Abstract
Purpose: To improve the efficiency of information sharing among the innovation agents of customer collaborative product innovation and shorten the product design cycle, an improved genetic annealing algorithm of the time optimization was presented. Design/methodology/approach: Based on the analysis of the objective relationship between the design tasks, the paper takes job shop problems for machining model and proposes the improved genetic algorithm to solve the problems, which is based on the niche technology and thus a better product collaborative innovation design time schedule is got to improve the efficiency. Finally, through the collaborative innovation design of a certain type of mobile phone, the proposed model and method were verified to be correct and effective. Findings and Originality/value: An algorithm with obvious advantages in terms of searching capability and optimization efficiency of customer collaborative product innovation was proposed. According to the defects of the traditional genetic annealing algorithm, the niche genetic annealing algorithm was presented. Firstly, it avoided the effective gene deletions at the early search stage and guaranteed the diversity of solution; Secondly, adaptive double point crossover and swap mutation strategy were introduced to overcome the defects of long solving process and easily converging local minimum value due to the fixed crossover and mutation probability; Thirdly, elite reserved strategy was imported that optimal solution missing was avoided effectively and evolution speed was accelerated. Originality/value: Firstly, the improved genetic simulated annealing algorithm overcomes some defects such as effective gene easily lost in early search. It is helpful to shorten the calculation process and improve the accuracy of the convergence value. Moreover, it speeds up the evolution and ensures the reliability of the optimal solution. Meanwhile, it has obvious advantages in efficiency of information sharing among the innovation agents of customer collaborative product innovation. So, the product design cycle could be shortened.
Highlights
The prosperity of the market and the rapid development of technology lead to the increasing complexity of the products
The simulation results show that the proposed algorithm can avoid the stagnation, which improves the global convergence ability and attains better optimization performance
Firstly, the improved genetic simulated annealing algorithm overcomes some defects such as effective gene lost in early search
Summary
The prosperity of the market and the rapid development of technology lead to the increasing complexity of the products. Song, Yang and Yang (2008) subjectively gave the remaining processing time to task weights and tasks identified, they adopted the least finish time on-line scheduling method to study the collaborative design task sorting in different states, so as to shorten the product development cycle, and improve the efficiency of product design. When the quantity of tasks and agents participating in collaborative innovation design increasing, the problem solving time would increase or even be difficult to obtain effective solution For this reason, the paper based on of the analysis of the objective relationship between the design tasks, takes the kinds of job shop problems for machining model and proposing the improved genetic algorithm to solve the problems, which is based on the niche technology and a better time schedule of collaborative product innovation design is got to improve the efficiency. Through the collaborative innovation design of a certain type of mobile phone, the proposed model and method were verified to be correct and effective
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