Unidirectional Loop Facility Layout Optimization Design Based on Niche Genetic Algorithm

The Facility layout of flexible manufacturing workshop is designed under process principle, which is suitable for medium and small batch production．However, large WIP is produced under this facility layout model, and the material transport is relatively disorderly, the production cost is caused very high. Aiming on the problem, the facility layout with variety of materials is researched, and the mathematical model of lowest logistics cost is established with the total logistics cost as the objective function. Finally, according to the workshop equipment layout optimization process, the simulation mode in unit is established with the computer simulation software eM-Plant, and the logistics cost is optimized and compared with the initial result.

Learning Classifier Systems (LCS) evolve IF-THEN rules for classification and control tasks. The earliest Michigan-style LCS used a panmictic Genetic Algorithm (GA) (in which all rules compete for selection) but newer ones tend to use a niche GA (in which only a certain subset of rules compete for selection at any one time). The niche GA was thought to be advantageous in all learning tasks, but recent research suggests it has difficulties when the rules composing the solution overlap. Furthermore, the niche GA's effects are implicit, making it difficult study, and fixed, which prevents tuning its performance. Given these issues, we set out on a long-term project to reevaluate the niche GA. This work is our starting point and in it we address the implicit and unquantified effects of the niche GA by building a mathematical model of the probability of rule selection. This model reveals a number of insights into the components of rule fitness, particularly the bonus for rule generality and penalty for overlaps, both previously unquantified. These theoretical results are our primary contribution. However, to demonstrate one way to apply this theory, we then introduce a new variant of the UCS algorithm, which uses a hybrid panmictic/niche GA. Preliminary results suggest, unexpectedly, that the niche GA may have even more drawbacks than previously thought.

An evolutionary optimal green layout design for a production facility by simulated annealing algorithm

This study addresses the challenge of prolonged forklift repair times at PT Karya Mitra Teknik, a heavy equipment repair company, attributed to inefficient facility layout. Through field observations and stakeholder interviews, Activity Relationship Chart (ARC) and Analysis Allocation Diagram (AAD) methods were applied to identify inefficiencies and propose layout improvements. Results show a significant reduction in total repair area distance from 110 meters to 55 meters, indicating enhanced operational efficiency. The findings underscore the importance of optimizing facility layout in heavy equipment repair workshops to streamline operations and reduce repair turnaround times, offering practical implications for similar industries seeking cost-effective solutions. Highlights: 1. Efficient facility layout reduces repair area distance.2. ARC and AAD methods streamline resource allocation.3. Optimal layout enhances operational efficiency, reduces turnaround times. Keywords: Facility Layout Optimization, Heavy Equipment Repair, Operational Efficiency, Activity Relationship Chart (ARC), Analysis Allocation Diagram (AAD)

We propose a new framework of computing-inspired multifocal cancer detection procedure (MCDP). Under the rubric of MCDP, the tumor foci to be detected are regarded as solutions of the objective function, the tissue region around the cancer areas represents the parameter space, and the nanorobots loaded with contrast medium molecules for cancer detection correspond to the optimization agents. The process that the nanorobots detect tumors by swimming in the high-risk tissue region can be regarded as the process that the agents search for the solutions of an objective function in the parameter space with some constraints. For multimodal optimization (MMO) aiming to locate multiple optimal solutions in a single simulation run, the niche technology has been widely used. Specifically, the niche genetic algorithm (NGA) has been shown to be particularly effective in solving MMO. It can be used to identify the global optima of multiple hump functions in a running, effectively keep the diversity of the population, and prematurely avoid the genetic algorithm. Learning from the optimization procedure of NGA, we propose the NGA-inspired MCDP in order to locate the tumor targets efficiently while taking into account realistic in vivo propagation and controlling of nanorobots, which is different from the use scenario of the standard NGA. To improve the performance of the MCDP, we also modify the crossover operator of the original NGA from crossing within a population to crossing between two populations. Finally, we present comprehensive numerical examples to demonstrate the effectiveness of the NGA-inspired MCDP when the biological objective function is associated with the blood flow velocity profile caused by tumor-induced angiogenesis.

Optimal arrangement of turbines in wind farms is very important to achieve maximum energy at the lowest cost. In the present study, the use of Vestas V-47 wind turbine and uniform one-way wind in achieving the optimal arrangement of horizontal axis turbines in Manjil with genetic and Monte Carlo algorithms has been investigated. Jensen model is used to simulate the wake effect on the downstream turbines. The objective function is considered as the ratio of cost to power of the power plant. The results show that the Monte Carlo method compared with genetic algorithm will give a better result. Under the same conditions, the Monte Carlo algorithm will give 29% and 40% better results in terms of the number of turbines and output power, respectively. In terms of optimization, in the Monte Carlo algorithm, its fitness value is 16% less than the genetic algorithm, which indicates its better optimization.

The unequal area facility layout problem (UA-FLP) refers to the reasonable arrangement of a certain number of facilities in a given layout area. The facility layout should satisfy given layout constraints and optimize given optimization objectives as far as possible. In this paper, a method combining improved lowest horizontal line method and particle swarm optimization algorithm is proposed to solve UA-FLP, which achieves multi-objective optimization of material handling cost, the adjacent value and the utilization rate of floor shop. On the one hand, the algorithm formulates facility packing rules through the improved lowest horizontal line method, which simplifies the legalization of facility layout. On the other hand, a modified particle swarm optimization (PSO) algorithm combining objective space division method (OSD) and niche technology is used for multi-objective optimization. The proposed algorithm overcomes the shortcomings of previous facility layout methods such as complex overlapping interference process, large amount of calculation and long time for multi-objective optimization. Compared with the comparison methods, the results show that material handling cost is reduced by 1%–6% and the utilization ratio of floor shop is increased by 2%–7%.

Public landscape is a settlement landscape formed under human intervention, which is both cultural resources and ecological environment resources. Niche genetic algorithm is the most widely used algorithm in modern times. It greatly expands the space of system design and optimizes all links in public landscape environment optimization system design. This emerging technology also meets a wide range of public landscape needs. Starting from the influence of environmental factors and niche genetic algorithm, this paper discusses various links of modern public landscape environmental design in urban environment, deeply discusses the relationship and background between niche genetic algorithm and optimizing public landscape environmental design system, and analyzes the limitations of public landscape design at present, Combined with the research of niche genetic algorithm at home and abroad, this paper analyzes the performance links and characteristics of public landscape environment design system, focuses on its application principles and aesthetic evaluation in improving the efficiency of public landscape environment optimization system, and analyzes and discusses the advantages of this emerging tool in the use of public landscape environment optimization system.

It was demonstrated that a niching genetic algorithm (NGA) could be efficient for the optimization of a SiC crystal growth system. And several design parameters of SiC crystal growth system could be optimized at the same time, and high diversity of population was maintained to obtain global optimization solution by NGA. Firstly, the NGA and thermal models were described and applied for SiC crystal growth by physical vapor transport (PVT) method, and the combination method of NGA and thermal models were presented. Then two cases were carried out to demonstrate the automatic optimization of SiC crystal growth system by NGA. One case was a single-objective optimization problem, in which the axial position of coils was optimized to improve the growth rate of crystal. The another was a multi-objective optimization problem, in which the thickness of substrate holder and input current were optimized for uniform temperature distribution along the growth surface for reducing the thermal stresses in growing crystal. Finally, all the optimization results were analyzed.

In order to optimize the orthogonal design of MIMO radar waveform, an improved algorithm based on niche genetic algorithm is proposed. The algorithm is able to eliminate individuals with similar structures in the population or data, maintain a certain distance between individuals, and effectively hold the diversity of them It thus eliminates the phenomenon of premature convergence, strengthens the global search ability of the algorithm, and improves its convergence speed. It is shown that the algorithm has both the advantages of overall search ability of genetic algorithm and the ability of the niche genetic algorithm to discover multiple optimal solutions. Simulation was given to show that the algorithm was cable to reduce the side-lobe of the auto-correlation function of the orthogonal polyphase code and the peak value of the cross-correlation function of the code with good performance of autocorrelation and cross-correlation obtained

In this paper we calculate a synthetic medium surface displacement response that is consistent with real measurement data by applying the least-square principle and a niche genetic algorithm to the parameters inversion problem of the wave equation in a two-phase medium. We propose a niche genetic multi-parameter (including porosity, solid phase density and fluid phase density) joint inversion algorithm based on a two-phase fractured medium in the BISQ model. We take the two-phase fractured medium of the BISQ model in a two-dimensional half space as an example, and carry out the numerical reservoir parameters inversion. Results show that this method is very convenient for solving the parameters inversion problem for the wave equation in a two-phase medium, and has the advantage of strong noise rejection. Relative to conventional genetic algorithms, the niche genetic algorithm based on a sharing function can not only significantly speed up the convergence, but also improve the inversion precision.

Appropriate selection of facility layout methodology for designing or improving a layout of a plant is very crucial and can have a high impact on the success of a plant. Continuous improvements in layout can increase productivity, reduce wastage and improve the quality and safety of the plant. A case study on improvement done in electronic product manufacturing plant is elaborated in this chapter. The plant layout is re-organized from a product-based layout to a hybrid of product- and process-based layouts. This is an observation-based study which resulted in significant improvement in material flow in the plant, improvement in productivity, and improvement in quality and safety in the plant. This study suggests involving continuous improvement in layout and measuring the impact of changes. Facility layout should be part of continuous improvements. Being a case study approach for improvement in layouts, this study highlights the credibility of applications of improvement in layout in the real-world working environment.

In this paper, cluster splitting and merging algorithms are used for flood assessment using LISS-III (before flood) and SAR (during flood) images. Bayesian Information Criteria (BIC) is used to determine the optimal number of clusters. Keeping this constraint, the cluster centers are generated using the cluster splitting techniques, namely Mean Shift Clustering (MSC), and Niche Genetic Algorithm (NGA). The merging method is used to group the data points into their respective classes, using the cluster centers obtained from the above techniques. These techniques are applied on the LISS-III and SAR image. Further, the resultant images are overlaid to analyze the extent of the flood in individual land classes. A performance comparison of these techniques (MSC and NGA) is presented. From the results obtained, we deduce that the NGA is efficient.

Manufacturing industry plays an important role in the country, and the optimization of factory facility layout design in basic manufacturing industry will affect the problem that needs to be solved urgently. The problem of facility layout optimization is not only an engineering research problem, but also more complex in practice, with high practicality. However, its interdisciplinary characteristics are also very obvious. Based on the principle of discrete system simulation, the initial model of the camshaft production line is established on the basis of determining the camshaft production process and the specific parameters of each machine tool. Secondly, according to the established model, discuss the working efficiency of each equipment, the working condition of the station, the failure efficiency of the production line and the bottleneck process, etc., run the model and analyze the results. According to the analysis of the simulation results of the model, it is gradually improved. Finally, the mass production of camshaft production line is discussed, and the initial population, individual fitness function, genetic parameters, etc. are analyzed. On this basis, genetic algorithm is used to optimize the scheduling problem, and the results are compared with the previous ones to verify whether the results are effective.

This paper presents a crowding niche cellular genetic algorithm (referred to NCGA) aiming at solving the problems of local convergence and non-uniform population distribution in traditional genetic algorithm. The selecting operation in traditional genetic algorithm is improved by bringing in the concept of neighbors of cellular genetic algorithm, and the population distribution is greatly enhanced by introducing crowding niche mechanism, which betters the ability of global searching and helps to avoid the population local convergence. Meanwhile, the paper describes the crowding niche cellular genetic algorithm in details and compares it with simple genetic algorithm （SGA） and simple niche genetic algorithm （NGA）; the comparison results reveal that, NCGA outperforms the other two algorithms in terms of convergence rate and population diversity.