Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization

This paper presents a novel modified interactive honey bee mating optimization (IHBMO) base fuzzy stochastic long-term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level and reduction in voltage deviation are considered simultaneously as the objective functions. First, these objectives are fuzzified and designed to be comparable with each other. Then, they are introduced into an IHBMO algorithm in order to obtain the solution which maximizes the value of integrated objective function. The output power of DERs is scheduled for each load level. An enhanced economic model is also proposed to justify investment on DER. An IEEE 30-bus radial distribution test system is used to illustrate the effectiveness of the proposed method.     

Multi-wall carbon nanotubes supported molybdenyl acetylacetonate: Efficient and highly reusable catalysts for epoxidation of alkenes with tert-butyl hydroperoxide

Efficient epoxidation of olefins catalyzed by MoO₂(acac)₂ supported on amines functionalized MWCNTs is reported. The MWCNTs bearing carboxylic acid groups were modified with 2-aminophenol and 2-aminothiophenol. These amine–MWCNTs act as bidentate ligand for attachment of Mo catalyst. These catalysts were characterized by elemental analysis, scanning electron microscopy, FT-IR and diffuse reflectance UV–Vis spectroscopic methods. The prepared catalysts were used for efficient epoxidation of different alkenes such as cyclic and linear ones with tert-butyl hydroperoxide in refluxing 1,2-dichloroethane. These heterogeneous catalysts can be reused several times without significant loss of their catalytic activity.     

Development of a simulation-based optimisation for controlling operation allocation and material handling equipment selection in FMS

Flexible manufacturing system (FMS) is described as a set of computerised numerical controlled machines, input–output buffers interconnected by automated material handling devices. This paper develops a bi-objective operation allocation and material handling equipment selection problem in FMS with the aim of minimising the machine operation, material handling and machine setup costs and maximising the machine utilisation. The proposed model is solved by a modified chaotic ant swarm simulation based optimisation (CAS²O) while applying pre-selection and discrete recombination operators is surveyed a capable method to simulate different experiments of FMS problems. A test problem is selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method to solve the FMS scheduling problem.     

Modelling of multi-period multi-product production planning considering production routes

The multi-period multi-product (MPMP) production planning problems, generally, deal with matching production levels of individual products with fluctuated demands over planning horizon. The conventional MPMP optimisation models suffer from insufficient utilisation of available capacity of machines. This fallacy is due to inappropriate formulation of machine capacity and material handling constraints. In this study, a novel mathematical model is proposed to simultaneously optimise production quantities and provide information about managerial decisions such as subcontracting, carrying inventory/backordering, and also hiring/layoff personnel. The problem is then formulated as a mixed integer linear programming (MILP) model by applying appropriate linearisation of non-linear components. The objective is to minimise production costs comprising of production, storage, shortage, subcontracting costs and costs associated with hiring/dismissing labourers. Superiority of the proposed model over existing ones, has been initially evaluated by solving the case presented by Byrne and Bakir [Byrne, M.D. and Bakir, M.A., 1999. Production planning using a hybrid simulation-analytical approach. International Journal of Production Economics, 59 (1), 305–311], and then evaluated by comparing the results obtained from solving both the proposed and the conventional MPMP production planning models using a 100-randomly-generated-test-problem.     

Non-dominated ranked genetic algorithm for a multi-objective mixed-model assembly line sequencing problem

The increasing market demand for product variety forces manufacturers to design mixed-model assembly lines (MMAL) on which a variety of product models similar to product characteristics are assembled. This paper presents a method combining the new ranked based roulette wheel selection algorithm with Pareto-based population ranking algorithm, named non-dominated ranking genetic algorithm (NRGA) to a just-in-time (JIT) sequencing problem when two objectives are considered simultaneously. The two objectives are minimisation the number of setups and variation of production rates. This type of problem is NP-hard. Various operators and parameters of the proposed algorithm are reviewed to calibrate the algorithm by means of the Taguchi method. The solutions obtained via NRGA are compared against solutions obtained via total enumeration (TE) scheme in small problems and also against four other search heuristics in small, medium and large problems. Experimental results show that the proposed algorithm is competitive with these other algorithms in terms of quality and diversity of solutions.     

Graph-neural network approach in cellular manufacturing on the basis of a binary system

In the past several years, many studies have been carried out on cellular manufacturing. Group technology is a manufacturing philosophy in which similar parts are identified and grouped together to take advantage of their similarities in manufacturing and design. The main problem in the development of cellular manufacturing is that of cell formation. In this paper, a graph-neural network approach is given for cell formation problems in group technology. Effort has been made to develop an algorithm that is more reliable than conventional methods. A graph-neural network has the advantages of fast computation and the ability to handle large scale industrial problems without the assumption of any parameter and the least exceptional elements in the presence of bottleneck machines and/or bottleneck parts. Two examples from the literature have been solved to demonstrate the advantages of the algorithm.     

A DEA approach for comparing solution efficiency in U-line balancing problem using goal programming

Line balancing problem plays an important role in the decision making process to increase efficiency and productivity. Recently, U-shaped layout in many production lines has replaced the traditional straight line layout using just-in-time concept. Here, we propose a model, using multi-objective decision making approach to the U-shaped line balancing problem, to offer enhanced decision maker flexibility, by allowing for conflicting goals. The assembly line operation efficiency is the most significant aim in our study, and this efficiency relates to management of resources and the solution of line balancing problem. First, the U-shaped line balancing problem is solved considering the model's goals. Then, the index function of assembly line balancing is determined and the efficiencies of the optimal solution outputs are evaluated using data envelopment analysis (DEA). Finally, the discrimination weakness and distribution of illogical weight in simple DEA models are resolved using a mixed method.     

Thermal behavior and entropy generation rate analysis of a viscous flow in MHD micropumps

Studying the effects of various parameters on the behavior of velocity, temperature and thus the entropy generation rate in the microfluidic systems to reduce loss power is very important. Minimization of entropy generation in the flow system enables us for the parametric optimization of the MHD micropumps operation. In the present study, a transient, laminar and fully developed electrically conductive fluid flow in MHD micropumps has been investigated and the temperature distribution and effects of dimensionless influencing parameters on the entropy generation rate has been presented. Pumping operator in MHD micropumps are the Lorentz forces, which is produced as a result of the interaction between magnetic and electric fields. Governing equations have been solved numerically using finite-difference (ADI) method. The results of simulation have shown good agreement with analytical results by ei-genfunction expansion method. In addition, the results are compared with experimental data from literature which confirms the accuracy of the model. The obtained results showed that aspect ratio, Hartman, Prandtl, Eckert numbers and Joule heating parameter have significant influences on the flow and temperature distribution as well as entropy generation rate in MHD micropumps that controlling them can lead us for optimized operation of MHD micropumps.

     

Influence of Growth Conditions on Magnetite Nanoparticles Electro-Crystallized in the Presence of Organic Molecules

Magnetite nanoparticles were synthesized by electrocrystallization in the presence of thiourea or sodium butanoate as an organic stabilizer. The synthesis was performed in a thermostatic electrochemical cell containing two iron electrodes with an aqueous solution of sodium sulfate as electrolyte. The effects of organic concentration, applied potential and growth temperature on particle size, morphology, structure and magnetic properties were investigated. The magnetite nanoparticles were characterized by X-ray diffraction, electron microscopy, magnetometry and Mössbauer spectrometry. When the synthesis is performed in the presence of sodium butanoate at 60 °C, a paramagnetic ferric salt is obtained as a second phase; it is possible to avoid formation of this phase, increase the specific magnetization and improve the structure of the oxide particles by tuning the growth conditions. Room-temperature magnetization values range from 45 to 90 Am²kg⁻¹, depending on the particle size, type of surfactant and synthesis conditions. Mössbauer spectra, which were recorded at 290 K for all the samples, are typical of nonstoichiometric Fe_3−δO₄, with a small excess of Fe³⁺, 0.05 ≤ δ ≤ 0.15.