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Mathematical Problems in Engineering
Volume 2015, Article ID 260205, 15 pages
Research Article

Supplier Selection and Production Planning by Using Guided Genetic Algorithm and Dynamic Nondominated Sorting Genetic Algorithm II Approaches

Department of Industrial Engineering & Management, National Taipei University of Technology, No. 1, Section 3, Chung-Hsiao E. Road, Taipei 10608, Taiwan

Received 23 February 2015; Accepted 11 June 2015

Academic Editor: Mingshu Peng

Copyright © 2015 H. S. Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Through the global supply chain (SC), numerous firms participate in vertically integrated manufacturing, and industrial collaboration and cooperation is the norm. SC management activities, such as delivery time, quality, and defect rate, are characterized by uncertainty. Based on all of the aforementioned factors, this study established a multiobjective mathematical model, integrating the guided genetic algorithm (Guided-GA) and the nondominated sorting genetic algorithm II (NSGA-II), developed in previous studies, to improve the mechanisms of the algorithms, thereby increasing the efficiency of the model and quality of the solution. The mathematical model was used to address the problems of supplier selection, assembly sequence planning, assembly line balancing, and defect rate, to enable suppliers to respond rapidly to sales orders. The model was empirically tested using a case study, showing that it is suitable for assisting decision makers in planning production and conducting SS according to sales orders, enabling production activities to achieve maximum efficiency and the competitiveness of firms to improve.