دانلود رایگان مقاله انگلیسی روش فرا ابتکاری با NSGA-II برای طراحی شبکه پشتیبانی زنجیره تامین با توجه به توسعه محصول جدید - اشپرینگر 2018

Abstract

There are many reasons for the growing interest in developing new product projects for any firm. The most embossed reason is surviving in a highly competitive industry which the customer tastes are changing rapidly. A well-managed supply chain network can provide the most profit for firms due to considering new product development. Along with profit, customer satisfaction and production of new products are goals which lead to a more efficient supply chain. As new products appear in the market, the old products could become obsolete, and then phased out. The most important parameter in a supply chain which considers new and developed products is the time that developed and new products are introduced and old products are phased out. With consideration of the factors noted above, this study proposes to design a tri-objective multi-echelon multiproduct multi-period supply chain model, which incorporates product development and new product production and their effects on supply chain configuration. The supply chain under consideration is assumed to consist of suppliers, manufacturers, distributors and customer groups. In terms of overcoming NP-hardness of the proposed model and in order to solve the complicated problem, a non-dominated sorting genetic algorithm is employed. As there is no benchmark available in the literature, the non-dominated ranking genetic algorithm is developed to validate the results obtained and some test problems are provided to show the applicability of the proposed methodology and evaluate the performance of the algorithms.

Conclusions

This article designs a multi-period multi-echelon multiproduct supply chain model. As the aim of the article is considering the profit of the chain due to new product development, the proposed model consists of three objectives which are the maximization of total profit, maximization of the satisfaction level of customer demands and maximization of new product production. Besides the model objectives, the goal is to determine the best and efficient time for introducing new products or changing and developing the current products.

In order to tackle the complex model, the model is solved using two Pareto-based multi-objective evolutionary algorithms known as NSGA-II and NRGA. Next, 12 test problems of different sizes are considered and solved by the proposed algorithms. The parameters of the algorithms were tuned using the Taguchi method. After the algorithms were compared in terms of five performance metrics, the TOPSIS method was implemented to compare the algorithms in terms of multi-objectives metrics. Based on the results, NSGA-II as a multi-objective Pareto-based optimization algorithm, showed better results compared with NRGA.