tardiness and rejection costs of jobs. A 0–1 mixed integer model of the problem is formulated. Since this problem class is NP-hard, four dispatching rules have been developed to solve the problem approximately. Moreover, a discrete event simulation model of the flexible flow shop system is developed for the purpose of experimentation. Four dispatching rules from the literature and four new dispatching rules proposed in this paper are incorporated in the simulation model. Simulation experiments have been conducted under various experimental conditions characterized by factors such as shop utilization level, due date tightness and number of stages in flexible flow shop. The results indicate that proposed dispatching rules provide better performance under problem assumptions.     

Defocus estimation from stroboscopic cryo-electron microscopy data

Defocus estimation is an important step for improving the resolution of single particle reconstructions. It can be troublesome to estimate the defocus from low-dose cryo-electron microscopy (cryo-EM) data, particularly if there is not sufficient contrast present in the Fourier transform of the micrograph. Most existing approaches estimate the defocus from the presence of Thon rings within the power spectrum, employing image enhancement techniques to highlight these rings. In this paper, an approach to estimating the defocus from a stroboscopic image series is described. The image series is used to obtain two statistical metrics: figure of merit (FOM) and Q-factor. These metrics have been used to estimate the defoci from low-dose stroboscopic cryo-EM data consisting of a variable number of images.     

The harp: a vehicle crash test apparatus for full-scale crash test experiments

The current paper describes an apparatus for full-scale vehicle crash test experimentation. This apparatus is referred to as the harp. In brief, the harp may either accelerate a trolley which is impacted into a test vehicle or the test vehicle itself may be accelerated and impacted into an object such as a barrier, a pole, or another vehicle. If a trolley is accelerated, it is equipped with load cells to record the axial crushing force. If a test vehicle is accelerated, it is equipped with a three-axis accelerometer to record the crushing force. At the impact site, high-speed cameras and instrumentation record vital data during the crash.     

Automatic diagnosis of neuro-degenerative diseases using gait dynamics

Here an approach for the diagnosis of neuro-degenerative diseases based on gait dynamics is proposed. The proposed method uses information from a time series of stride intervals, swing intervals, stance intervals and double support intervals of stride-to-stride measures of footfall contact times using force-sensitive resistors. Different features were extracted from these time series and the best of them were selected for the diagnosis. The support vector machines using different kernels were examined for the diagnosis. The radial basis function kernel obtained the best performance for this aim. The results show that features derived from double support intervals are common effective features for the diagnosis of neuro-degenerative diseases using the gait dynamics.