Abstract
With the economic globalization and market constantly subdivision, the life cycle of product and the development cycle increasingly shorter. In the development process of new products, more and more scholars have realized that if suppliers collectively participate in the initial stage of product development, enterprises will effectively save the cost and the time, dramatically increase the product quality and the production flexibility. Recently, several research papers proposed some relevant methods integrating supplier selection into product family design. In this paper, a one-step unified optimization model, which integrates product family design with supplier selection, is established. The optimization model with the objective of maximizing the total profits of product family is based on multinomial logit consumer choice rule, which is regarded to be more flexible and suitable to formulate consumer choice behavior. To solve the proposed optimization model, genetic algorithm is adopted. The optimization results of integrating supplier selection and product family design and that of separating supplier selection and product family design, and the optimization results under multinomial logit rule and that under the deterministic choice rule are compared respectively. Meanwhile, in the case study, sensitivity analysis of the model is also performed.
Original language | English |
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Title of host publication | 2013 25th Chinese Control and Decision Conference, CCDC 2013 |
Pages | 4634-4639 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 28 Aug 2013 |
Externally published | Yes |
Event | 2013 25th Chinese Control and Decision Conference, CCDC 2013 - Guiyang, China Duration: 25 May 2013 → 27 May 2013 |
Conference
Conference | 2013 25th Chinese Control and Decision Conference, CCDC 2013 |
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Country/Territory | China |
City | Guiyang |
Period | 25/05/13 → 27/05/13 |
Keywords
- genetic algorithm
- multinomial logit consumer choice rule
- product family
- supplier selection
ASJC Scopus subject areas
- Control and Systems Engineering