An improved physical programming method for multi-objective inverse problems

2016 ◽  
Vol 52 (3-4) ◽  
pp. 1151-1159 ◽  
Author(s):  
Siguang An ◽  
Shiyou Yang ◽  
Yanan Bai ◽  
Xiushan Wu
2006 ◽  
Vol 34 (3) ◽  
pp. 170-194 ◽  
Author(s):  
M. Koishi ◽  
Z. Shida

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.


2019 ◽  
Vol 6 (04) ◽  
Author(s):  
ASHUTOSH UPADHYAYA

A study was undertaken in Bhagwanpur distributary of Vaishali Branch Canal in Gandak Canal Command Area, Bihar to optimally allocate land area under different crops (rice and maize in kharif, wheat, lentil, potato in rabi and green gram in summer) in such a manner that maximizes net return, maximizes crop production and minimizes labour requirement employing simplex linear programming method and Multi-Objective Fuzzy Linear Programming (MOFLP) method. Maximum net return, maximum agricultural production, and minimum labour required under defined constraints (including 10% affinity level of farmers to rice and wheat crops) as obtained employing Simplex method were ` 3.7 × 108, 5.06 × 107 Kg and 66,092 man-days, respectively, whereas Multi-Objective Fuzzy Linear Programming (MOFLP) method yielded compromised solution with net return, crop production and labour required as ` 2.4 × 108, 3.3 × 107Kg and 1,79,313 man-days, respectively. As the affinity level of farmers to rice and wheat crops increased from 10% to 40%, maximum net return and maximum production as obtained from simplex linear programming method and MOFLP followed a decreasing trend and minimum labour required followed an increasing trend. MOFLP may be considered as one of the best capable ways of providing a compromised solution, which can fulfill all the objectives at a time.


BIOPHYSICS ◽  
2013 ◽  
Vol 58 (2) ◽  
pp. 157-166 ◽  
Author(s):  
A. S. Pisarev ◽  
M. G. Samsonova

2014 ◽  
Vol 24 ◽  
pp. 341-362 ◽  
Author(s):  
Gilberto Reynoso-Meza ◽  
Javier Sanchis ◽  
Xavier Blasco ◽  
Sergio García-Nieto

2011 ◽  
Vol 63-64 ◽  
pp. 277-280 ◽  
Author(s):  
Hong Zhi Liu ◽  
Li Na Liu

We build up a multi-objective location model of emergency logistics center location, The model combined the construction cost of emergency logistics center with the point of shortest distance from the emergency needs of the logistics center to the limited demand, so that meet the different deployment strategies. We get satisfactory results in the practical by using parametric programming method for solving multi-objective model and verifying for the analysis of examples of this multi-objective decision. The model take into account the cost, efficiency and fairness, combined with the traditional model of coverage (covering model, gravity model and the P-median model).To meet the city's emergency different deployment strategy, the multi-objective location model solve and verify by the parametric programming method and some examples.


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