Additive Manufacturing of Hard Magnetic Passive Shims to Increase Field Homogeneity of a Halbach Magnet

Obtaining a highly homogeneous magnetic field is desired for field-controlled applications. For example, the resolution of magnetic analysis methods can be improved by generating a stronger and more homogeneous field over the region of interest (ROI). A set of 3D-printed passive shims is fabricated using additive manufacturing to improve the magnetic field homogeneity of a Halbach magnet assembly. The feedstock is a custom acrylonitrile butadiene styrene (ABS)-hard magnet composite filament filled with 60% wt. isotropic NdFeB. Additionally, a method for investigating the remanence is developed and validated. The result reveals a good agreement between the new method and existing measurement techniques for the remanence of permanent magnets. It is also shown that the additive manufacturing procedure has negligible effects on the magnetic properties. Performing a parametric study over a rectangular ROI, an optimized shim configuration is achieved. In the optimized and 3D-printed configuration, the average norm of the magnetic flux density, B_norm, is increased by 13% and, more importantly, a 43% increase in the magnetic uniformity is obtained. These results highlight the great potential of freeform manufacturing, namely, additive manufacturing, to tailor the properties of magnet structures.     

Estimation of magnitude and time duration of temporary overvoltages using ANN in transmission lines during power system restoration

In most countries, the main step in the process of power system restoration, following a complete/partial blackout, is energization of primary restorative transmission lines. Artificial neural network (ANN) is employed for performing a nonlinear input–output mapping in this work, in order to estimate the temporary overvoltages (TOVs) due to transmission lines energization. In the proposed methodology, Levenberg–Marquardt second order method is used to train the multilayer perceptron. Proposed ANN is trained with equivalent circuit parameters of the network as input parameters, trained ANN has therefore satisfactory generalization capability. Both single and three-phase line energizations are analyzed. The simulated results for 39-bus New England test system, indicate that the proposed technique can estimate the peak values and duration of switching overvoltages with acceptable accuracy.     

A hybrid method for flowshops scheduling with condition-based maintenance constraint and machines breakdown

One of the most important assumptions in production scheduling is that the machines are permanently available without any breakdown. In the real world of scheduling, machines can be made unavailable due to various reasons such as preventive maintenance and unpredicted breakdown. In this paper, we explore flowshop configuration under the assumption of condition-based maintenance to minimize expected makespan. Furthermore, we consider a condition-based maintenance (CBM) strategy which could be used in most industrial settings. The proposed algorithm is designed for non-resumable flowshop state where the processing of jobs after preventive maintenance is restarted from the beginning. We propose a hybrid algorithm based on genetic algorithm and simulated annealing. Additionally, we conduct an extensive parameter calibration with the utilization of Taguchi method and select the optimal levels of the algorithm’s performance influential factors. The preliminary results indicate that the proposed method provides significantly better results compared with other high performing algorithms in the literature.     

A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations

A resource investment problem with discounted cash flows (RIPDCF) is a project-scheduling problem in which (a) the availability levels of the resources are considered decision variables and (b) the goal is to find a schedule such that the net present value of the project cash flows optimizes. In this paper, the RIPDCF in which the activities are subject to generalized precedence relations is first modeled. Then, a genetic algorithm (GA) is proposed to solve this model. In addition, design of experiments and response surface methodology are employed to both tune the GA parameters and to evaluate the performance of the proposed method in 240 test problems. The results of the performance analysis show that the efficiency of the proposed GA method is relatively well.     

Multi-Verse Optimizer: a nature-inspired algorithm for global optimization

This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at http://www.alimirjalili.com/MVO.html.     

Impacts in multibody systems: modeling and experiments

This paper outlines a novel approach to the modeling and analysis of impact involving multibody systems. This approach is based on an analysis of energy absorption and restitution during impact, using a decomposition of the kinetic energy, which decouples the parts associated with the spaces of admissible and constrained motions of the underlying unilateral constraints. Such a decomposition turns out to be useful in the analysis of energy dissipation during impact, and leads to a generalized definition of the energetic coefficient of restitution, which targets particularly collisions in multibody systems. The applicability of the approach reported is investigated by conducting an experimental study on a robotic testbed. It is shown that impact between multibody systems is considerably affected not only by the local dynamics characteristics of the interacting bodies, but also the configuration of the whole multibody system. The results reported here show that our decomposition can offer a sound characterization of impact in several problems of multibody systems.     

Multi-product multi-chance-constraint stochastic inventory control problem with dynamic demand and partial back-ordering: A harmony search algorithm

In this paper, a multiproduct inventory control problem is considered in which the periods between two replenishments of the products are assumed independent random variables, and increasing and decreasing functions are assumed to model the dynamic demands of each product. Furthermore, the quantities of the orders are assumed integer-type, space and budget are constraints, the service-level is a chance-constraint, and that the partial back-ordering policy is taken into account for the shortages. The costs of the problem are holding, purchasing, and shortage. We show the model of this problem is an integer nonlinear programming type and to solve it, a harmony search approach is used. At the end, three numerical examples of different sizes are given to demonstrate the applicability of the proposed methodology in real world inventory control problems, to validate the results obtained, and to compare its performances with the ones of both a genetic and a particle swarm optimization algorithms.     

Optimal pricing, lot-sizing and marketing planning in a capacitated and imperfect production system

In the classical economic production quantity (EPQ) problem demand is considered to be known in advance. However, in the real-world, demand of a product is a function of factors such as product’s price, its quality, and marketing expenditures for promoting the product. Quality level of the product and specifications of the adopted manufacturing process also affect the unit product’s cost. Therefore, in this paper we consider a profit maximizing firm who wants to jointly determine the optimal lot-sizing, pricing, and marketing decisions along with manufacturing requirements in terms of flexibility and reliability of the process. Geometric programming (GP) technique is proposed to address the resulting nonlinear optimization problem. Using recent advances in optimization techniques we are able to optimally solve the developed, highly nonlinear, mathematical model. Finally, using numerical examples, we illustrate the solution approach and analyze the solution under different conditions.     

Genetic application in a facility location problem with random demand within queuing framework

In many service and industrial applications of the facility location problem, the number of required facilities along with allocation of the customers to the facilities are the two major questions that need to be answered. In this paper, a facility location problem with stochastic customer demand and immobile servers is studied. Two objectives considered in this problem are: (1) minimizing the average customer waiting time and (2) minimizing the average facility idle-time percentage. We formulate this problem using queuing theory and solve the model by a genetic algorithm within the desirability function framework. Several examples are presented to demonstrate the applications of the proposed methodology.