Multi-objective optimisation of allocations and locations of incineration facilities with Voronoi diagram and genetic algorithm: case study of Hiroshima City and Aki County

In today’s world, using fashion goods is a vital of human. In this research, we focused on developing a scheduling method for distributing and selling fashion goods in a multi-market/multi-retailer supply chain while the product demands in markets are stochastic. For this purpose, a new multi-objective mathematical programming model is developed where maximizing the profit of selling fashion goods and minimizing delivering time and customer’s dissatisfaction are considered as objective functions. In continue due to the complexity of the problem, a number of metaheuristics are compared and a hybrid of Non-dominated Sorting Genetic Algorithm II (NSGAII) and simulated annealing is selected for solving the case studies. Then, in order to find the best values for input parameters of the algorithm, a Taguchi method is applied. In continue, a number of case studies are selected from literature review and solved by the algorithm. The outcomes are analyzed and it is found that using multi-objective models can find more realistic solutions. Then, the model is applied for a case study with real data from industry and outcomes showed that the proposed algorithm can be successfully applied in practice.

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Parameter Based Multi-Objective Optimization of Video CODECs

Applied Signal and Image Processing ◽

10.4018/978-1-60960-477-6.ch017 ◽

2011 ◽

pp. 288-308

Author(s):

F. Al-Abri ◽

E.A. Edirisinghe ◽

C. Grecos

Keyword(s):

Genetic Algorithm ◽

Mathematical Formulation ◽

Video Codec ◽

Video Content ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Video Codecs ◽

Multi Objective ◽

On Demand

This chapter presents a generalised framework for multi-objective optimisation of video CODECs for use in off-line, on-demand applications. In particular, an optimization scheme is proposed to determine the optimum coding parameters for a H.264 AVC video codec in a memory and bandwidth constrained environment, which minimises codec complexity and video distortion. The encoding/decoding parameters that have a significant impact on the performance of the codec are initially obtained through experimental analysis. A mathematical formulation by means of regression is subsequently used to associate these parameters with the relevant objectives and define a Multi-Objective Optimization (MOO) problem. Solutions to the optimization problem are reached through a Non-dominated Sorting Genetic Algorithm (NSGA-II). It is shown that the proposed framework is flexible on the number of objectives that can jointly be optimized. Furthermore, any of the objectives can be included as constraints depending on the requirements of the services to be supported. Practical use of the proposed framework is described using a case study that involves video content transmission to a mobile hand.

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Multi-Objective Optimization of Manufacturing Processes Using Evolutionary Algorithms

Computational Methods for Optimizing Manufacturing Technology - Advances in Mechatronics and Mechanical Engineering ◽

10.4018/978-1-4666-0128-4.ch002 ◽

2012 ◽

pp. 44-66

Author(s):

M. Kanthababu

Keyword(s):

Genetic Algorithm ◽

Evolutionary Algorithms ◽

Multiple Objectives ◽

Manufacturing Processes ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Local Optima ◽

Multi Objective ◽

Research Areas

Recently evolutionary algorithms have created more interest among researchers and manufacturing engineers for solving multiple-objective problems. The objective of this chapter is to give readers a comprehensive understanding and also to give a better insight into the applications of solving multi-objective problems using evolutionary algorithms for manufacturing processes. The most important feature of evolutionary algorithms is that it can successfully find globally optimal solutions without getting restricted to local optima. This chapter introduces the reader with the basic concepts of single-objective optimization, multi-objective optimization, as well as evolutionary algorithms, and also gives an overview of its salient features. Some of the evolutionary algorithms widely used by researchers for solving multiple objectives have been presented and compared. Among the evolutionary algorithms, the Non-dominated Sorting Genetic Algorithm (NSGA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) have emerged as most efficient algorithms for solving multi-objective problems in manufacturing processes. The NSGA method applied to a complex manufacturing process, namely plateau honing process, considering multiple objectives, has been detailed with a case study. The chapter concludes by suggesting implementation of evolutionary algorithms in different research areas which hold promise for future applications.

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