Discovering Potential Partners via Projection-Based Link Prediction in the Supply Chain Network
, Qian Chen- DOI
- 10.2991/ijcis.d.200813.001How to use a DOI?
- Keywords
- Supply chain network; Resilience; Potential partners; Link prediction
- Abstract
As reserving a certain number of potential partners plays a significant role in alleviating existing partners' collaborative interruption risks, we investigate the process of discovering potential partners to improve the supply chain network's resilience. Most of the existing research confines its focus on discovering potential partners in the supply chain on the basic of sufficient partners' information, but very few works consider discovering potential partners in the supply chain network according to the structure of the supply chain network when the partner information is insufficient. In this situation, a novel model which applies projection-based link prediction method to discover potential partners in the supply chain network is proposed. The proposed model is composed of three stages. The first stage is predicting the candidate partnerships links based on the projection one-model graph which is transformed from the supply chain network according to its structure. The second stage is discovering potential partners by comparing the acquired connectivity of candidate partnership links with the maximal connectivity of existent partnerships. In the third stage, a resilience evaluation framework considering the both connectivity and flexibility indexes is presented to determine whether the supply chain network's agility is improved. In the experimental design, a supply chain network which is formed from a real dataset containing mobile phone suppliers, manufacturers and packers is used to evaluate the proposed algorithm's prediction accuracy. The results reveal that the algorithm achieves highest area under curve (AUC) scores and the supply chain network's resilience is improved by discovering potential partners.
- Copyright
- © 2020 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Zhi-Gang Lu AU - Qian Chen PY - 2020 DA - 2020/08/21 TI - Discovering Potential Partners via Projection-Based Link Prediction in the Supply Chain Network JO - International Journal of Computational Intelligence Systems SP - 1253 EP - 1264 VL - 13 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.200813.001 DO - 10.2991/ijcis.d.200813.001 ID - Lu2020 ER -