scholarly journals Design Optimization of an Automotive Component in Product Development

2019 ◽  
Vol 9 (1) ◽  
pp. 6129-6135
Keyword(s):  
Simulated Annealing ◽  
Design Methodology ◽  
Simulated Annealing Algorithm ◽  
Parametric Modeling ◽  
Leaf Spring ◽  
Hybrid Design ◽  
Maximum Deflection ◽  
Weight Optimization ◽  
Annealing Algorithm ◽  
Parabolic Leaf Spring

This paper will briefly explain the engineer’s approach to optimize the conservative design of parabolic Leaf Spring in various steps. The Hybrid Design methodology and technique is employed for Size. Shape and weight Optimization by means of Parametric Modeling and Optimization-Simulated annealing algorithm. Various designs have been created, analyzed and its Maximum deflection and stress is compared with the material’s yield strength.

A multiproduct economic order quantity model with simulated annealing application

2017 ◽  
Vol 12 (1) ◽  
pp. 119-142 ◽  
Author(s):  
Valdecy Pereira ◽  
Helder Gomes Costa
Keyword(s):  
Simulated Annealing ◽  
Design Methodology ◽  
Simulated Annealing Algorithm ◽  
Economic Order Quantity ◽  
Economic Order ◽  
Optimal Solutions ◽  
Order Quantity ◽  
Single Product ◽  
Content Type ◽  
Annealing Algorithm

Purpose This paper aims to present a set of five models for the economic order quantity problem. Four models solve problems for a single product: incremental discounts with or without backorders and all-unit discounts with or without backorders, and the last model solves problems for the multiproduct case. Design/methodology/approach A basic integer non-linear model with binary variables is presented, and its flexible structure allows for all five models to be utilised with minor modifications for adaptation to individual situations. The multiproduct model takes into consideration the work of Chopra and Meindl (2012), who studied two types of product aggregations: full and adaptive. To find optimal or near-optimal solutions for the multiproduct case, the authors propose a simulated annealing metaheuristic application. Numerical examples are presented to improve the comprehension of each model, and the authors also present the efficiency of the simulated annealing algorithm through an example that aggregates 50 products, each one with different discount schemes and some allowing backorders. Findings Our model proved to be efficient at finding optimal or near optimal solutions even when confronted with mathematical complexities such as the allowance of backorders and incremental discounts. Originality/value Finally our model can process a mix of products with different discount schemes at the same time, and the simulated annealing metaheuristics could find optimal or near optimal solutions with very few iterations.

Parameter Optimization of the Sheet Metal Shearing Process in the Manufacturing of Leaf Spring Assembly Using the Grey Taguchi Method and Simulated Annealing Algorithm

2011 ◽  
Vol 314-316 ◽  
pp. 2458-2463 ◽  
Author(s):  
Gopalsamy Gopalaramasubramaniyan ◽  
V.S. Senthil Kumar
Keyword(s):  
Simulated Annealing ◽  
Taguchi Method ◽  
Sheet Metal ◽  
Simulated Annealing Algorithm ◽  
Optimal Level ◽  
Leaf Spring ◽  
Optimal Process ◽  
Grey Relational Grade ◽  
Annealing Algorithm ◽  
Parameter Values

The processing of Sheet metal includes the number of process, which forms the finished product. Shearing is the preliminary process in the sheet metal processing industries. In this paper, an attempt has been made to develop an approach to predict the optimal process parameters such as Squareness and length for the Shearing process on sheet metal. The Grey based Taguchi method is used to produce the optimal level of the selected controlled factors. The Grey relational grade is calculated and the statistical analysis of variance results the significant parameters. The obtained model is further processed with the simulated annealing algorithm to get the optimal process parameter values.

A New Solution for Maxterm Problem in Trigonometric Functions by Simulated Annealing Algorithm

2013 ◽  
Vol 4 (2) ◽  
pp. 20-28
Author(s):  
Farhad Soleimanian Gharehchopogh ◽  
Hadi Najafi ◽  
Kourosh Farahkhah
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Trigonometric Functions ◽  
Matlab Software ◽  
Annealing Algorithm ◽  
Do So

The present paper is an attempt to get total minimum of trigonometric Functions by Simulated Annealing. To do so the researchers ran Simulated Annealing. Sample trigonometric functions and showed the results through Matlab software. According the Simulated Annealing Solves the problem of getting stuck in a local Maxterm and one can always get the best result through the Algorithm.

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