Augmented ε-constraint method in multi-objective flowshop problem with past sequence set-up times and a modified learning effect

In this article, a modification of multi-objective differential evolution based on simulated annealing is proposed to solve a general tri-objective non-permutation flow shop problem. The flow shop system considers the release dates, machine breakdowns, past-sequence-dependent setup times and learning effect for all the jobs. The algorithm proposed to tackle such a model combines the robustness of differential evolution with the rapid convergence and conditional diversification of simulated annealing. For small and medium low-sized problems, the solutions found by the proposed algorithm are compared with the exact solutions, achieved by augmented ε-constraint method. Due to the high complexity of the model, for medium high and large-sized problems, the algorithm is tested against the imperialist competitive algorithm and the multi-objective differential evolution scheduling. Comparisons of the results show a good balance between intensification and diversification in the proposed algorithm.

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Applications of Multiple Objective Genetic Algorithms in the Optimization Design of Tracked Self-Moving Power’s Layout

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.307.161 ◽

2013 ◽

Vol 307 ◽

pp. 161-165

Author(s):

Hai Jin ◽

Jin Fa Xie

Keyword(s):

Optimization Design ◽

Pareto Front ◽

Layout Optimization ◽

Multiple Objective ◽

Multi Objective Optimization ◽

Layout Problem ◽

Basic Principles ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Set Up

A multi-objective genetic algorithm is applied into the layout optimization of tracked self-moving power. The layout optimization mathematical model was set up. Then introduced the basic principles of NSGA-Ⅱ, which is a Pareto multi-objective optimization algorithm. Finally, NSGA-Ⅱwas presented to solve the layout problem. The algorithm was proved to be effective by some practical examples. The results showed that the algorithm can spread toward the whole Pareto front, and provide many reasonable solutions once for all.

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A Pareto biogeography-based optimisation for multi-objective two-sided assembly line sequencing problems with a learning effect

Computers & Industrial Engineering ◽

10.1016/j.cie.2014.01.001 ◽

2014 ◽

Vol 69 ◽

pp. 89-104 ◽

Cited By ~ 16

Author(s):

Parames Chutima ◽

Wanwisa Naruemitwong

Keyword(s):

Learning Effect ◽

Assembly Line ◽

Multi Objective ◽

Sequencing Problems

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Virtual Machines Scheduling Algorithm Based on Multi-Objective Optimization in Cloud Computing

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1046.508 ◽

2014 ◽

Vol 1046 ◽

pp. 508-511

Author(s):

Jian Rong Zhu ◽

Yi Zhuang ◽

Jing Li ◽

Wei Zhu

Keyword(s):

Cloud Computing ◽

Energy Consumption ◽

Virtual Machine ◽

Virtual Machines ◽

Scheduling Algorithm ◽

Machine Scheduling ◽

Multi Objective Optimization ◽

Multi Objective ◽

Virtual Machine Scheduling ◽

Set Up

How to reduce energy consumption while improving utility of datacenter is one of the key technologies in the cloud computing environment. In this paper, we use energy consumption and utility of data center as objective functions to set up a virtual machine scheduling model based on multi-objective optimization VMSA-MOP, and design a virtual machine scheduling algorithm based on NSGA-2 to solve the model. Experimental results show that compared with other virtual machine scheduling algorithms, our algorithm can obtain relatively optimal scheduling results.

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