Multiple-platform based product family design for mass customization using a modified genetic algorithm

A successful product family design method should achieve an optimal tradeoff among a set of conflicting objectives, which involves maximizing commonality across the family of products with the prerequisite of satisfying customers’ performance requirements. Optimization based methods are experiencing new found use in product family design to resolve the inherent tradeoff between commonality and distinctiveness that exists within a product family. This paper presents and develops a 2-level chromosome structured genetic algorithm (2LCGA) to simultaneously determine the optimal settings for the product platform and corresponding family of products, by automatically varying the amount of platform commonality within the product family during a single optimization process. The single-stage approach can yield improvements in the overall performance of the product family compared with two-stage approaches, in which the first stage involves determining the best settings for the platform variables and values of unique variables are found for each product in the second stage. The augmented scope of 2LCGA allows multiple platforms to be considered during product family optimization, offering opportunities for superior overall design by more efficacious tradeoffs between commonality and performance. The effectiveness of the proposed approach is demonstrated through the design of a family of universal electric motors and comparison against previous work.     

A cooperative coevolutionary algorithm for design of platform-based mass customized products

As a new business model, mass customization (MC) intends to enable enterprises to comply with customer requirements at mass production efficiencies. A widely advocated approach to implement MC is platform product customization (PPC). In this approach, a product variant is derived from a given product platform to satisfy customer requirements. Adaptive PPC is such a PPC mode in which the given product platform has a modular architecture where customization is achieved by swapping standard modules and/or scaling modular components to formulate multiple product variants according to market segments and customer requirements. Adaptive PPC optimization includes structural configuration and parametric optimization. This paper presents a new method, namely, a cooperative coevolutionary algorithm (CCEA), to solve the two interrelated problems of structural configuration and parametric optimization in adaptive PPC. The performance of the proposed algorithm is compared with other methods through a set of computational experiments. The results show that CCEA outperforms the existing hierarchical evolutionary approaches, especially for large-scale problems tested in the experiments. From the experiments, it is also noticed that CCEA is slow to converge at the beginning of evolutionary process. This initial slow convergence property of the method improves its searching capability and ensures a high quality solution.     

A heuristic genetic algorithm for product portfolio planning

Product portfolio planning has been recognized as a critical decision facing all companies across industries. It aims at the selection of a near-optimal mix of products and attribute levels to offer in the target market. It constitutes a combinatorial optimization problem that is deemed to be NP-hard in nature. Conventional enumeration-based optimization techniques become inhibitive given that the number of possible combinations may be enormous. Genetic algorithms have been proven to excel in solving combinatorial optimization problems. This paper develops a heuristic genetic algorithm for solving the product portfolio planning problem more effectively. A generic encoding scheme is introduced to synchronize product portfolio generation and selection coherently. The fitness function is established based on a shared surplus measure leveraging both the customer and engineering concerns. An unbalanced index is proposed to model the elitism of product portfolio solutions.     

A methodology of developing product family architecture for mass customization

Mass customization, aiming at delivering an increasing product variety that best serves customer needs while keeping mass production efficiency, has recently received numerous attention and popularity in industry and academia alike. This paper presents a methodology of developing product family architecture (PFA) to rationalize product development for mass customization. Systematic steps are developed to formulate a PFA in terms of functional, technical and physical views. The diverse needs of customers are matched with the capabilities of a firm through systematic planning of modularity in three consecutive views. The development of a PFA provides a unifying integration platform to synchronize market positioning, commonality employment and manufacturing scale of economy across the entire product realization process. A case study in an electronics company is reported to illustrate the potential and the feasibility of PFA methodology.     

A generic genetic algorithm for product family design

Product family design (PFD) has been well recognized as an effective means to satisfy diverse market niches while maintaining the economies of scale and scope. PFD essentially entails a configuration problem by “combination," where combinatorial explosion always occurs and is known to be mathematically intractable or NP-hard. Although genetic algorithms (GAs) have been proven to excel in solving combinatorial optimization problems, it is difficult to adopt the traditional GA to deal with the complex data and interrelationships inherent in the PFD problem. This paper proposes a generic genetic algorithm (GGA) for PFD. A generic encoding scheme is developed to adapt to diverse PFD scenarios. A hybrid constraint-handling strategy is proposed to handle complex and distinguishing constraints at different stages along the evolutionary process. The design and implementation procedures of the GGA are discussed in detail. An application of the proposed GGA to motor family design is reported. The GGA efficiency is also tested through efficiency analysis in terms of the probability of generating feasible solutions, as well as through analysis of the GGA complexity.     

Product platform two-stage quality optimization design based on multiobjective genetic algorithm

Product platform design (PFD) has been recognized as an effective means to satisfy diverse market niches while maintaining the economies of scale and scope. Numerous optimization-based approaches have been proposed to help resolve the tradeoff between platform commonality and the ability to achieve distinct performance targets for each variant. In this study, we propose a two-stage multiobjective optimization-based platform design methodology (TMOPDM) for solving the product family problem using a multiobjective genetic algorithm. In the first stage, the common product platform is identified using a nondominated sorting genetic algorithm II (NSGA-II); In the second stage, each individual product is designed around the common platform such that the functional requirements of the product are best satisfied. The design of a family of traction machine is used as an example to benchmark the effectiveness of the proposed approach against previous approachs.     

Evolving choice structures for genetic programming

It is quite difficult but essential for Genetic Programming (GP) to evolve the choice structures. Traditional approaches usually ignore this issue. They define some “if-structures” functions according to their problems by combining “if-else” statement, conditional criterions and elemental functions together. Obviously, these if-structure functions depend on the specific problems and thus have much low reusability. Based on this limitation of GP, in this paper we propose a kind of termination criterion in the GP process named “Combination Termination Criterion” (CTC). By testing CTC, the choice structures composed of some basic functions independent to the problems can be evolved successfully. Theoretical analysis and experiment results show that our method can evolve the programs with choice structures effectively within an acceptable additional time.     

A Hybrid approach for integer programming combining genetic algorithms, linear programming and ordinal optimization

Hybrid methods are promising tools in integer programming, as they combine the best features of different methods in a complementary fashion. This paper presents such a framework, integrating the notions of genetic algorithm, linear programming, and ordinal optimization in an effort to shorten computation times for large and/or difficult integer programming problems. Capitalizing on the central idea of ordinal optimization and on the learning capability of genetic algorithms to quickly generate good feasible solutions, and then using linear programming to solve the problem that results from fixing the integer part of the solution, one may be able to obtain solutions that are close to optimal. Indeed ordinal optimization guarantees the quality of the solutions found. Numerical testing on a real-life complex scheduling problem demonstrates the effectiveness and efficiency of this approach.     

An efficient decomposed multiobjective genetic algorithm for solving the joint product platform selection and product family design problem with generalized commonality

Product family optimization involves not only specifying the platform from which the individual product variants will be derived, but also optimizing the platform design and the individual variants. Typically these steps are performed separately, but we propose an efficient decomposed multiobjective genetic algorithm to jointly determine optimal (1) platform selection, (2) platform design, and (3) variant design in product family optimization. The approach addresses limitations of prior restrictive component sharing definitions by introducing a generalized two-dimensional commonality chromosome to enable sharing components among subsets of variants. To solve the resulting high dimensional problem in a single stage efficiently, we exploit the problem structure by decomposing it into a two-level genetic algorithm, where the upper level determines the optimal platform configuration while each lower level optimizes one of the individual variants. The decomposed approach improves scalability of the all-in-one problem dramatically, providing a practical tool for optimizing families with more variants. The proposed approach is demonstrated by optimizing a family of electric motors. Results indicate that (1) decomposition results in improved solutions under comparable computational cost and (2) generalized commonality produces families with increased component sharing under the same level of performance. A preliminary version of this paper was presented at the 2007 AIAA Multidisciplinary Design Optimization Specialists Conference.     

基于DSM的复杂产品开发流程优化遗传算法

为减少产品开发过程中的返工迭代,提出一种基于设计结构矩阵(DSM)理论的多目标流程优化遗传算法.通过优化任务执行顺序,减少产品开发过程中的返工以压缩进度和降低成本.该优化算法是一种改进的遗传(GA)算法,在适应度函数中考虑了时间和费用两个指标;在选择、交叉、变异算子中采用了优解保持策略.仿真结果表明,对于高任务耦合度的产品开发项目,该优化算法能使开发时间压缩30%～40%,费用降低7%～2O%.     

Hybrid evolutionary algorithm and application to structural optimization

This paper introduces a new evolutionary algorithm with a globally stochastic but locally heuristic search strategy. It is implemented by incorporating a modified micro-genetic algorithm with two local optimization operators. Performance tests using two benchmarking functions demonstrate that the new algorithm has excellent convergence performance when applied to multimodal optimization problems. The number of objective function evaluations required to obtain global optima is only 3.5–3.7% of that of using the conventional micro-genetic algorithm. The new algorithm is used to optimize the design of an 18-bar truss, with the aim of minimizing its weight while meeting the stress, section area, and geometry constraints. The corresponding optimal design is obtained with considerably fewer computational operations than required for the existing algorithms.     

遗传算法采用进化方向算子的几种策略

提出了采用实数编码情况下应用进化方向算子的几种策略,包括单亲进化方向算子、双亲进化方向算子以及无轮盘赌选择的双亲进化方向算子策略,并进行了数值仿真。仿真结果表明,灵活使用方向进化算子以及遗传操作可大大提高遗传算法的全局搜索能力。     

解非线性约束规划问题的新型多目标遗传算法

给出非线性约束规划问题的一种新解法。把带约束的非线性规划问题转化成为两个目标的多目标优化问题,并为转化后的多目标优化模型设计了一种新型多目标遗传算法,数据实验表明该算法对带约束的非线性规划问题求解是非常有效的。     

A self-organizing genetic algorithm for multimodal function optimization

A genetic algorithm (GA) has control parameters that must be determined before execution. We propose a self-organizing genetic algorithm (SOGA) as a multimodal function optimizer which sets GA parameters such as population size, crossover probability, and mutation probability adaptively during the execution of a genetic algorithm. In SOGA, GA parameters change according to the fitnesses of individuals. SOGA and other approaches for adapting operator probabilities in GAs are discussed. The validity of the proposed algorithm is verified in simulation examples, including system identification. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

Available-to-promise based bidding decision by fuzzy mathematical programming and genetic algorithm

The present study attempts to integrate bidding decisions with order promising and production planning to enhance supplier profitability and service level. This study formulates the bid price and production plan as a mixed integer programming model with fuzzy constraints. The fuzzy constraints represent the decision-maker’s subjective judgment regarding the customer’s price tolerance. The proposed model combines the advanced available-to-promise (AATP) concept to find optimum resource allocation and enable accurate estimations of production costs and delivery dates. The proposed solution procedure determines the optimum bid price by striking a compromise between profitability and the possibility to win the contract. This study develops a genetic algorithm to solve this problem, and provides computer simulated experiments to evaluate the performance of the proposed approach.     

Genetic programming for prediction and control

The relatively new field of genetic programming has received a lot of attention during the last few years. This is because of its potential for generating functions which are able to solve specific problems. This paper begins with an extensive overview of the field, highlighting its power and limitations and providing practical tips and techniques for the successful application of genetic programming in general domains. Following this, emphasis is placed on the application of genetic programming to prediction and control. These two domains are of extreme importance in many disciplines. Results are presented for an oral cancer prediction task and a satellite attitude control problem. Finally, the paper discusses how the convergence of genetic programming can be significantly speeded up through bulk synchronous model parallelisation.     

A variable-grouping based genetic algorithm for large-scale integer programming

To effectively reduce the dimensionality of search space, this paper proposes a variable-grouping based genetic algorithm (VGGA) for large-scale integer programming problems (IPs). The VGGA first groups IP’s decision variables based on the optimal solution to the IP’s continuous relaxation problem, and then applies a standard genetic algorithm (GA) to the subproblem for each group of variables. We compare the VGGA with the standard GA and GAs based on even variable-grouping by applying them to solve randomly generated convex quadratic knapsack problems and integer knapsack problems. Numerical results suggest that the VGGA is superior to the standard GA and GAs based on even variable-grouping both on computation time and solution quality.     

Multi-objective reliability optimization for dissimilar-unit cold-standby systems using a genetic algorithm

A genetic algorithm approach is used to solve a multi-objective discrete reliability optimization problem in a k dissimilar-unit non-repairable cold-standby redundant system. Each unit is composed of a number of independent components with generalized Erlang distributions arranged in a series–parallel configuration. There are multiple component choices with different distribution parameters available for being replaced with each component of the system. The objective of the reliability optimization problem is to select the best components, from the set of available components, to be placed in the standby system in order to minimize the initial purchase cost of the system, maximize the system MTTF (mean time to failure), minimize the system VTTF (variance of time to failure) and also maximize the system reliability at the mission time. Finally, we apply a genetic algorithm with double strings using continuous relaxation based on reference solution updating (GADSCRRSU) to solve this multi-objective problem, using goal attainment formulation. The results are also compared against the results of a discrete-time approximation technique to show the efficiency of the proposed GA approach.     

Stochastic simulation-based genetic algorithm for chance constraint programming problems with continuous random variables

In this article, we present a stochastic simulation-based genetic algorithm for solving chance constraint programming problems, where the random variables involved in the parameters follow any continuous distribution. Generally, deriving the deterministic equivalent of a chance constraint is very difficult due to complicated multivariate integration and is only possible if the random variables involved in the chance constraint follow some specific distribution such as normal, uniform, exponential and lognormal distribution. In the proposed method, the stochastic model is directly used. The feasibility of the chance constraints are checked using stochastic simulation, and the genetic algorithm is used to obtain the optimal solution. A numerical example is presented to prove the efficiency of the proposed method.