Antibacterial Activity of Silver Nanoflake (SNF)-Blended Polysulfone Ultrafiltration Membrane.

The aim of this research was to study the possibility of using silver nanoflakes (SNFs) as an antibacterial agent in polysulfone (PSF) membranes. SNFs at different concentrations (0.1, 0.2, 0.3 and 0.4 wt.%) were added to a PSF membrane dope solution. To investigate the effect of SNFs on membrane performance and properties, the water contact angle, protein separation, average pore size and molecular weight cutoffs were measured, and water flux and antibacterial tests were conducted. The antimicrobial activities of the SNFs were investigated using Escherichia coli taken from river water. The results showed that PSF membranes blended with 0.1 wt.% SNFs have contact angles of 55°, which is less than that of the pristine PSF membrane (81°), exhibiting the highest pure water flux. Molecular weight cutoff values of the blended membranes indicated that the presence of SNFs does not lead to enlargement of the membrane pore size. The rejection of protein (egg albumin) was improved with the addition of 0.1 wt.% SNFs. The SNFs showed antimicrobial activity against Escherichia coli, where the killing rate was dependent on the SNF concentration in the membranes. The identified bacterial colonies that appeared on the membranes decreased with increasing SNF concentration. PSF membranes blended with SNF, to a great degree, possess quality performance across several indicators, showing great potential to be employed as water filtration membranes.

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

Multi-item multiperiodic inventory control problem with variable demand and discounts: a particle swarm optimization algorithm.

A multi-item multiperiod inventory control model is developed for known-deterministic variable demands under limited available budget. Assuming the order quantity is more than the shortage quantity in each period, the shortage in combination of backorder and lost sale is considered. The orders are placed in batch sizes and the decision variables are assumed integer. Moreover, all unit discounts for a number of products and incremental quantity discount for some other items are considered. While the objectives are to minimize both the total inventory cost and the required storage space, the model is formulated into a fuzzy multicriteria decision making (FMCDM) framework and is shown to be a mixed integer nonlinear programming type. In order to solve the model, a multiobjective particle swarm optimization (MOPSO) approach is applied. A set of compromise solution including optimum and near optimum ones via MOPSO has been derived for some numerical illustration, where the results are compared with those obtained using a weighting approach. To assess the efficiency of the proposed MOPSO, the model is solved using multi-objective genetic algorithm (MOGA) as well. A large number of numerical examples are generated at the end, where graphical and statistical approaches show more efficiency of MOPSO compared with MOGA.