Product design-time optimization using a hybrid meta-heuristic algorithm

Production management is a function including a process of planning, directing, controlling, and organizing ongoing activities to convert inputs into the product. Industrial production management systems allow users to manage all aspects of mass processing in a centralized and decentralized environment. They focus on the overall process of production and distribution of goods and services. The purpose of this paper is to continuously improve the flow of materials so that the quality of the product and the final services are increased, and at the same time, the cost for the consumer is reduced. Since a crucial step in the process of managing the production is the product design phase, therefore, there are always many efforts to optimize and reduce product design time. In this paper, a new algorithm for scheduling design tasks has been suggested to minimize design time in production management systems using a hybrid algorithm. Considering that the scheduling of design tasks and allocating them to engineers concerning prioritizing tasks, skill levels, and preferences of designers is an NP-hard issue, many evolutionary and meta-heuristic approaches to solve this problem have been proposed. Therefore, the offered technique in this paper utilizes a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for task allocation to designers. The proposed method is simulated via a MATLAB programming tool. The results of the experiments on a set of real-world data and random data indicate that the method improves the time of product design. Considering the different design processes for different kinds of products to improve the performance of designers is not done in this research.