Solving a bi-objective cell formation problem with stochastic production quantities by a two-phase fuzzy linear programming approach

We propose a bi-objective cell formation problem with demand of products expressed in a number of probabilistic scenarios. To deal with the uncertain demand of products, a framework of two-stage stochastic programming model is presented. The proposed model considers minimizing the sum of the miscellaneous costs (machine constant cost, expected machine variable cost, cell fixed-charge cost, and expected intercell movement cost) and expected total cell loading variation. Because of conflicting objectives, we develop a two-phase fuzzy linear programming approach for solving bi-objective cell formation problem. To show the effectiveness of the proposed approach, numerical examples are solved and the results are compared with the two existing approaches in the literature. The computational results show that the proposed fuzzy method achieves lower objective functions as well as higher satisfaction degrees.     

A latency-aware task scheduling algorithm for allocating virtual machines in a cost-effective and time-sensitive fog-cloud architecture

The Journal of Supercomputing - Recently, with the expansion of communications and generated data, the need for processing this high volume of data in minimum time and maximum speed has increased....     

Cyclic scheduling of a robotic flexible cell with load lock and swap

In this paper, we study the problem of robotic cell scheduling with m machines with flexibility, load lock and swap assumptions. The robotic cell repetitively produces parts of identical types. We determine the cycle time of all 1-unit cycles in this type of robotic cell and present two new lower bounds for robot move cycles with load lock and swap, either there is flexibility or inflexibility. We also provide a new robot move cycle and prove that it dominates all classical robot move cycles considered in the existing literature of m-machine robotic cells.     

In situ solvent formation microextraction based on a new ionic liquid for green preconcentration of trace amount of Cu (II) ions in water samples

A simple, fast, and effective analytical technique known as in situ solvent formation microextraction was used to preconcentrate/separate trace amounts of Cu(II) ions in water samples prior to determination by flame atomic absorption spectroscopy. In the present method, 6,6′-(1Z,1′Z)(butane-1,4-diylbis(azan-1-yl-1-ylidene)bis(methan-1-yl-1-ylidene)bis-3-bromophenol (Schiff base ligand) as the complexing agent and 1-methyl-3-pentylimidazolium bromide (ionic liquid) as an extracting agent were successfully synthesized and characterized by FTIR, C-NMR, and H-NMR spectroscopies. The effects of several analytical parameters on the method were studied and optimized, and the merits of the method, such as LDR (0.2–1000 µg L^?1), LOD (0.12 µg L^?1), RSD (4.1%), and preconcentration factor (70) were evaluated.     

Effect of mixer geometry and operating conditions on flow mixing of shear thinning fluids with yield stress

Flow mixing of a non‐Newtonian fluid in a stirred tank equipped with a side‐entry impeller was observed using particle image velocimetry (PIV). The effects of some geometrical parameters including the mixer shape and impeller type and position on the flow pattern were studied on velocity fields obtained at different locations inside the mixing domain. The different flow structures revealed that the ratio of inertial and viscous forces largely defines the flow pattern. Dead zones were observed inside the tank due to the rheological properties of the fluid. The size of the dynamic regions and the average velocity near the impeller were enhanced by increasing the suction area. Likewise, large pitch ratios were found to improve the active mixing zone and the axial discharge. Curves for the power and pumping numbers are reported for different axial flow impellers. © 2013 American Institute of Chemical Engineers AIChE J, 60: 1156–1167, 2014     

Investigation of well test behavior in gas condensate reservoir using single-phase pseudo-pressure function

Gas condensate reservoirs present complicated thermodynamic behavior when pressure falls below the dew point pressure, due to fluid dropout and change in the fluid composition. Condensate blockage in the near wellbore region reduces the well deliverability. Mixture composition change in the reservoir makes the interpretation of well tests in gas condensate reservoirs a serious challenge. In this study, at first the capillary number effect and Non-Darcy Flow on compositional simulation of gas condensate reservoirs were investigated and then well test analysis was carried out. The main objective of this work was to examine gas condensate well test analysis using single-phase gas pseudo-pressure and radial composite model assuming capillary number effect and Non-Darcy Flow. For this purpose some fluid samples were selected and results compared. Results indicate that estimation of reservoir properties below the dew point is in good agreement with actual input, particularly for lean fluid samples.     

A software framework for model predictive control with GenOpt

There is a growing interest in integrated control strategies for building systems with numerous responsive elements, such as solar shading devices, thermal storage and hybrid ventilation systems, both for energy efficiency and for demand response. Model predictive control is a promising way of approaching this challenge. This paper presents a flexible software framework for model predictive control using GenOpt, along with a modified genetic algorithm developed for use within it, and applies it to a case study of demand response by zone temperature ramping in an office space. Various areas for further research and development using this framework are discussed.     

Multiobjective optimization of building design using TRNSYS simulations,genetic algorithm,and Artificial Neural Network

Building optimization involving multiple objectives is generally an extremely time-consuming process. The GAINN approach presented in this study first uses a simulation-based Artificial Neural Network (ANN) to characterize building behaviour, and then combines this ANN with a multiobjective Genetic Algorithm (NSGA-II) for optimization. The methodology has been used in the current study for the optimization of thermal comfort and energy consumption in a residential house. Results of ANN training and validation are first discussed. Two optimizations were then conducted taking variables from HVAC system settings, thermostat programming, and passive solar design. By integrating ANN into optimization the total simulation time was considerably reduced compared to classical optimization methodology. Results of the optimizations showed significant reduction in terms of energy consumption as well as improvement in thermal comfort. Finally, thanks to the multiobjective approach, dozens of potential designs were revealed, with a wide range of trade-offs between thermal comfort and energy consumption.     

圆形封闭水箱非稳态数值模拟

采用数值方法对圆形封闭水箱内放置两根加热管的二维流动传热进行了非稳态数值模拟,探讨了Ra数从104到107四种情况和封闭水箱的加热管不同摆放位置下的流动和换热;对流场和温度场进行了分析,讨论了不同情况下加热管平均努赛尔数(Nuavg)的变化情况。数值模拟结果表明,随着Ra数增大水箱内的流动表现出了很大变化,决定了传热是导热或者是对流;两个水平加热管在水箱存在最佳的放置距离,这样可以使自然对流情况下的换热达到最好。