A robust approach to design a single facility layout plan in dynamic manufacturing environments using a permutation-based genetic algorithm

During the past few decades, developing efficient methods to solve dynamic facility layout problems has been focused on significantly by practitioners and researchers. More specifically meta-heuristic algorithms, especially genetic algorithm, have been proven to be increasingly helpful to generate sub-optimal solutions for large-scale dynamic facility layout problems. Nevertheless, the uncertainty of the manufacturing factors in addition to the scale of the layout problem calls for a mixed genetic algorithm–robust approach that could provide a single unlimited layout design. The present research aims to devise a customized permutation-based robust genetic algorithm in dynamic manufacturing environments that is expected to be generating a unique robust layout for all the manufacturing periods. The numerical outcomes of the proposed robust genetic algorithm indicate significant cost improvements compared to the conventional genetic algorithm methods and a selective number of other heuristic and meta-heuristic techniques.

Study on Combinational Scheduling Optimization of Bus Transit Rapid Based on Tabu Search & Genetic Algorithm

The goal is to minimize the sum of operating cost and passengers’ travel cost, and establish an optimized combinational scheduling model of Bus Rapid Transit (BRT) combined with regular bus, express bus and shuttle bus. A mixed genetic algorithm based on tabu search algorithm (GA-TS) has been designed after analyzing the fundamental principle of genetic algorithm (GA) and tabu search (TS). A case study has been carried out on the combinational scheduling optimization of a selecting BRT line. By adopting the combinational scheduling model, 5.24% of the total system cost can be saved, which is quite prominent. The mixed genetic algorithm based on GA-TS can optimize the BRT scheduling system, shorten the turnaround time of operating BRT vehicles, effectively reduce the total system cost of BRT and improve decision-making efficiency and service quality.

An Optimization Method of the Heavy Machine Cutting Parameters

This paper analyzes the machining process of CNC machine tools, and builds an optimization model of the machining process parameters based on the mechanical vibration and the operational research. The model mixed genetic algorithm and particle swarm optimization (PSO) is built. It proposes an optimization algorithm that has higher convergence precision and execute ability to solve engineering problem with nonlinear and multi-extremum. According to case study, it proves the correctness of the model and the efficiency and high-performance nature of the designed optimization algorithm. It also appears the efficiency to solve the common engineering problems by the intelligent optimization algorithms.

Complex Genetic Algorithm for Structure Shape Optimization Design of Mixed Discrete Variables

Discrete complex method is used in genetic algorithm(GA) and a mixed genetic algorithm called complex genetic algorithm(CGA) is formed. The complex method used here increases the quality of species groups, and improves the searching efficiency. The mixed genetic algorithm method is used in the shape optimization for mixed discrete variables. The integration and coding of the shape variables and the cross-section variables in genetic algorithm can not only solve the coupling problem of two kinds of variables, but also avoid the partial optimum solution resulting from the separation of the two kinds of variables. The result of the exemplification indicates that the complex genetic algorithm for structure shape optimization design of mixed discrete variables is effective.

Fusegates selection and operation: simulation–optimization approach

Fusegates present a reliable and cost-effective alternative, which increase flood protection and water supply benefits. This article develops a comprehensive simulation–optimization framework for practical selection, installation, and operation of fusegates. The computational model simulates the complicated hydraulic behavior of fusegates systems with varying design characteristics and consequential anomalous routing process in case of flood events. An efficient mixed genetic algorithm (GA) is subsequently developed and coupled with the highly nonlinear hydraulic simulation model to minimize the overall expected annual cost under structural, hydraulic, and operational constraints. Types, heights, and tipping heads of gates are explicitly treated as optimization decision variables. Furthermore, the frequent practice of installing all gates in the same level is practically improved to favorably help minimize water loss in case of moderate discharge floods. The proposed model is demonstrated and discussed for a case study of the Taleghan Dam fusegates installation project in Iran. A series of sensitivity analyses are also conducted to assess routing effect and uncertainty in water unit price and replacement costs and provide more insight and understanding of the design problem.

Optimal Design of Gear Transmission System of Increasing Speed for Half-Direct-Drive Tidal Current Energy Station

The half-direct-drive transmission is conducted for the transmission system of tidal current power stations using a small speed-up ratio of planetary gearbox between turbine and generator. A design model is developed for the optimization of the planetary gear transmission system. And a mixed genetic algorithm is applied on the basis of fundamentals of genetic algorithm to carry out the optimization. From the example calculation, a better design scheme is obtained by the optimization.