scholarly journals A Decomposed Genetic Algorithm for Solving the Joint Product Family Optimization Problem

2007 ◽  
Author(s):  
Aida Khajavirad ◽  
Jeremy Michalek ◽  
Timothy Simpson
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Product Family ◽  
Joint Product ◽  
Product Family Optimization

A Single-Stage Gradient-Based Approach for Solving the Joint Product Family Platform Selection and Design Problem Using Decomposition

2007 ◽  
Author(s):  
Aida Khajavirad ◽  
Jeremy J. Michalek
Keyword(s):  
Design Problem ◽  
Optimization Problem ◽  
Computational Cost ◽  
Product Family ◽  
Single Stage ◽  
System Level ◽  
Continuous Space ◽  
Joint Product ◽  
Product Family Optimization ◽  
Gradient Based

A core challenge in product family optimization is to develop a single-stage approach that can optimally select the set of variables to be shared in the platform(s) while simultaneously designing the platform(s) and variants within an algorithm that is efficient and scalable. However, solving the joint product family platform selection and design problem involves significant complexity and computational cost, so most prior methods have narrowed the scope by treating the platform as fixed or have relied on stochastic algorithms or heuristic two-stage approaches that may sacrifice optimality. In this paper, we propose a single-stage approach for optimizing the joint problem using gradient-based methods. The combinatorial platform-selection variables are relaxed to the continuous space by applying the commonality index and consistency relaxation function introduced in a companion paper. In order to improve scalability properties, we exploit the structure of the product family problem and decompose the joint product family optimization problem into a two-level optimization problem using analytical target cascading so that the system-level problem determines the optimal platform configuration while each subsystem optimizes a single product in the family. Finally, we demonstrate the approach through optimization of a family of ten bathroom scales; Results indicate encouraging success with scalability and computational expense.

Multidisciplinary design optimization of vehicle chassis product family

2021 ◽  
pp. 095440542110366
Author(s):  
Xiaokai Chen ◽  
Chenyu Wang ◽  
Guobiao Shi ◽  
Mingkai Zeng
Keyword(s):  
Design Optimization ◽  
Design Process ◽  
Multidisciplinary Design Optimization ◽  
Optimization Design ◽  
Optimization Problem ◽  
Product Family ◽  
Multidisciplinary Design ◽  
Product Family Design ◽  
Product Platforms ◽  
Product Family Optimization

In order to improve the performance of automotive product platforms and product families while keeping high development efficiency, a product family optimization design method that combines shared variable decision-making and multidisciplinary design optimization (MDO) is proposed. First, the basic concepts related to product family design optimization were clarified. Then, the mathematical description and MDO model of the product family optimization problem were established, and the improved product family design process was given. Finally, for the chassis product family optimization problem of an automotive product platform, the effectiveness of the proposed optimization method, and design process were exemplified. The results show that the collaboratively optimized product family can effectively handle the coordination between multiple products and multiple targets, compared to Non-platform development, it can maximize the generalization rate of vehicle parts and components under the premise of ensuring key performance, and give full play to the advantages of product platforms.

scholarly journals The Use of a Simulation Model for High-Runner Strategy Implementation in Warehouse Logistics

2020 ◽  
Vol 12 (23) ◽  
pp. 9818
Author(s):  
Gabriel Fedorko ◽  
Vieroslav Molnár ◽  
Nikoleta Mikušová
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Optimization Problem ◽  
Software Tool ◽  
Time Saving ◽  
General Validity ◽  
Resistance Function ◽  
Genetic Algorithm Method ◽  
Working Day ◽  
Enterprise Logistics

This paper examines the use of computer simulation methods to streamline the process of picking materials within warehouse logistics. The article describes the use of a genetic algorithm to optimize the storage of materials in shelving positions, in accordance with the method of High-Runner Strategy. The goal is to minimize the time needed for picking. The presented procedure enables the creation of a software tool in the form of an optimization model that can be used for the needs of the optimization of warehouse logistics processes within various types of production processes. There is a defined optimization problem in the form of a resistance function, which is of general validity. The optimization is represented using the example of 400 types of material items in 34 categories, stored in six rack rows. Using a simulation model, a comparison of a normal and an optimized state is realized, while a time saving of 48 min 36 s is achieved. The mentioned saving was achieved within one working day. However, the application of an approach based on the use of optimization using a genetic algorithm is not limited by the number of material items or the number of categories and shelves. The acquired knowledge demonstrates the application possibilities of the genetic algorithm method, even for the lowest levels of enterprise logistics, where the application of this approach is not yet a matter of course but, rather, a rarity.

scholarly journals An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jing Xiao ◽  
Jing-Jing Li ◽  
Xi-Xi Hong ◽  
Min-Mei Huang ◽  
Xiao-Min Hu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Experimental Comparison ◽  
Software Project ◽  
Project Portfolio ◽  
Multiobjective Evolutionary Algorithms ◽  
Reference Distance ◽  
Nondominated Sorting

As it is becoming extremely competitive in software industry, large software companies have to select their project portfolio to gain maximum return with limited resources under many constraints. Project portfolio optimization using multiobjective evolutionary algorithms is promising because they can provide solutions on the Pareto-optimal front that are difficult to be obtained by manual approaches. In this paper, we propose an improved MOEA/D (multiobjective evolutionary algorithm based on decomposition) based on reference distance (MOEA/D_RD) to solve the software project portfolio optimization problems with optimizing 2, 3, and 4 objectives. MOEA/D_RD replaces solutions based on reference distance during evolution process. Experimental comparison and analysis are performed among MOEA/D_RD and several state-of-the-art multiobjective evolutionary algorithms, that is, MOEA/D, nondominated sorting genetic algorithm II (NSGA2), and nondominated sorting genetic algorithm III (NSGA3). The results show that MOEA/D_RD and NSGA2 can solve the software project portfolio optimization problem more effectively. For 4-objective optimization problem, MOEA/D_RD is the most efficient algorithm compared with MOEA/D, NSGA2, and NSGA3 in terms of coverage, distribution, and stability of solutions.

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

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