Journal
SOFT COMPUTING
Volume 23, Issue 24, Pages 13321-13337Publisher
SPRINGER
DOI: 10.1007/s00500-019-03874-y
Keywords
Artificial bee colony; Complex network; Multi-objective optimization; Pareto optimal solutions; Three-echelon supply chain
Categories
Funding
- National Natural Science Foundation of China [61572225]
- Natural Science Foundation of the Science and Technology Department of Jilin Province, China [20180101044JC]
- Foundation of the Education Department of Jilin Province, China [JJKH20180465KJ]
- Foundation of Social Science of Jilin Province, China [2017BS28]
- Foundation of Jilin University of Finance and Economics [2018Z05]
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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.
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