Stochastic Multi-Objective Optimisation of a Gearbox Synchroniser and Selector Mechanism

2003 ◽  
Author(s):  
Massimiliano Gobbi ◽  
Giampiero Mastinu ◽  
Massimo Caudano
Keyword(s):  
System Model ◽  
Objective Functions ◽  
Performance Indices ◽  
Global Approximation ◽  
Stochastic Perturbations ◽  
New Approach ◽  
Multi Objective ◽  
Design Solutions ◽  
Shift Action ◽  
Multi Body

In the paper a new approach is presented for the design of the synchroniser and selector mechanism of a road vehicle gearbox. The main aim is to improve shiftability and driver comfort. The new approach is based not only on the theory of multi-objective optimisation but also on robust design. A multi-body physical system model of the synchroniser and selector mechanism has been developed and vaildated experimentally. The physical model is stochastic being many of the system parameters defined by stochastic processes. The fifty-eight parameters of the system model have been tuned in order to achieve the desired dynamic behaviour of the synchroniser and selector mechanism during a reference shift action, defined by nine performance indices. The new approach is characterised both by the optimisation of the objective functions (corresponding to system performance indices) and by the reduction (or minimisation) of the sensitivity (variance) of the performance indices to stochastic perturbations. Such variances are computed (very quickly) by means of an original procedure based on the global approximation of the objective functions. Additionally, with respect to the mentioned features, the new approach is based on both a special study to explore all of the feasible design solutions, and on a global sensitivity procedure to analyse (in a stochastic context) the influence of each design variable on each objective function. Pareto-optimal design solutions for different levels of “robustness” have been computed in a very short time. The optimisation method has been applied with successful results. A number of optimised synchronisers and selector mechanisms have been defined, all of them featuring relevant improvements in terms of performance and robustness with respect to the reference system, already effective and under production.

Optimal and Robust Design of Ground Vehicle Systems

2006 ◽  
Author(s):  
Massimiliano Gobbi
Keyword(s):  
Robust Design ◽  
Simple Problem ◽  
Objective Functions ◽  
Performance Indices ◽  
Global Approximation ◽  
Stochastic Perturbations ◽  
New Approach ◽  
Complex Design ◽  
Design Solutions ◽  
Short Time

A new approach for the design of vehicle subsystems is addressed in the paper. The new approach is based not only on the theory of multi-objective optimisation but also on robust design. The method is characterised both by the optimisation of the objective functions (corresponding to system performance indices) and by the reduction (or minimisation) of the sensitivity (variance) of the performance indices to stochastic perturbations. Such variances are computed (very quickly) by means of an original procedure based on the global approximation of the objective functions. Additionally, with respect to the mentioned features, the new approach is based on both a special study to explore all of the feasible design solutions, and on a global sensitivity procedure to analyse (in a stochastic context) the influence of each design variable on each objective function. Pareto-optimal design solutions for different levels of “robustness” can be computed in a very short time. The optimisation method has been tested on a relatively simple problem and applied with successful results to a complex design problem related to vehicle design.

scholarly journals A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization

2021 ◽  
Vol 11 (10) ◽  
pp. 4575
Author(s):  
Eduardo Fernández ◽  
Nelson Rangel-Valdez ◽  
Laura Cruz-Reyes ◽  
Claudia Gomez-Santillan
Keyword(s):  
Portfolio Optimization ◽  
Imperfect Information ◽  
Model Parameters ◽  
Final Decision ◽  
Project Portfolio ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Group Member

This paper addresses group multi-objective optimization under a new perspective. For each point in the feasible decision set, satisfaction or dissatisfaction from each group member is determined by a multi-criteria ordinal classification approach, based on comparing solutions with a limiting boundary between classes “unsatisfactory” and “satisfactory”. The whole group satisfaction can be maximized, finding solutions as close as possible to the ideal consensus. The group moderator is in charge of making the final decision, finding the best compromise between the collective satisfaction and dissatisfaction. Imperfect information on values of objective functions, required and available resources, and decision model parameters are handled by using interval numbers. Two different kinds of multi-criteria decision models are considered: (i) an interval outranking approach and (ii) an interval weighted-sum value function. The proposal is more general than other approaches to group multi-objective optimization since (a) some (even all) objective values may be not the same for different DMs; (b) each group member may consider their own set of objective functions and constraints; (c) objective values may be imprecise or uncertain; (d) imperfect information on resources availability and requirements may be handled; (e) each group member may have their own perception about the availability of resources and the requirement of resources per activity. An important application of the new approach is collective multi-objective project portfolio optimization. This is illustrated by solving a real size group many-objective project portfolio optimization problem using evolutionary computation tools.

Design Improvement by Sensitivity Analysis (DISA) Under Interval Uncertainty Using Multi-Objective Optimization

2009 ◽  
Author(s):  
J. Hamel ◽  
M. Li ◽  
S. Azarm
Keyword(s):  
Sensitivity Analysis ◽  
Interval Uncertainty ◽  
Minimal Amount ◽  
Engineering System ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Design Improvement ◽  
Multi Objective ◽  
System A

Uncertainty in the input parameters to an engineering system may not only degrade the system’s performance, but may also cause failure or infeasibility. This paper presents a new sensitivity analysis based approach called Design Improvement by Sensitivity Analysis (DISA). DISA analyzes the interval parameter uncertainty of a system and, using multi-objective optimization, determines an optimal combination of design improvements required to enhance performance and ensure feasibility. This is accomplished by providing a designer with options for both uncertainty reduction and, more importantly, slight design adjustments. The approach can provide improvements to a design of interest that will ensure a minimal amount of variation in the objective functions of the system while also ensuring the engineering feasibility of the system. A two stage sequential framework is used in order to effectively employ metamodeling techniques to approximate the analysis function of an engineering system and greatly increase the computational efficiency of the approach. This new approach has been applied to two engineering examples of varying difficulty to demonstrate its applicability and effectiveness.

On the Optimisation of a Double Cone Synchroniser for Improved Manual Transmission Shiftability

2002 ◽  
Author(s):  
Massimiliano Gobbi ◽  
Gianpiero Mastinu ◽  
Augusto D’Orazio ◽  
Massimo Caudano ◽  
Giorgio Faustini
Keyword(s):  
Dynamic Behaviour ◽  
Programming Approach ◽  
Approximation Procedure ◽  
Pareto Optimal ◽  
Performance Indices ◽  
Global Approximation ◽  
Multi Objective Programming ◽  
Design Variables ◽  
Design Solutions ◽  
Sensitivity Method

The paper presents a method to optimise the synchroniser of a road vehicle gearbox in order to improve shiftability and driver comfort. A multi-body physical model of the synchroniser has been developed and validated experimentally. The optimisation method is based on a Multi-objective Programming approach, and it allows to tune the thirty-two parameters of the synchroniser in order to achieve the desired dynamic behaviour of the system during a reference shift action, defined by seven performance indices. A Global Approximation procedure has been followed to solve numerically the optimisation problem. A special study has been performed and implemented in order to explore all of the feasible design solutions within the design variables domain. A global sensitivity method has been applied in order to analyse the relationships among the thirty-two design variables and the seven performance indices. Pareto-optimal design solutions have been computed in a very short time. These Pareto-optimal solutions have been checked for robustness by applying the minimum sensitivity method. The optimisation method has been applied with successful results. A number of optimised synchronisers have been defined, all of them featuring relevant improvements in the dynamic behaviour (shiftability) with respect to the reference synchroniser, aleady effective and under production.

Fuzzy multi-objective decision making approach for nuclear power plant installation

2020 ◽  
Vol 39 (5) ◽  
pp. 6339-6350
Author(s):  
Esra Çakır ◽  
Ziya Ulukan
Keyword(s):  
Linear Programming ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Energy Supply ◽  
Energy Demand ◽  
Nuclear Power ◽  
Power Plants ◽  
Total Duration ◽  
Objective Functions ◽  
Multi Objective

Due to the increase in energy demand, many countries suffer from energy poverty because of insufficient and expensive energy supply. Plans to use alternative power like nuclear power for electricity generation are being revived among developing countries. Decisions for installation of power plants need to be based on careful assessment of future energy supply and demand, economic and financial implications and requirements for technology transfer. Since the problem involves many vague parameters, a fuzzy model should be an appropriate approach for dealing with this problem. This study develops a Fuzzy Multi-Objective Linear Programming (FMOLP) model for solving the nuclear power plant installation problem in fuzzy environment. FMOLP approach is recommended for cases where the objective functions are imprecise and can only be stated within a certain threshold level. The proposed model attempts to minimize total duration time, total cost and maximize the total crash time of the installation project. By using FMOLP, the weighted additive technique can also be applied in order to transform the model into Fuzzy Multiple Weighted-Objective Linear Programming (FMWOLP) to control the objective values such that all decision makers target on each criterion can be met. The optimum solution with the achievement level for both of the models (FMOLP and FMWOLP) are compared with each other. FMWOLP results in better performance as the overall degree of satisfaction depends on the weight given to the objective functions. A numerical example demonstrates the feasibility of applying the proposed models to nuclear power plant installation problem.

Multi-Objective Design Problem of Tire Wear and Visualization of Its Pareto Solutions2

2006 ◽  
Vol 34 (3) ◽  
pp. 170-194 ◽  
Author(s):  
M. Koishi ◽  
Z. Shida
Keyword(s):  
Inverse Problems ◽  
Conceptual Design ◽  
Dimensional Space ◽  
Design Stage ◽  
Objective Functions ◽  
Pareto Solutions ◽  
Design Problems ◽  
Multi Objective ◽  
Design Engineers ◽  
Design Variables

Abstract Since tires carry out many functions and many of them have tradeoffs, it is important to find the combination of design variables that satisfy well-balanced performance in conceptual design stage. To find a good design of tires is to solve the multi-objective design problems, i.e., inverse problems. However, due to the lack of suitable solution techniques, such problems are converted into a single-objective optimization problem before being solved. Therefore, it is difficult to find the Pareto solutions of multi-objective design problems of tires. Recently, multi-objective evolutionary algorithms have become popular in many fields to find the Pareto solutions. In this paper, we propose a design procedure to solve multi-objective design problems as the comprehensive solver of inverse problems. At first, a multi-objective genetic algorithm (MOGA) is employed to find the Pareto solutions of tire performance, which are in multi-dimensional space of objective functions. Response surface method is also used to evaluate objective functions in the optimization process and can reduce CPU time dramatically. In addition, a self-organizing map (SOM) proposed by Kohonen is used to map Pareto solutions from high-dimensional objective space onto two-dimensional space. Using SOM, design engineers see easily the Pareto solutions of tire performance and can find suitable design plans. The SOM can be considered as an inverse function that defines the relation between Pareto solutions and design variables. To demonstrate the procedure, tire tread design is conducted. The objective of design is to improve uneven wear and wear life for both the front tire and the rear tire of a passenger car. Wear performance is evaluated by finite element analysis (FEA). Response surface is obtained by the design of experiments and FEA. Using both MOGA and SOM, we obtain a map of Pareto solutions. We can find suitable design plans that satisfy well-balanced performance on the map called “multi-performance map.” It helps tire design engineers to make their decision in conceptual design stage.

scholarly journals Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2775
Author(s):  
Tsubasa Takano ◽  
Takumi Nakane ◽  
Takuya Akashi ◽  
Chao Zhang
Keyword(s):  
Optimization Problem ◽  
Line Pair ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Framework ◽  
Visually Impaired People ◽  
Comprehensive Comparison ◽  
Basic Characteristics ◽  
Support Devices

In this paper, we propose a method to detect Braille blocks from an egocentric viewpoint, which is a key part of many walking support devices for visually impaired people. Our main contribution is to cast this task as a multi-objective optimization problem and exploits both the geometric and the appearance features for detection. Specifically, two objective functions were designed under an evolutionary optimization framework with a line pair modeled as an individual (i.e., solution). Both of the objectives follow the basic characteristics of the Braille blocks, which aim to clarify the boundaries and estimate the likelihood of the Braille block surface. Our proposed method was assessed by an originally collected and annotated dataset under real scenarios. Both quantitative and qualitative experimental results show that the proposed method can detect Braille blocks under various environments. We also provide a comprehensive comparison of the detection performance with respect to different multi-objective optimization algorithms.

scholarly journals A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification

2021 ◽  
Vol 26 (2) ◽  
pp. 27
Author(s):  
Alejandro Castellanos-Alvarez ◽  
Laura Cruz-Reyes ◽  
Eduardo Fernandez ◽  
Nelson Rangel-Valdez ◽  
Claudia Gómez-Santillán ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Selective Pressure ◽  
Optimization Problems ◽  
Region Of Interest ◽  
Decision Makers ◽  
Multiple Objective ◽  
Objective Functions ◽  
Multi Objective ◽  
Imperfect Knowledge ◽  
Real World Problems

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.

Analyzing the Objective Functions for Multi-Objective Optimization of the Amplifier Adaptive Control of Operating Point

2021 ◽  
Author(s):  
Erick A. Barboza ◽  
Carmelo J. A. Bastos-Filho ◽  
Daniel A. R. Chaves ◽  
Joaquim F. Martins-Filho ◽  
Leonardo D. Coelho ◽  
...  
Keyword(s):  
Adaptive Control ◽  
Operating Point ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective
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