1. Fast artificial bee colony algorithm with complex network and naive bayes classifier for supply chain network management.
- Author
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Jiang, Jianhua, Wu, Di, Chen, Yujun, Yu, Dianjia, Wang, Limin, and Li, Keqin
- Subjects
SUPPLY chain management ,BEES algorithm ,LEAD time (Supply chain management) ,SUPPLY chains ,MATHEMATICAL optimization ,SOFTWARE-defined networking - Abstract
In supply chain network (SCN) management, multi-objective Pareto optimization means the network can meet the demand for both minimal cost and minimal lead-time in SCN. Due to the compromise between cost and lead-time, it is a non-trivial issue to search for multi-objective Pareto optimal solutions (POS) in SCN. Furthermore, with the wide application of the internet, an increasing number of SCN applications have been based on the internet. As a result, the complexity of SCN increases exponentially with the number of suppliers increasing. It is really a big challenge to find the global multi-objective POS within a limited time in SCN management. In order to solve this problem, first, this paper proposes an artificial bee colony (ABC) optimization algorithm with two improvements: (1) a novel solution framework designed to extend the application field of the SCN based on complex network; (2) the acceleration of search speed by adopting naive Bayes classifier. Second, the paper provides a case example of optimizing a three-echelon SCN with the objective of minimizing both cost and lead-time. After the simulation with this example, it turns out that the enhanced ABC algorithm can satisfy the requirements of: (1) finding the global multi-objective POS; (2) improving the speed of finding optimal solutions in SCN management. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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