A new multi-objective optimization method for master production scheduling problems based on genetic algorithm

In this paper, a new interval multi-objective optimization (MOO) method integrating with the multidimensional parallelepiped (MP) interval model has been proposed to handle the uncertain problems with dependent interval variables. The MP interval model is integrated to depict the uncertain domain of the problem, where the uncertainties are described by marginal intervals and the degree of the dependencies among the interval variables is described by correlation coefficients. Then an efficient multi-objective iterative algorithm combining the micro multi-objective genetic algorithm (MOGA) with an approximate optimization method is formulated. Three numerical examples are presented to demonstrate the efficiency of the proposed approach.

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A Product Configuration Optimization Method Based on Non-Dominated Sorting Genetic Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.215.366 ◽

2011 ◽

Vol 215 ◽

pp. 366-372

Author(s):

W.H. Sun ◽

W.C. Lu ◽

D.Y. Lin

Keyword(s):

Genetic Algorithm ◽

Optimization Method ◽

Product Configuration ◽

Configuration Design ◽

Hierarchical Analysis ◽

Multi Objective Optimization ◽

Configuration Optimization ◽

Complex Product ◽

Multi Objective

In order to realize the complex product rapid configuration design in the environment of mass configuration (MC), the non-dominated sorting genetic algorithm (NGSA) for product rapid configuration design is proposed in this paper. The model of multi-objective product configuration optimization is established, and hierarchical analysis is made for configuration design. By comparing the similarity and integrity of requirement and instance, the sequence of retrieval instances is given according to the reuse degree, and multi-objective optimization configuration based on NGSA is realized. Finally, the validity and practicability of the method is verified by an instance which is applied in rapid configuration design of the drive module of tuyere puncher.

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Non-Dominated Sorting Genetic Quantum Algorithm for Multi-Objective Optimization

Volume 6: 33rd Design Automation Conference, Parts A and B ◽

10.1115/detc2007-35554 ◽

2007 ◽

Author(s):

Amir-R. Khorsand ◽

G. Gary Wang ◽

J. Raghavan

Keyword(s):

Genetic Algorithm ◽

Quantum Algorithm ◽

Optimization Method ◽

Benchmark Problems ◽

General Interest ◽

Test Results ◽

Multi Objective Optimization ◽

Systematic Comparison ◽

Multi Objective ◽

Single Objective

This paper presents a new multi-objective optimization method, which is inspired from the idea of non-dominated sorting genetic algorithm (NSGA) and genetic quantum algorithm (GQA). The GQA has been tested on well known test beds in single objective optimization and compared with the genetic algorithm (GA) in the lead author’s previous work [22]. This paper aims to apply the idea of GQA to multi-objective optimization (MOO). The developed method is called non-dominated sorting genetic quantum algorithm (NSGQA). The developed method is tested with benchmark problems collected from literature, which have characteristics representing various aspects of a MOO problem. Test results show that NSGQA has better performance on most benchmark problems than currently popular MOO methods such as the NSGA. The integration of GQA with MOO, and the systematic comparison with other MOO methods on benchmark problems, should be of general interest to researchers on MOO and to practitioners using MOO methods in design.

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A fuzzy matter-element scheme multi-objective optimization method based on genetic algorithm

Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527) ◽

10.1109/wcica.2002.1021402 ◽

2003 ◽

Cited By ~ 1

Author(s):

Zhao Yanwei ◽

Zhang Guoxian

Keyword(s):

Genetic Algorithm ◽

Optimization Method ◽

Multi Objective Optimization ◽

Element Scheme ◽

Multi Objective

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Inversion of marine controlled source electromagnetic data using a parallel non-dominated sorting genetic algorithm

Geophysical Journal International ◽

10.1093/gji/ggz501 ◽

2019 ◽

Vol 220 (2) ◽

pp. 1066-1077 ◽

Cited By ~ 1

Author(s):

Mohit Ayani ◽

Lucy MacGregor ◽

Subhashis Mallick

Keyword(s):

Genetic Algorithm ◽

Synthetic Data ◽

Optimization Method ◽

Model Parameters ◽

Data Sets ◽

Multi Objective Optimization ◽

Controlled Source ◽

Multi Objective ◽

Electromagnetic Data ◽

Deterministic Methods

SUMMARY We developed a multi-objective optimization method for inverting marine controlled source electromagnetic data using a fast-non-dominated sorting genetic algorithm. Deterministic methods for inverting electromagnetic data rely on selecting weighting parameters to balance the data misfit with the model roughness and result in a single solution which do not provide means to assess the non-uniqueness associated with the inversion. Here, we propose a robust stochastic global search method that considers the objective as a two-component vector and simultaneously minimizes both components: data misfit and model roughness. By providing an estimate of the entire set of the Pareto-optimal solutions, the method allows a better assessment of non-uniqueness than deterministic methods. Since the computational expense of the method increases as the number of objectives and model parameters increase, we parallelized our algorithm to speed up the forward modelling calculations. Applying our inversion to noisy synthetic data sets generated from horizontally stratified earth models for both isotropic and anisotropic assumptions and for different measurement configurations, we demonstrate the accuracy of our method. By comparing the results of our inversion with the regularized genetic algorithm, we also demonstrate the necessity of casting this problem as a multi-objective optimization for a better assessment of uncertainty as compared to a scalar objective optimization method.

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An Improved Multi-Objective Evolutionary Approach for Aerospace Shell Production Scheduling Problem

Symmetry ◽

10.3390/sym12040509 ◽

2020 ◽

Vol 12 (4) ◽

pp. 509

Author(s):

Qing Wang ◽

Xiaoshuang Wang ◽

Haiwei Luo ◽

Jian Xiong

Keyword(s):

Evolutionary Algorithm ◽

Production Scheduling ◽

Production Line ◽

Optimization Problems ◽

Structural Features ◽

Optimization Process ◽

Scheduling Problems ◽

Multi Objective Optimization ◽

Multi Objective ◽

Shell Production

To certain degree, multi-objective optimization problems obey the law of symmetry, for instance, the minimum of one objective function corresponds to the maximum of another objective. To provide effective support for the multi-objective operation of the aerospace product shell production line, this paper studies multi-objective aerospace shell production scheduling problems. Firstly, a multi-objective optimization model for the production scheduling of aerospace product shell production lines is established. In the presented model, the maximum completion time and the cost of production line construction are optimized simultaneously. Secondly, to tackle the characteristics of discreteness, non-convexity and strong NP difficulty of the multi-objective problem, a knowledge-driven multi-objective evolutionary algorithm is designed to solve the problem. In the proposed approach, structural features of the scheduling plan are extracted during the optimization process and used to guide the subsequent optimization process. Finally, a set of test instances is generated to illustrate the addressed problem and test the proposed approach. The experimental results show that the knowledge-driven multi-objective evolutionary algorithm designed in this paper has better performance than the two classic multi-objective optimization methods.

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A Multi-Objective Optimization Method for Coordinated Control

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.97-98.942 ◽

2011 ◽

Vol 97-98 ◽

pp. 942-946

Author(s):

Yun Feng Gao ◽

Hua Hu ◽

Tao Wang ◽

Xiao Guang Yang

Keyword(s):

Genetic Algorithm ◽

Optimal Solution ◽

Optimization Method ◽

Transmission Model ◽

Traffic Signal Control ◽

Cell Transmission Model ◽

Multi Objective Optimization ◽

Multi Objective ◽

Weighted Combination ◽

Single Objective

In this paper, to overcome the limitations of the weighted combination and single objective optimization methods, we presented a multi-objective optimization and simulation methodology for network-wide traffic signal control. A multi-objective genetic algorithm based on Non-dominated Sorting Genetic Algorithm II was given to solve the model directly to obtain Pareto optimal solution set. The objectives were evaluated by Enhanced Cell Transmission Model used to describe traffic dynamics on signalized urban road network. The results showed that the single objective optimization method made some of the objectives worsen when the objective to be optimized reaching optimal, and that the weighted combination optimization method gained a compromised solution, but the multi-objective optimization method gave consideration to more objectives, making the number of optimal or suboptimal ones is more than that of worse ones.

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