TY - GEN
T1 - Decision and Evaluation of Ordering and Transshipment Schemes Based on Multi-objective Programming
AU - Zhao, Qihan
AU - Zhuang, Wenwen
AU - Yu, Junfeng
AU - Zhu, Ao
AU - Wang, Jia
AU - Yan, Yanying
AU - Chen, Yinghao
AU - Hou, Muzhou
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The decision-making and evaluation of raw material ordering and transportation schemes are common in the actual production process. This paper makes an in-depth study on the formulation of ordering and transportation schemes in the future. Firstly, this paper determines the four quantitative indicators of supplier compliance rate, stability, market reputation and raw material output-input ratio with mean square deviation to weight them, establishes TOPSIS comprehensive analysis and evaluation model, and obtains the list of the top five importance to the production guarantee of the enterprise. Then, the time series Arima prediction model is used to process the historical data of the supply volume of the top 50 suppliers and predict the supply volume corresponding to the next 24 weeks. The specific ordering and transshipment scheme is obtained by using the prediction value, the single objective function model of "minimum number of suppliers", double objective function model of "minimum number of suppliers + minimum cost", and the three objective function model of "minimum number of suppliers + minimum cost + minimum overall loss rate".
AB - The decision-making and evaluation of raw material ordering and transportation schemes are common in the actual production process. This paper makes an in-depth study on the formulation of ordering and transportation schemes in the future. Firstly, this paper determines the four quantitative indicators of supplier compliance rate, stability, market reputation and raw material output-input ratio with mean square deviation to weight them, establishes TOPSIS comprehensive analysis and evaluation model, and obtains the list of the top five importance to the production guarantee of the enterprise. Then, the time series Arima prediction model is used to process the historical data of the supply volume of the top 50 suppliers and predict the supply volume corresponding to the next 24 weeks. The specific ordering and transshipment scheme is obtained by using the prediction value, the single objective function model of "minimum number of suppliers", double objective function model of "minimum number of suppliers + minimum cost", and the three objective function model of "minimum number of suppliers + minimum cost + minimum overall loss rate".
KW - 0-1 programming
KW - KS test
KW - TOPSIS model
KW - multi-objective function solution
KW - time series ARIMA model prediction
UR - http://www.scopus.com/inward/record.url?scp=85127001839&partnerID=8YFLogxK
U2 - 10.1109/ICDSBA53075.2021.00097
DO - 10.1109/ICDSBA53075.2021.00097
M3 - Conference Proceeding
AN - SCOPUS:85127001839
T3 - Proceedings - 2021 5th International Conference on Data Science and Business Analytics, ICDSBA 2021
SP - 474
EP - 478
BT - Proceedings - 2021 5th International Conference on Data Science and Business Analytics, ICDSBA 2021
A2 - Patnaik, Srikanta
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th Annual International Conference on Data Science and Business Analytics, ICDSBA 2021
Y2 - 24 September 2021 through 26 September 2021
ER -