An optimization-based heuristic for the machine reassignment problem

In this paper, we introduce a generalization of the vector bin packing problem, where the bins have variable sizes. This generalization can be used to model virtual machine placement problems and in particular to build feasible solutions for the machine reassignment problem. We propose several families of greedy heuristics for this problem and show that they are flexible and can be adapted to handle additional constraints. We present structural properties of the machine reassignment problem, that allow us to decompose it into smaller subproblems and adapt our heuristics to them. We evaluate our heuristics on academic benchmarks of the vector bin packing problem, a randomly generated vector bin packing problem with heterogeneous bins benchmark as well as Google’s realistic instances of the machine reassignment problem.

A decision support system for production scheduling in an ion plating cell

It is known that neighbourhood structures affect search performance. In this study we analyse a series of neighbourhood structures to facilitate the search. The well known steepest descent (SD) local search algorithm is used in this study as it is parameter free. The search problem used is the Google Machine Reassignment Problem (GMRP). GMRP is a recent real world problem proposed at ROADEF/EURO challenge 2012 competition. It consists in reassigning a set of services into a set of machines for which the aim is to improve the machine usage while satisfying numerous constraints. In this paper, the effectiveness of three neighbourhood structures and their combinations are evaluated on GMRP instances, which are very diverse in terms of number of processes, resources and machines. The results show that neighbourhood structure does have impact on search performance. A combined neighbourhood structures with SD can achieve results better than SD with single neighbourhood structure.

This research is motivated by a real-life hybrid flowshop scheduling problem where jobs are organized in families according to their machine settings and tools. This type of problem is common in the production process of standard metal components. The problem is complicated by the requirement of family setup time when a machine changes from processing one job family to another and the formation of job families varies in different stages. This problem has been previously solved with a non-delay scheduling heuristic in which no machine is kept idle. This research illustrates that inserting intentional idle time into a non-delay schedule can further reduce the total setup time as well as makespan. With the inserted idle time, the non-delay schedules are extended to active schedules. This paper presents a mechanism to determine the locations and lengths of intentional idle times in the efficient active schedules. Four active scheduling approaches are developed by integrating two types of waiting factor operators into two non-delay approaches. Computational experiments have been conducted to compare the proposed active scheduling approaches in terms of effectiveness and efficiency. The results have shown that the proposed active scheduling approaches are superior to non-delay scheduling. The analysis of variance has been applied on the factors related to scheduling environment, problem size and scheduling approach. The analysis has identified factors that are most influential on the scheduling result.

This paper presents a manufacturing model of a flat-end mill using a five-axis computer numerical control (CNC) grinding machine. The profile of the helical groove can be precisely calculated using a given wheel profile and the relative movements between the workpiece and the grinding wheel. The results of the calculation of the numerical control (NC) data for all the grinding processes of the end mill are fully presented in the paper. The NC data for the machining end mill were generated from the developed program by using the given design parameters of tool geometry, wheel geometry, wheel setting, and machine setting. The effects of the design and manufacturing of the end mill were analyzed based on the computer simulation and experiment results. This study provides a practical and efficient model for the manufacturing of an end mill using a CNC grinding machine with simple programming techniques. The prediction of the end mill geometry for the designing of the proper configuration of the end mill before manufacturing and generating the NC code for machining in a CNC machine is necessary to save time and to reduce the manufacturing cost.

Computer experiments are very useful for improving vehicle designs. Construction of computer experiments is of crucial importance. There are many space filling designs for computer experiments. The uniformity and low-discrepancy sets have played an important role in the construction of designs for computer experiments. To gain insight into why space-filling designs have good performance in computer experiments, in this paper, we compare several statistical models from different statistical viewpoints. We further demonstrate that the uniform design is an optimal one from several aspects.

This paper presents computer simulation of a field oriented controlled doubly-–ed induction machine (DFIM) drive system. Functions and simulation model of this system are given. Necessity of the gap flux open loop control is discussed. The damping characteristics of flux during variations of both power mains and machine load is simulated. Compensation method for increasing damping speed of flux oscillation is proposed. Both simulation and test operation show that the DFIM drive has a good performance which fulfill requirements of some high dynamic, low cost applications.

This paper studies an integrated scheduling problem which consists of both production and transportation stages. Jobs are processed on a set of parallel batch-processing machines in the production stage, and then transported to a target customer in the transportation stage. The aim is to find an optimal integrated production and transportation schedule with the objective to minimize the weighted sum of total delivery time and total transportation cost with a relative preference α. A 0-1 mixed integer programming model is then constructed. To solve the proposed model, which is NP-hard, a developed GA is proposed to produce near-optimal solutions for large-size instances. Computational experiments on 720 instances with up to 150 jobs and 5 parallel batch-processing machines show the effectiveness and efficiency of the developed GA.

This paper presents a novel deformable model for automatic segmentation of prostates from three-dimensional ultrasound images, by statistical matching of both shape and texture. A set of Gabor-support vector machines (G-SVMs) are positioned on different patches of the model surface, and trained to adaptively capture texture priors of ultrasound images for differentiation of prostate and nonprostate tissues in different zones around prostate boundary. Each G-SVM consists of a Gabor filter bank for extraction of rotation-invariant texture features and a kernel support vector machine for robust differentiation of textures. In the deformable segmentation procedure, these pretrained G-SVMs are used to tentatively label voxels around the surface of deformable model as prostate or nonprostate tissues by a statistical texture matching. Subsequently, the surface of deformable model is driven to the boundary between the tentatively labeled prostate and non-prostate tissues. Since the step of tissue labeling and the step of label-based surface deformation are dependent on each other, these two steps are repeated until they converge. Experimental results by using both synthesized and real data show the good performance of the proposed model in segmenting prostates from ultrasound images.

Numerical estimation of the size of the kidney is useful in evaluating conditions of the kidney, especially, when serial MR imaging is performed to evaluate the kidney function. This paper presents a new method for automatic segmentation of the kidney in three-dimensional (3D) MR images, by extracting texture features and statistical matching of geometrical shape of the kidney. A set of Wavelet-based support vector machines (W-SVMs) is trained on the MR images. The W-SVMs capture texture priors of MRI for classification of the kidney and non-kidney tissues in different zones around the kidney boundary. In the segmentation procedure, these W-SVMs are trained to tentatively label each voxel around the kidney model as a kidney or non-kidney voxel by texture matching. A probability kidney model is created using 10 segmented MRI data. The model is initially localized based on the intensity profiles in three directions. The weight functions are defined for each labeled voxel for each Wavelet-based, intensity-based, and model-based label. Consequently, each voxel has three labels and three weights for the Wavelet feature, intensity, and probability model. Using a 3D edge detection method, the model is re-localized and the segmented kidney is modified based on a region growing method in the model region. The probability model is re-localized based on the results and this loop continues until the segmentation converges. Experimental results with mouse MRI data show the good performance of the proposed method in segmenting the kidney in MR images.

This paper describes continuous piecewise linear (CPWL) programming where the objective and constraints are in the form of hinging hyperplane (HH). And HH has received wide attention due to its simplicity and good performance in system identification. When solving a CPWL programming problem, some excellent features inspire us to come up with more efficient algorithms: the two distinguished states of a hinge function reminds us of application of genetic algorithm, while the piecewise linearity and concavity of the problem of minimization of HH naturally lead to the usage of well developed methods for concave programming, such as the cutting plane method. In order to find the global minima, we propose an improved genetic algorithm (GA) incorporating the cutting plane method. The main improvement lies in three aspects. First, it utilizes binary strings that derive local minima as chromosomes, with the proposed local minima locating method. Second, a stopping criterion has been established to ensure the global optimality of GA, with the structure information provided by γ extension of local minima. And third, genetic operations have also been revised to enhance the performance of the algorithm, which is assessed by the computational experiments.

In data mining, graph mining is a promising new approach and some algorithms are proposed. However, their application is limited in the field of business. This is because of the wide diversity of business data. In this paper, we propose a method which extracts new valuable patterns by using graph mining approach and MOEA. In our method, historical purchasing data for each customer is transformed into tree structured data and gene is constructed from the structured data at first. Then the patterns are extracted by using existing MOEA from these genes. We apply our proposed method to a practical business data. From computational experiments, we show that our method has good performance and is able to extract valuable patterns from the view of business.

Chinese National Emergency Warehouse Location Research based on VNS Algorithm

Dynamic simulation of reversible solid-fluid reactions in nonisothermal porous spheres with Stefan-Maxwell diffusion

Flexible field-effect transistors from a liquid crystalline semiconductor by solution processes