Multi-Objective Optimisation of the Benchmark Wind Farm Layout Problem

Wind farm layout optimisation has become a very challenging and widespread problem in recent years. In many publications, the main goal is to achieve the maximum power output and minimum wind farm cost. This may be accomplished by applying single or multi-objective optimisation techniques. In this paper, we apply a single objective hill-climbing algorithm (HCA) and three multi-objective evolutionary algorithms (NSGA-II, SPEA2 and PESA-II) to a well-known benchmark optimisation problem proposed by Mosetti et al., which includes three different wind scenarios. We achieved better results by applying single- and multi-objective algorithms. Furthermore, we showed that the best performing multi-objective algorithm was NSGA-II. Finally, an extensive comparison of the results of past publications is made.

Comparison of spreadsheet-based optimization tools applied to construction site layout problem

This paper compares different spreadsheet-based optimization tools applied to a practical example of a construction site layout problem. The objective of the optimization is to minimize the total time of material transportation by optimal positioning of tower crane and work facilities on the construction site with repetitive operations. Computer programs, such as MS Excel, LibreOffice Calc, and Google Sheets can be applied as modeling tools for a variety of construction optimization problems in addition to their usual functionalities. In this study, LibreOffice Calc Solver, MS Excel Solver, along with other MS Excel add-ins, i.e., OpenSolver, Evolver, and What’sBest, are analyzed. The capabilities of optimization tools mentioned above are examined on the problem of optimal positioning of tower crane and work facilities on the construction site. The results obtained by optimization tools are noted and discussed. The paper ends with conclusions and recommendations for further research.

Double Genes Improved Genetic Algorithm for Solving Two-Dimensional Rectangular Layout Problem

Two-dimensional rectangular layout is according to the number of rectangular pieces and the size of the area of the rectangular pieces into the plate. Depending on the iteration of population in genetic algorithm, better utilization rate of plate is obtained. However, due to the characteristics of vertical and horizontal rows of rectangular pieces, relying on the sequence of rectangular pieces alone as the gene cannot guarantee the genetic diversity of the population, and leads to premature algorithm. In view of the special characters of rectangular layout, Double Genes improved genetic algorithm is proposed according to the order of rectangular layout and its own placement characteristics. In order to improve population diversity, Angle genes were added on the basis of rectangular sequencing genes. In view of the particularity of double genes, double random crossover operators and double mutation operators are proposed to improve the population diversity and randomness of genetic algorithm. Experimental results show the effectiveness of the improved algorithm.

An Application of an Unequal-Area Facilities Layout Problem with Fixed-Shape Facilities

The unequal-area facility layout problem (UA-FLP) is the problem of locating rectangular facilities on a rectangular floor space such that facilities do not overlap while optimizing some objective. The objective considered in this paper is minimizing the total distance materials travel between facilities. The UA-FLP considered in this paper considers facilities with fixed dimension and was motivated by the investigation of layout options for a production area at the Toyota Motor Manufacturing West Virginia (TMMWV) plant in Buffalo, WV, USA. This paper presents a mathematical model and a genetic algorithm for locating facilities on a continuous plant floor. More specifically, a genetic algorithm, which consists of a boundary search heuristic (BSH), a linear program, and a dual simplex method, is developed for an UA-FLP. To test the performance of the proposed technique, several test problems taken from the literature are used in the analysis. The results show that the proposed heuristic performs well with respect to solution quality and computational time.

Evolutionary Methods in House Floor Plan Design

In this paper, an evolutionary technique is proposed as a method for generating new design solutions for the floor layout problem. The genotypes are represented by the vectors of numerical values of points representing endpoints of room walls. Equivalents of genetic operators for such a representation are proposed. A case study of the design problem of one-story houses is presented from the initial requirements to the best solutions. An evaluation method using requirement-weighted fitness function for evolved plans is also proposed. The obtained results as well as the advantages and issues related to such an approach are also discussed.