An improved algorithm for solving a multi-period facility location problem

An exact algorithm for the multi-period facility location problem is proposed that efficiently integrates mixed-integer and dynamic programming methods. Two simplification procedures are introduced to reduce the size of the general multi-period facility location problem substantially. Because the proposed algorithm utilizes dynamic programming to obtain the optimal sequence over the entire planning horizon, many near-optimal solutions also become available that are extremely useful for postoptimality analysis. The solution method is tested and compared with a well-known procedure on several problems with varying conditions. The comparisons appear very promising, and the required CPU times by the proposed method are substantially reduced.     

Fast parallel Preconditioned Conjugate Gradient algorithms for robot manipulator dynamics simulation

In this paper fast parallel Preconditioned Conjugate Gradient (PCG) algorithms for robot manipulator forward dynamics, or dynamic simulation, problem are presented. By exploiting the inherent structure of the forward dynamics problem, suitable preconditioners are devised to accelerate the iterations. Also, based on the choice of preconditioners, a modified dynamic formulation is used to speedup both serial and parallel computation of each iteration. The implementation of the parallel algorithms on two interconnected processor arrays is discussed and their computation and communication complexities are analyzed. The simulation results for a Puma Arm are presented to illustrate the effectiveness of the proposed preconditioners. With a faster convergence due to preconditioning and a faster computation of iterations due to parallelization, the developed parallel PCG algorithms represent the fastest alternative for parallel computation of the problem withO(n) processors.     

Optimal Development of Agricultural Sectors in the Basin Based on Economic Efficiency and Social Equality

The limitation of freshwater resources and the growing demand for water, make the issue of water resource development planning and water allocation among stakeholders even more important. Ideally, water allocation should be economically efficient and socially equitable. In this study, a water allocation model is presented in an integrated framework that considers the interaction of water supply and demand according to economic and social factors. To achieve this, a reliability-based multi-objective optimization - simulation approach has been employed. The objective functions of the problem are: 1) maximizing GDP from agricultural sectors and 2) maximizing social equality in different provinces of the basin (measured using the Williamson coefficient). The fair development and allocation among the shared provinces in the basin can reduce conflicts in the region. Karkheh basin has been considered as a case study and decision variables of the problem are area under cultivation of agricultural development sectors in different provinces. The results show that, without harming the income of the agricultural sector, the spatial distribution of development projects can be done in such a way that equality (according to income level and the number of people working in each province) is achieved. One of the solutions of Pareto front compared to previous studies shows that, in addition to an increase of about 12% of the objective function 1 (GDP), the value of the objective function 2 (Williamson coefficient) decreased from 1.19 to 0.98. This indicates a decrease in income inequality among the provinces of the basin.

     

Ignite-GPU: a GPU-enabled in-memory computing architecture on clusters

The Journal of Supercomputing - During recent years, big data explosion and the increase in main memory capacity, on the one hand, and the need for faster data processing, on the other hand, have...     

Sentic Computing for social media marketing

In a world in which millions of people express their opinions about commercial products in blogs, wikis, fora, chats and social networks, the distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand or organization. Opinion mining for product positioning, in fact, is getting a more and more popular research field but the extraction of useful information from social media is not a simple task. In this work we merge AI and Semantic Web techniques to extract, encode and represent this unstructured information. In particular, we use Sentic Computing, a multi-disciplinary approach to opinion mining and sentiment analysis, to semantically and affectively analyze text and encode results in a semantic aware format according to different web ontologies. Eventually we represent this information as an interconnected knowledge base which is browsable through a multi-faceted classification website.     

Dynamic data reconciliation in mineral and metallurgical plants

Data reconciliation is a well-known technique to improve accuracy and reliability of plant measurements. It relies on process models that could range from simple mass and energy conservation equations to complete causal dynamic models. Generally, precise estimates imply detailed plant models that could be difficult to build and update in practice. The trade-off between modeling efforts and estimation performances has thus lead to various approaches to deal with plant dynamics. The objective of the paper is to review and compare most common observers used for dynamic data reconciliation in the mineral and metallurgical processing industries. Comparisons are carried out using a separation unit and a flotation circuit as simulated benchmark plants. Observer performances are evaluated in terms of variance reduction. Strengths and weaknesses of the different methods are highlighted. Aspects such as estimation of model parameters, detection of gross errors, and handling of bilinear equations and plant non-linearities are discussed.     

Multiobjective invasive weed optimization: Application to analysis of Pareto improvement models in electricity markets

This paper presents a proposal for multiobjective Invasive Weed Optimization (IWO) based on nondominated sorting of the solutions. IWO is an ecologically inspired stochastic optimization algorithm which has shown successful results for global optimization. In the present work, performance of the proposed nondominated sorting IWO (NSIWO) algorithm is evaluated through a number of well-known benchmarks for multiobjective optimization. The simulation results of the test problems show that this algorithm is comparable with other multiobjective evolutionary algorithms and is also capable of finding better spread of solutions in some cases. Next, the proposed algorithm is employed to study the Pareto improvement model in two complex electricity markets. First, the Pareto improvement solution set is obtained for a three-player oligopolistic electricity market with a nonlinear demand function. Then, the IEEE 30-bus power system with transmission constraints is considered, and the Pareto improvement solutions are found for the model with deterministic cost functions. In addition, NSIWO algorithm is used to analyze this system with stochastic cost data in a risk management problem which maximizes the expected total profit but minimizes the profit risk in the market.     

Fabrication of a biocompatible flexible electroosmosis micropump

In in-vivo microsystems, one of the components is a biocompatible micropump in order to produce the necessary force to deliver the fluid from the inlet to the outlet. In this contribution, a flexible micropump is fabricated which is aimed to be suitable in drug delivery applications. It provides high degree of biocompatibility, since the only employed materials are implantation grade polydimethylsiloxane elastomer and gold for the electrical interconnects. The working principle of the micropump is based on transverse DC electroosmosis which is a new variant of conventionally applied high voltage DC electroosmosis. This new technique is based on topography irregularities introduced in the channel resulting in a non-uniform charge distribution. The advantage is to drive the micropump using a relatively low DC voltage of 10 V while getting an effective flow speed of 60 μm/s. In order to characterize the flow speed, dyed 3 μm beads are dispersed in the working fluid and their speed is measured by the line scanning technique using a confocal microscope. It is also observed that the flow has a helical profile which is an attractive feature for an efficient micro-mixer in active microfluidics and μ-TAS applications.     

Intelligent control of static synchronous series compensator via an adaptive self-tuning PID controller for suppression of torsional oscillations

In this paper, an intelligent controller is proposed to control a static synchronous series compensator (SSSC) in order to mitigate subsynchronous resonance (SSR) oscillations in a power system. This intelligent controller is an adaptive self-tuning PID controller. To train the PID controller, the gradient descent method is employed where the learning rate is adapted in every iteration in order to accelerate the speed of convergence. This control scheme also requires a wavelet neural network (WNN) to identify the controlled system dynamic. To update the parameters of WNN, the gradient descent (GD) along with the adaptive learning rates derived by the Lyapunov method is used. The computer simulations are used to show the ability of the proposed controller. In addition, the performance of the proposed controller is compared with another self-tuning PID controller. The results demonstrate that the proposed controller has a successful performance in minimizing the SSR.