A hybrid genetic algorithm for integrating virtual cellular manufacturing with supply chain management considering new product development. (July 2020)

• Record Type: Journal Article
• Authors: Rostami, Ahmadreza; Paydar, Mohammad Mahdi; Asadi-Gangraj, Ebrahim
• Is Part Of: Computers & industrial engineering. Volume 145(2020)
• Publication Date: 2020-07
• Subjects: Virtual cellular manufacturing -- Supply chain -- New product development -- Multi-choice goal programming -- Genetic algorithm -- Variable neighborhood search; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106565
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Integrating virtual cellular manufacturing with supply chain management. Considering NPD in the design of a dynamic virtual cellular manufacturing system. Developing a multi-objective model maximizing profit, GE and of new products. Extending a new hybrid GA-VNS to investigate large-scale problems. Abstract: The importance of issues such as responding to customers' demand, reducing production costs and better flow of materials is clear for industry owners. Manufacturers need to be able to produce products with a lower cost and higher quality in the shortest possible time to deliver the products on time to customers. Also, production systems should be able to respond quickly to changes in product design and demand without significant investment. Hence, one of the ways for increasing productivity and having a strong presence in the competitive market is to integrate virtual cellular manufacturing (VCM) into the supply chain (SC) by taking into account the new product development concept. In this study, a multi-objective mathematical model is presented to the simultaneous integration of VCM with the SC and new product development. Given that the proposed model of this study is multi-objective, a multi-choice goal programming with a utility function (MCGP-U) has been used to solve the research problem. Also, the MCGP-U is implemented in a new hybrid genetic algorithm (GA), which is the integration of a GA and a variable neighborhood search (VNS), to tackle large-scale instances. Finally, a comparison is done between the results of MCGP-U and MCGP-GA-VNS.