Yuzhe Hu
ABSTRACT
As is well acknowledged to all, enterprise needs to make the correct choice of the ordering and transhipment scheme of the future raw materials to achieve the maximum income. However, it is always challenging to take into account the revenue and the cost of purchasing at the same time. These problems can be considered as single-objective planning, multi-objective planning and maximum-minimum-objective planning. A plan for the enterprise under the condition to minimize the transhipment loss rate of the forwarders is made. The genetic algorithm and machine learning are addressed to predict the optimal procurement and transhipment plan for the company in the next 24 weeks. In this paper, the genetic algorithm is selected to solve the goal programming problem. Machine learning models like XGBoost are selected to predict the future profit. Finally, sensitivity analysis is adopted to test the robustness of the model.
- Wrigley, E. A. “The Supply of Raw Materials in the Industrial Revolution.” The Economic History Review, vol. 15, no. 1, 1962, pp. 1–16.Google Scholar
Cross Ref
- Zhao Q, Zhuang W, Yu J, Decision and Evaluation of Ordering and Transshipment Schemes Based on Multi-objective Programming[C]//2021 5th Annual International Conference on Data Science and Business Analytics (ICDSBA). IEEE, 2021: 474-478.Google Scholar
- Abo-Elnaga Y, Nasr S. K-means cluster interactive algorithm-based evolutionary approach for solving bilevel multi-objective programming problems[J]. Alexandria Engineering Journal, 2022, 61(1): 811-827.Google ScholarCross Ref
- Matejaš J, Perić T, Mlinarić D. Which efficient solution in multi objective programming problem should be taken?[J]. Central European Journal of Operations Research, 2021, 29(3): 967-987.Google ScholarCross Ref
- Ren C, Xie Z, Zhang Y, An improved interval multi-objective programming model for irrigation water allocation by considering energy consumption under multiple uncertainties[J]. Journal of Hydrology, 2021, 602: 126699.Google ScholarCross Ref
- Li J, Hao J, Feng Q Q, Optimal selection of heterogeneous ensemble strategies of time series forecasting with multi-objective programming[J]. Expert Systems with Applications, 2021, 166: 114091.Google ScholarCross Ref
- Maiti I, Mandal T, Pramanik S, Solving multi-objective linear fractional programming problem based on Stanojevic's normalisation technique under fuzzy environment[J]. International Journal of Operational Research, 2021, 42(4): 543-564.Google ScholarCross Ref
- Katoch S, Chauhan S S, Kumar V. A review on genetic algorithm: past, present, and future[J]. Multimedia Tools and Applications, 2021, 80(5): 8091-8126.Google ScholarDigital Library
- Garud K S, Jayaraj S, Lee M Y. A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models[J]. International Journal of Energy Research, 2021, 45(1): 6-35.Google ScholarCross Ref
- Kim C, Batra R, Chen L, Polymer design using genetic algorithm and machine learning[J]. Computational Materials Science, 2021, 186: 110067.Google ScholarCross Ref
- Hamdia K M, Zhuang X, Rabczuk T. An efficient optimization approach for designing machine learning models based on genetic algorithm[J]. Neural Computing and Applications, 2021, 33(6): 1923-1933.Google ScholarDigital Library
- Chen T, Guestrin C. Xgboost: A scalable tree boosting system[C]//Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016: 785-794.Google Scholar
- Zhong Z, Yuan X, Liu S, Machine learning prediction models for prognosis of critically ill patients after open-heart surgery[J]. Scientific Reports, 2021, 11(1): 1-10.Google ScholarCross Ref
- Hoang A T, Nižetić S, Ong H C, A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels[J]. Sustainable Energy Technologies and Assessments, 2021, 47: 101416.Google ScholarCross Ref
- Yang Y, Du R, Tang H, SSLPNet: A financial econometric prediction model for small-sample long panel data[C]//2021 The 9th International Conference on Information Technology: IoT and Smart City. 2021: 174-180.Google Scholar
- Chen Y, Zheng W, Li W, Large group activity security risk assessment and risk early warning based on random forest algorithm[J]. Pattern Recognition Letters, 2021, 144: 1-5.Google ScholarDigital Library
- Khan M A, Memon S A, Farooq F, Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest[J]. Advances in Civil Engineering, 2021, 2021.Google Scholar
- Hosmer Jr D W, Lemeshow S, Sturdivant R X. Applied logistic regression[M]. John Wiley & Sons, 2013.Google ScholarCross Ref
- Caplice, C. and Sheffi, Y. (2003), OPTIMIZATION-BASED PROCUREMENT FOR TRANSPORTATION SERVICES. Journal of Business Logistics, 24: 109-128.Google ScholarCross Ref
Index Terms
- Goal Planning of Enterprise Raw Material Ordering and Transshipment Based on Genetic Algorithm and Machine Learning
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