A hybrid electromagnetism algorithm for multi-depot periodic vehicle routing problem

Electromagnetism algorithm is a meta-heuristic proposed to derive approximate solutions for computationally hard problems. In the literature, several successful applications have been reported for graph-based optimization problems, such as scheduling problems. This paper presents a novel hybrid electromagnetism algorithm called SA_EM to solve the multi-depot periodic vehicle routing problem (MDPVRP). The main feature of the hybrid algorithm is to hybridize the solution construction mechanism of the electromagnetism (EM) with simulated annealing (SA). Moreover, during implementing the hybrid algorithm, cyclic transfers, an effective class of neighborhood search is applied. The objective consists of two terms as follows: total traveled distance at each depot and total waiting time of all customers to take service. Distances are assumed Euclidean or straight line. These conditions are exactly consistent with the real-world situation and have little attention in the literature. Finally, the experimental results have shown that the proposed hybrid method is competitive to solve the vehicle routing problem compared with the best existing methods in terms of solution quality.     

A clustering-based modified variable neighborhood search algorithm for a dynamic job shop scheduling problem

The dynamic job shop scheduling (DJSS) problem occurs when some real-time events are taken into account in the ordinary job shop scheduling problem. Most researches about the DJSS problem have focused on methods in which the problem’s input data structure and their probable relationship are not considered in the optimization process while some useful information can be extracted from such data. In this paper, the variable neighborhood search (VNS) combined with the k-means algorithm as a modified VNS (MVNS) algorithm is proposed to address the DJSS problem. The k-means algorithm as a cluster analysis algorithm is used to place similar jobs according to their processing time into the same clusters. Jobs from different clusters are considered to have greater probability to be selected when an adjacent for a solution is made in an optimization process using the MVNS algorithm. To deal with the dynamic nature of the problem, an event-driven policy is also selected. Computational results obtained using the proposed method in comparison with VNS and other common algorithms illustrate better performance in a variety of shop floor conditions.     

Constrained grinding optimization for time,cost, and surface roughness using NSGA-II

Selection of parameters in machining process significantly affects quality, productivity, and cost of a component. This paper presents an optimization procedure to determine the optimal values of wheel speed, workpiece speed, and depth of cut in a grinding process considering certain grinding conditions. Experimental studies have been carried out to obtain optimum conditions. Mathematical models have also been developed for estimating the surface roughness based on experimental investigations. A non-dominated sorting genetic algorithm (NSGA II) is then used to solve this multi-objective optimization problem. The objectives under investigation in this study are surface finish, total grinding time, and production cost subjected to the constraints of production rate and wheel wear parameters. The Pareto-optimal fronts provide a wide range of trade-off operating conditions which an appropriate operating point can be selected by a decision maker. The results show the proposed algorithm demonstrates applicability of machining optimization considering conflicting objectives.     

Investigation on the resistance spot-welded austenitic/ferritic stainless steel

In this work, the effect of weld current on joining capability of austenitic stainless steel (AISI 304) and ferritic stainless steel (AISI 430) sheets with application of resistance spot welding process was investigated. Macrostructure, microstructure, microhardness, tensile shear strength, and failure mode of welded materials were evaluated for different weld currents. The values of weld current were 2.5, 3.75, and 5 kA. It was found that when the weld current increased, the nugget size and the weld strength were increased. Two distinct failure modes including interfacial and pullout were observed during tensile shear test. Finally, an adequate weld current was obtained.     

Optimisation of probiotic yoghurt production enriched with phytosterols

To optimise the formulation of phytosterol‐enriched probiotic yoghurt, the effect of different concentrations of phytosterols, fat and the probiotic inoculation rate on the survival of Lactobacillus acidophilus LA‐5, Bifidobacterium lactis BB‐12 and physicochemical indices over 14 days of storage was explored. Data analysis showed that the phytosterol concentration and fat content were two crucial factors affecting probiotic viability. Addition of phytosterols had no adverse effect on the overall scores for sensory attributes. Regarding maximisation of viability and storage time, the optimum conditions were found to be fat = 8.07% (w/v), phytosterol = 18 g/L, probiotic inoculation rate = 71.42 mg/L and storage time = 12 days.     

Fabrication,characterization and evaluation of the effect of PLGA and PLGA–PEG biomaterials on the proliferation and neurogenesis potential of human neural SH-SY5Y cells

In recent years with regard to the development of nanotechnology and neural stem cell discovery, the combinatorial therapeutic strategies of neural progenitor cells and appropriate biomaterials have raised the hope for brain regeneration following neurological disorders. This study aimed to explore the proliferation and neurogenic effect of PLGA and PLGA–PEG nanofibers on human SH-SY5Y cells in in vitro condition. Nanofibers of PLGA and PLGA–PEG biomaterials were synthesized and fabricated using electrospinning method. Physicochemical features were examined using HNMR, FT-IR, and water contact angle assays. Ultrastructural morphology, the orientation of nanofibers, cell distribution and attachment were visualized by SEM imaging. Cell survival and proliferation rate were measured. Differentiation capacity was monitored by immunofluorescence staining of Map-2. HNMR, FT-IR assays confirmed the integration of PEG to PLGA backbone. Water contact angel assay showed increasing surface hydrophilicity in PLGA–PEG biomaterial compared to the PLGA substrate. SEM analysis revealed the reduction of PLGA–PEG nanofibers' diameter compared to the PLGA group. Cell attachment was observed in both groups while PLGA–PEG had a superior effect in the promotion of survival rate compared to other groups (p < .05). Compared to the PLGA group, PLGA–PEG increased the number of Ki67⁺ cells (p < .01). PLGA–PEG biomaterial induced neural maturation by increasing protein Map-2 compared to the PLGA scaffold in a three-dimensional culture system. According to our data, structural modification of PLGA with PEG could enhance orientated differentiation and the dynamic growth of neural cells.     

Concurrent impact of chipping and pitting damages in a locomotive power transmission system

Journal of Mechanical Science and Technology - The reliability of power transmission systems in locomotives directly impacts trains’ availability and running safety. The failure of gears,...     

Optimization of Output Power and Thermal Efficiency of Solar‐Dish Stirling Engine Using Finite Time Thermodynamic Analysis

This paper presents an investigation on finite time thermodynamic (FTT) evaluation of a solar‐dish Stirling heat engine. FTTs has been applied to determine the output power and the corresponding thermal efficiency, exergetic efficiency, and the rate of entropy generation of a solar Stirling system with a finite rate of heat transfer, regenerative heat loss, conductive thermal bridging loss, and finite regeneration process time. Further imperfect performance of the dish collector and convective/radiative heat transfer mechanisms in the hot end as well as the convective heat transfer in the heat sink of the engine are considered in the developed model. The output power of the engine is maximized while the highest temperature of the engine is considered as a design parameter. In addition, thermal efficiency, exergetic efficiency, and the rate of entropy generation corresponding to the optimum value of the output power is evaluated. Results imply that the optimized absorber temperature is some where between 850 K and 1000 K. Sensitivity of results against variations of the system parameters are studied in detail. The present analysis provides a good theoretical guidance for the designing of dish collectors and operating the Stirling heat engine system.     

A critical review of ash slagging mechanisms and viscosity measurement for low-rank coal and bio-slags

Gasification or combustion of coal and biomass is the most important form of power generation today. However, the use of coal/biomass at high temperatures has an inherent problem related to the ash generated. The formation of ash leads to a problematic phenomenon called slagging. Slagging is the accumulation of molten ash on the walls of the furnace, gasifier, or boiler and is detrimental as it reduces the heat transfer rate, and the combustion/gasification rate of unburnt carbon, causes mechanical failure, high-temperature corrosion and on occasions, superheater explosions. To improve the gasifier/combustor facility, it is very important to understand the key ash properties, slag characteristics, viscosity and critical viscosity temperature. This paper reviews the content, compositions, and melting characteristics of ashes in differently ranked coal and biomass, and discusses the formation mechanism, characteristics, and structure of slag. In particular, this paper focuses on low-rank coal and biomass that have been receiving increased attention recently. Besides, it reviews the available methodologies and formulae for slag viscosity measurement/prediction and summarizes the current limitations and potential applications. Moreover, it discusses the slagging behavior of different ranks of coal and biomass by examining the applicability of the current viscosity measurement methods to these fuels, and the viscosity prediction models and factors that affect the slag viscosity. This review shows that the existing viscosity models and slagging indices can only satisfactorily predict the viscosity and slagging propensity of high-rank coals but cannot predict the slagging propensity and slag viscosity of low-rank coal, and especially biomass ashes, even if they are limited to a particular composition only. Thus, there is a critical need for the development of an index, or a model or even a measurement method, which can predict/measure the slagging propensity and slag viscosity correctly for all low-rank coal and biomass ashes.