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Complex network oriented artificial bee colony algorithm for global bi-objective optimization in three-echelon supply chain
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DOI：10.1016/j.asoc.2018.12.006.png)
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摘要
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Finding the best flow patterns (i.e., choices of resources) for a family of products is a key part of supply chain management. It primarily focuses on reasonable selecting suppliers for every component, selecting plants for assembling every sub- or final assembly, and selecting the delivery options to bring products to customers. Different selecting operations form different cost and lead-time. Balancing a trade-off between cost and lead-time is a non-trivial problem in a three-echelon supply chain, which forms a complex network. We focus on finding the best flow patterns in which reasonable selections can be formed together to provide products or services. The objective is to minimize the bi-objective of cost and lead-time for any product. In this paper, we propose a complex network oriented artificial bee colony algorithm, which can be processed in parallel, to tackle the so-called combinatorial problem. Besides, we employ simulated annealing and gradient descent to find global Pareto optimal solutions in a supply chain network. Extensive experiments on the three-echelon supply chain network demonstrate the superiority of our proposals: (1) the proposed CN-ABC and CN-ABC-SAGD have the capability of discovering global POS in a complex three-echelon SCN; (2) the speed of searching global POS is accelerated to satisfy the requirement of its complexity of a SCN. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Artificial bee colony algorithm
Bi-objective optimization
Complex network
Three-echelon supply chain
Gradient descent
Simulated annealing
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期刊
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6.6
论文数:
1.4W
被引数:
4.8W
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