Multi-Objective Single Product Robust Optimization: An Integrated Design and Marketing Approach

2005 ◽  
Vol 128 (4) ◽  
pp. 884-892 ◽  
Author(s):  
B. Besharati ◽  
L. Luo ◽  
S. Azarm ◽  
P. K. Kannan
Keyword(s):  
Integrated Design ◽  
Design Domain ◽  
Sampled Data ◽  
Single Product ◽  
Multi Objective ◽  
Customer Preferences ◽  
Acceptable Range ◽  
Design Alternatives ◽  
Objective Performance ◽  
Marketing Approach

We present an integrated design and marketing approach to facilitate the generation of an optimal robust set of product design alternatives to carry forward to the prototyping stage. The approach considers variability in both (i) engineering design domain, and (ii) customer preferences in marketing domain. In the design domain, the approach evaluates performance and robustness of a design alternative due to variations in its uncontrollable parameters. In the marketing domain, in addition to considering competitive product offerings, the approach considers designs that are robust in customer preferences with respect to: (1) the variations in the design domain, and (2) the inherent variations in the estimates of preferences given the fit of the preference model to the sampled data. Our overall goal is to obtain design alternatives that are multi-objectively robust and optimal, i.e., (1) are optimal for nominal values of parameters, and (2) are within a known acceptable range in their multi-objective performance, and (3) maintain feasibility even when they are subject to applications and environments that are different from nominal or standard laboratory conditions. We illustrate the highlights of our approach with the design of a corded power tool example.

An Integrated Robust Design and Marketing Approach for Product Design Selection Process

2004 ◽  
Author(s):  
B. Besharati ◽  
L. Luo ◽  
S. Azarm ◽  
P. K. Kannan
Keyword(s):  
Engineering Design ◽  
Selection Process ◽  
Design Domain ◽  
Sampling Errors ◽  
Customer Preferences ◽  
Design Alternatives ◽  
Feasibility Robustness ◽  
Design Selection ◽  
The Impact ◽  
Marketing Approach

We present an integrated engineering design and marketing approach to facilitate the selection of a robust set of product designs to carry forward to the prototype stage. Our approach considers variability (i.e., noise or uncertainty) in both (i) engineering design domain, and (ii) customer preferences in marketing domain, to prune a set of design alternatives to a manageable size. In the design domain, our approach evaluates performance and feasibility robustness of a design alternative when there are variations in uncontrollable parameters. The goal of our approach in the design domain is to obtain a set of design alternatives that shows the best possible performance while maintaining feasibility even if the alternatives are subject to applications and environments that are different from their standard laboratory conditions (i.e., nominal parameter values). In the marketing domain, our approach considers the impact of performance variations in different usage situations and conditions on customer preferences and the uncertainties and sampling errors in estimating customer preferences. In addition, competitive products and their positions are considered in pruning the set of design alternatives. We illustrate our approach in the context of the design of a cordless power tool example, which highlights the advantages of using our approach.

Integrated Design and Multi-Objective Optimization of a Single Stage Heat-Pump Turbocompressor

2013 ◽  
Author(s):  
J. Schiffmann
Keyword(s):  
Design Optimization ◽  
High Efficiency ◽  
Integrated Design ◽  
Heat Pumps ◽  
Small Scale ◽  
Multi Objective Optimization ◽  
Design Constraints ◽  
Multi Objective ◽  
Wide Range ◽  
Standard Design

Small scale turbomachines in domestic heat pumps reach high efficiency and provide oil-free solutions which improve heat-exchanger performance and offer major advantages in the design of advanced thermodynamic cycles. An appropriate turbocompressor for domestic air based heat pumps requires the ability to operate on a wide range of inlet pressure, pressure ratios and mass flows, confronting the designer with the necessity to compromise between range and efficiency. Further the design of small-scale direct driven turbomachines is a complex and interdisciplinary task. Textbook design procedures propose to split such systems into subcomponents and to design and optimize each element individually. This common procedure, however, tends to neglect the interactions between the different components leading to suboptimal solutions. The authors propose an approach based on the integrated philosophy for designing and optimizing gas bearing supported, direct driven turbocompressors for applications with challenging requirements with regards to operation range and efficiency. Using previously validated reduced order models for the different components an integrated model of the compressor is implemented and the optimum system found via multi-objective optimization. It is shown that compared to standard design procedure the integrated approach yields an increase of the seasonal compressor efficiency of more than 12 points. Further a design optimization based sensitivity analysis allows to investigate the influence of design constraints determined prior to optimization such as impeller surface roughness, rotor material and impeller force. A relaxation of these constrains yields additional room for improvement. Reduced impeller force improves efficiency due to a smaller thrust bearing mainly, whereas a lighter rotor material improves rotordynamic performance. A hydraulically smoother impeller surface improves the overall efficiency considerably by reducing aerodynamic losses. A combination of the relaxation of the 3 design constraints yields an additional improvement of 6 points compared to the original optimization process. The integrated design and optimization procedure implemented in the case of a complex design problem thus clearly shows its advantages compared to traditional design methods by allowing a truly exhaustive search for optimum solutions throughout the complete design space. It can be used for both design optimization and for design analysis.

Monitoring Perioperative Services Using 3D Multi-Objective Performance Frontiers

2021 ◽  
Vol 45 (3) ◽  
Author(s):  
Andrea J. Elhajj ◽  
Donna M. Rizzo ◽  
Gary C. An ◽  
Jaideep J. Pandit ◽  
Mitchell H. Tsai
Keyword(s):  
Multi Objective ◽  
Objective Performance

Efficient Computation of Probabilistic Dominance in Multi-objective Optimization

2021 ◽  
Vol 1 (4) ◽  
pp. 1-26
Author(s):  
Faramarz Khosravi ◽  
Alexander Rass ◽  
Jürgen Teich
Keyword(s):  
Optimization Problems ◽  
Statistical Tests ◽  
Optimization Techniques ◽  
Simultaneous Optimization ◽  
Sampled Data ◽  
Efficient Computation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Comparison Operator

Real-world problems typically require the simultaneous optimization of multiple, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions. To cope with such uncertainties, stochastic and robust optimization techniques are widely studied aiming to distinguish candidate solutions with uncertain objectives specified by confidence intervals, probability distributions, sampled data, or uncertainty sets. In this scope, this article first introduces a novel empirical approach for the comparison of candidate solutions with uncertain objectives that can follow arbitrary distributions. The comparison is performed through accurate and efficient calculations of the probability that one solution dominates the other in terms of each uncertain objective. Second, such an operator can be flexibly used and combined with many existing multi-objective optimization frameworks and techniques by just substituting their standard comparison operator, thus easily enabling the Pareto front optimization of problems with multiple uncertain objectives. Third, a new benchmark for evaluating uncertainty-aware optimization techniques is introduced by incorporating different types of uncertainties into a well-known benchmark for multi-objective optimization problems. Fourth, the new comparison operator and benchmark suite are integrated into an existing multi-objective optimization framework that features a selection of multi-objective optimization problems and algorithms. Fifth, the efficiency in terms of performance and execution time of the proposed comparison operator is evaluated on the introduced uncertainty benchmark. Finally, statistical tests are applied giving evidence of the superiority of the new comparison operator in terms of \epsilon -dominance and attainment surfaces in comparison to previously proposed approaches.

Multi-objective optimization for safety and reliability trade-off: Optimization and results processing

2018 ◽  
Vol 232 (6) ◽  
pp. 661-676
Author(s):  
Cristina Johansson ◽  
Johan Ölvander ◽  
Micael Derelöv
Keyword(s):  
Decision Making ◽  
Solid Base ◽  
Multi Objective Optimization ◽  
Specific System ◽  
Multi Objective ◽  
Trade Offs ◽  
Design Alternatives ◽  
Safety And Reliability ◽  
Potential Issue ◽  
Selection Of

In early design phases, it is vital to be able to screen the design space for a set of promising design alternatives for further study. This article presents a method able to balance several objectives of different mathematical natures, with high impact on the design choices. The method (MOSART) handles multi-objective optimization for safety and reliability trade-offs. The article focuses on optimization problem approach and processing of results as a base for decision-making. The output of the optimization step is the selection of specific system elements obtaining the best balance between the targets. However, what is a good base for decision can easily transform into too much information and overloading of the decision-maker. To solve this potential issue, from a set of Pareto optimal solutions, a smaller sub-set of selected solutions are visualized and filtered out using preference levels of the objectives, yielding a solid base for decision-making and valuable information on potential solutions. Trends were observed regarding each system element and discussed while processing the results of the analysis, supporting the decision of one final best solution.

scholarly journals Multi-objective performance optimization of ORC cycle based on improved ant colony algorithm

Open Physics ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 48-59 ◽  
Author(s):  
Rong He ◽  
Xinli Wei ◽  
Nasruddin Hassan
Keyword(s):  
Performance Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Second Law Of Thermodynamics ◽  
Optimization Method ◽  
Ant Colony ◽  
Multi Objective ◽  
Objective Performance ◽  
Performance Evaluation Index ◽  
Improved Ant Colony Algorithm

Abstract To solve the problem of multi-objective performance optimization based on ant colony algorithm, a multi-objective performance optimization method of ORC cycle based on an improved ant colony algorithm is proposed. Through the analysis of the ORC cycle system, the thermodynamic model of the ORC system is constructed. Based on the first law of thermodynamics and the second law of thermodynamics, the ORC system evaluation model is established in a MATLAB environment. The sensitivity analysis of the system is carried out by using the system performance evaluation index, and the optimal working parameter combination is obtained. The ant colony algorithm is used to optimize the performance of the ORC system and obtain the optimal solution. Experimental results show that the proposed multi-objective performance optimization method based on the ant colony algorithm for the ORC cycle needs a shorter optimization time and has a higher optimization efficiency.

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