Supplier Selection Based on Supplier Portrait and Markov Monte Carlo Method

doi:10.16182/j.issn1004731x.joss.21-FZ0664E

Journal of System Simulation ›› 2021, Vol. 33 ›› Issue (11): 2720-2732.doi: 10.16182/j.issn1004731x.joss.21-FZ0664E

Previous Articles Next Articles

Supplier Selection Based on Supplier Portrait and Markov Monte Carlo Method

Sun Bingli¹, Song Xiao², Gong Guanghong¹

1. School of Automation Science & Electrical Engineering, Beihang University, Beijing 100191, China;
2. School of Cyber Science & Technology, Beihang University, Beijing 100191, China

Received:2021-06-13 Revised:2021-07-13 Online:2021-11-18 Published:2021-11-17
Contact: Song Xiao (1976-), male, PhD, associate professor, research area: intelligent manufacturing and simulation. E-mail: songxiao@buaa.edu.cn
About author:Sun Bingli(1997-), male, master student, research area: intelligent manufacturing and simulation. E-mail: youthbl@163.com
Supported by:
Key R&D Projects (2020YFB1712203)

Abstract

Abstract: The supplier selection problem is a complex multi-objective decision-making problem and the key is how to establish the supplier's portrait. For the supplier selection of aerospace equipment, enterprise qualification management, business risks, and product quality are comprehensively considered. Based on Bayesian theory, the multi-parameter joint distribution derivation of portrait sample data is realized. Combined with the mathematical model derived, a Markov Monte Carlo simulation method is proposed. And combined with Gibbs sampler, the supplier ranking and selection are achieved when data is difficult to obtain or missing, which provides a new idea for supplier selection in the aerospace field.

Key words: supplier portrait, Markov Monte Carlo Method, gibbs sampling, bayesian theory, supplier selection

CLC Number:

TP391.9

Sun Bingli, Song Xiao, Gong Guanghong. Supplier Selection Based on Supplier Portrait and Markov Monte Carlo Method[J]. Journal of System Simulation, 2021, 33(11): 2720-2732.

References

[1] Chai J Y, Ngai E W T. Decision-making Techniques in Supplier Selection: Recent Accomplishments and What Lies Ahead[J]. Expert Systems with Applications (S0957-4174), 2020, 140(5): 112903.
[2] Tirkolaee E B, Mardani A, Dashtian Z, et al.A Novel Hybrid Method Using Fuzzy Decision Making and Multi-objective Programming for Sustainable-reliable Supplier Selection in Two-echelon Supply Chain Design[J]. Journal of Cleaner Production (S0959-6526), 2020, 250(3): 119517.
[3] Saaty T L.Decision Making with the Analytic Hierarchy Process[J]. International Journal Services Sciences (S1753-1454), 2008(1): 83-98.
[4] Abdollahi M, Arvan M, Razmi J.An Integrated Approach for Supplier Portfolio Selection: Lean or Agile?[J]. Expert Systems with Applications (S0957-4174), 2015, 42(1): 679-690.
[5] Wang J H, Yang J.Performance Evaluation Model and Algorithm of Sustainable Supply Chain based on Structural Entropy Weight Method and Improved TOPSIS Method[J]. China Market (S1005-6432), 2013, 20(26): 15-20.
[6] Purohit A K, Choudhary D, Shankar R.Inventory Lot-sizing with Supplier Selection Under Non-stationary Stochastic Demand[J]. International Journal of Production Research (S0020-7543), 2016, 54(8): 2459-2469.
[7] Adeinat H, Ventura J A.Integrated Pricing and Supplier Selection in a Two-stage Supply Chain[J]. International Journal of Production Economics (S0925-5273), 2018, 201(8): 193-202.
[8] Arampantzi C, Minis I, Dikas G.A Strategic Model for Exact Supply Chain Network Design and its Application to a Global Manufacturer[J]. International Journal of Production Research (S0020-7543), 2018, 57(5): 1-27.
[9] Tavana M, Fallahpour A, Caprio D D, et al.A Hybrid Intelligent Fuzzy Predictive Model with Simulation for Supplier Evaluation and Selection[J]. Expert Systems with Applications (S0957-4174), 2016, 61(1): 129-144.
[10] Hosseini S, Barker K.A Bayesian Network Model for Resilience-based Supplier Selection[J]. International Journal of Production Economics (S0925-5273), 2016, 180(10): 68-87.
[11] X Song, Kai Chen, X Li, et al. Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network, IEEE Transactions on Intelligent Transportation Systems (S1524-9050), 2021, 22(6): 3285-3302.
[12] Sarkis J, Dhavale D G.Supplier Selection for Sustainable Operations: A Triple-bottom-line Approach Using a Bayesian Framework[J]. International Journal of Production Economics (S0925-5273), 2015, 166(8): 177-191.
[13] Chakraborty S, Goyal N K, Mahapatra S, et al.A Monte-Carlo Markov Chain Approach for Coverage-area Reliability of Mobile Wireless Sensor Networks with Multistate Nodes[J]. Reliability Engineering & System Safety (S0951-8320), 2020, 193(1): 106662.
[14] Vihola M, Helske J, Franks J.Importance Sampling Type Estimators based on Approximate Marginal Markov Chain Monte Carlo[J]. Scandinavian Journal of Statistics (S0303-6898), 2020, 47(4): 1339-1376.
[15] Ni W, Lu L, Xiang Y, et al.Reliability Assessment of Integrated Energy System based on Markov Process Monte Carlo Method[J]. Electric Power Network Technology (S1000-3673), 2020, 44(1): 150-158.
[16] Gelfand A E.Gibbs Sampling[J]. Journal of the American Statistical Association (S0162-1459), 2000, 95(452): 1300-1304.

Supplier Selection Based on Supplier Portrait and Markov Monte Carlo Method

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Zhijie Li, Haoqi Shi, Changhua Li, Jie Zhang. Image Center Layout Optimization Method Based on Improved Genetic Algorithm [J]. Journal of System Simulation, 2022, 34(6): 1173-1184.
[2]	Bin Chen, Yue Liu, Yalei Yang. Airport Flight Transit Support Time Collaborative Planning Modeling Based on STN [J]. Journal of System Simulation, 2022, 34(6): 1196-1207.
[3]	Kai Yang, Chunyi Chen, Xiaojuan Hu, Haiyang Yu. Denoising Algorithm Based on Multi-feature Non-local Mean Filtering for Monte Carlo Rendered Images [J]. Journal of System Simulation, 2022, 34(6): 1259-1266.
[4]	Qi Chen, Haoyang Cui. Visual inspection model of UAV cluster based on improved pigeon flock hierarchy [J]. Journal of System Simulation, 2022, 34(6): 1275-1285.
[5]	Muqing Wang, Lei Zhang, Xiumin Fan, Xiaomeng Luo, Wenmin Zhu. Simulation Method of Virtual Human Pose Optimization Based On VR Peripherals [J]. Journal of System Simulation, 2022, 34(6): 1296-1303.
[6]	Cheng Lu, Xuesheng Jin. Design of Interactive Simulated Water Gun Fire Fighting Training System Based on Steam VR [J]. Journal of System Simulation, 2022, 34(6): 1312-1319.
[7]	Hongnai Gao, Lijiang Fu, Qian Xia, Ya Guo. Application of Observability in Performance Evaluation of Photosynthesis Model [J]. Journal of System Simulation, 2022, 34(6): 1330-1342.
[8]	Lingjia Ni, Xiaoxia Huang, Hongga Li, Zibo Zhang. Research on Fire Emergency Evacuation Simulation Based on Cooperative Deep Reinforcement Learning [J]. Journal of System Simulation, 2022, 34(6): 1353-1366.
[9]	Dun Meng, Zhuo Hu, Huajun Zhang. Simulation of Multi-layer Ship Evacuation System Based on Improved A* Algorithm [J]. Journal of System Simulation, 2022, 34(6): 1375-1382.
[10]	Yufei Guo, Kang Zhao, Yongqing Hai. Triangular Mesh Boolean Operation Method for Finite Element Analysis [J]. Journal of System Simulation, 2022, 34(5): 1003-1014.
[11]	Tong Wu, Qinghui Wang, Zhijia Xu. Study on the Scale Characteristics of Permeability of TPMS Porous Materials [J]. Journal of System Simulation, 2022, 34(5): 1015-1024.
[12]	Yangsheng Jiang, Sichen Wang, Kuan Gao, Meng Liu, Zhihong Yao. Cellular Automata Model of Mixed Traffic Flow Composed of Intelligent Connected Vehicles’ Platoon [J]. Journal of System Simulation, 2022, 34(5): 1025-1032.
[13]	Jiangtao Liang, Huiqin Wang. Study on Building Fire Evacuation Path Planning Based on Improved Ant Colony Algorithm [J]. Journal of System Simulation, 2022, 34(5): 1044-1053.
[14]	Qiwen Zhang, Bin Zhang. Teaching-Learning-Based Optimization Algorithm for Permutation Flowshop Scheduling [J]. Journal of System Simulation, 2022, 34(5): 1054-1063.
[15]	Genshang Xing, Fang Lu, Shushan Li, Dingti Luo. Supply Chain Delivery Model and Simulation Based on Product Experience [J]. Journal of System Simulation, 2022, 34(5): 1064-1075.