Automating the layout of network diagrams with specified visual organization.

Network diagrams are a familiar graphic form that can express many different kinds of information. The problem of automating network-diagram layout has therefore received much attention. Previous research on network-diagram layout has focused on the problem of aesthetically optimal layout, using such criteria as the number of link crossings, the sum of all link lengths, and total diagram area. In this paper the authors propose a restatement of the network-diagram layout problem in which layout-aesthetic concerns are subordinated to perceptual-organization concerns. The authors present a notation for describing the visual organization of a network diagram. This notation is used in reformulating the layout task as a constrained-optimization problem in which constraints are derived from a visual-organization specification and optimality criteria are derived from layout-aesthetic considerations. Two new heuristic algorithms are presented for this version of the layout problem: one algorithm uses a rule-based strategy for computing a layout; the other is a massively parallel genetic algorithm. The authors demonstrate the capabilities of the two algorithms by testing them on a variety of network-diagram layout problems.Engineering and Applied Science

Layout Optimization of Microsatellite Components Using Genetic Algorithm

The placement of satellite components usually belongs to non-deterministic polynomial-time hard (NP-hard) problems that in terms of computational complexity is very difficult to solve. This problem is normally known as layout optimization problem (LOP). In this study the layout of microsatellite components has to meet the requirements set by mission payloads, launcher and spacecraft attitude control. The novel scheme is to find the various possibilities of optimal layout using genetic algorithms combined with order-based positioning technique (OPT). Each component has a given index and then placed in a container based on specific order of placements in accordance with a bottom-left (BL) algorithm that is already established. The placement order is generated by the genetic algorithm which explore various possibilities to obtain a sequence that brings the best solution

A Sule’s Method initiated genetic algorithm for solving QAP formulation in facility layout design: A real world application

This paper considers the Quadratic Assignment Problem (QAP) as one of the most important issues in optimization. This NP-hard problem has been largely studied in the scientific literature, and exact and approximate (heuristic and meta-heuristic) approaches have been used mainly to optimize one or more objectives. However, most of these studies do not consider or are not tested in real applications. Hence, in this work, we propose the use of Sule’s Method and genetic algorithms, for a QAP (stated as a facility Layout Problem) in a real industry application in Colombia so that the total cost to move the required material between the facilities is minimized. As far as we know, this is the first work in which Sule’s Method and genetic algorithms are used simultaneously for this combinatorial optimization problem. Additionally the proposed approach was tested using well-known datasets from the literature in order to assure its efficiency

Layout Planning with Isles: A Genetic Approach

Plant layout problems involve distributing different resources or departments in a given plant and achieving maximum efficiency for the services or goods being made or offered. To this end, plants are designed to optimize production flow from the first stage (i.e. as raw material) to finish product. However, optimization which is generally expressed either in terms of minimization (for example, of material handling costs) or of maximization (for example, the number of desired adjacencies in a qualitative chart) is not always feasible when real problems or real sizes are being handled. The level of complexity may turn out considerable as the number of parameters, restrictions and other variables considered in the study become larger. This kind of problem has been formulated, from a mathematical view point as a static quadratic assignment problem. However, the number of problems that are usceptible to being solved by optimization methods is very limited. Some alternatives have been called from the field of graph-theory, direct method algorithms, construction algorithms (such as CORELAP), and improvement algorithms (such as CRAFT). In this thesis work, an attempt is made to develop the algorithm for solving layout problem with real-life restriction like aisles, used in factories for the easy transfer of materials from one section to the other, using Genetic Algorithm

Adaptation of the simulated evolution algorithm for wind farm layout optimization

Wind energy is a potential replacement for traditional, fossil-fuel-based power generation sources. One important factor in the process of wind energy generation is to design of the optimal layout of a wind farm to harness maximum energy. This layout optimization is a complex, NP-hard optimization problem. Due to the sheer complexity of this layout design, intelligent algorithms, such as the ones from the domain of natural computing, are required. One such effective algorithm is the simulated evolution (SE) algorithm. This paper presents a simulated evolution algorithm engineered to solve the wind farm layout design (WFLD)optimization problem. In contrast to many non-deterministic algorithms, such as genetic algorithms and particle swarm optimization which operate on a population, the SE algorithm operates on a single solution, decreasing the computational time. Furthermore, the SE algorithm has only one parameter to tune as opposed to many algorithms that require tuning multiple parameters. A preliminary empirical study is done using data collected from a potential location in the northern region of Saudi Arabia. Experiments are carried out on a 10 × 10 grid with 15 and 20 turbines while considering turbines with a rated capacity of 1.5 MW. Results indicate that a simulated evolution algorithm is a viable option for the said problem

Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts

[EN] RGB-D sensors can collect postural data in an automatized way. However, the application of these devices in real work environments requires overcoming problems such as lack of accuracy or body parts' occlusion. This work presents the use of RGB-D sensors and genetic algorithms for the optimization of workstation layouts. RGB-D sensors are used to capture workers' movements when they reach objects on workbenches. Collected data are then used to optimize workstation layout by means of genetic algorithms considering multiple ergonomic criteria. Results show that typical drawbacks of using RGB-D sensors for body tracking are not a problem for this application, and that the combination with intelligent algorithms can automatize the layout design process. The procedure described can be used to automatically suggest new layouts when workers or processes of production change, to adapt layouts to specific workers based on their ways to do the tasks, or to obtain layouts simultaneously optimized for several production processes.This work was supported by the Programa estatal de investigacion, desarrollo e innovacion orientada a los retos de la sociedad of the Government of Spain under Grant TIN2013-42504-R.Diego-Mas, JA.; Poveda Bautista, R.; Garzon-Leal, D. (2017). Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts. Applied Ergonomics. 65:530-540. doi:10.1016/j.apergo.2017.01.012S5305406

Comparison of offshore wind farm layout optimization using a genetic algorithm and a particle swarm optimizer

This article explores the application of a binary genetic algorithm and a binary particle swarm optimizer to the optimization of an offshore wind farm layout. The framework developed as part of this work makes use of a modular design to include a detailed assessment of a wind farm’s layout including validated analytic wake modeling, cost assessment, and the design of the necessary electrical infrastructure considering constraints. This study has found that both algorithms are capable of optimizing the layout with respect to levelized cost of energy when using a detailed, complex evaluation function. Both are also capable of identifying layouts with lower levelized costs of energy than similar studies that have been published in the past and are therefore both applicable to this problem. The performance of both algorithms has highlighted that both should be further tuned and benchmarked in order to better characterize their performance

Facility layout problem: Bibliometric and benchmarking analysis

Facility layout problem is related to the location of departments in a facility area, with the aim of determining the most effective configuration. Researches based on different approaches have been published in the last six decades and, to prove the effectiveness of the results obtained, several instances have been developed. This paper presents a general overview on the extant literature on facility layout problems in order to identify the main research trends and propose future research questions. Firstly, in order to give the reader an overview of the literature, a bibliometric analysis is presented. Then, a clusterization of the papers referred to the main instances reported in literature was carried out in order to create a database that can be a useful tool in the benchmarking procedure for researchers that would approach this kind of problems