Skip to main content

Advertisement

SpringerLink
Log in

Research on inventory control method based on demand response in power system fuzzy hybrid particle swarm optimization algorithm

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

The supply chain is a network system composed of interconnected members such as suppliers, manufacturers, wholesalers, retailers, and end customers. Inventory plays a major role in supply chain management, but it directly affects the cost and service quality of the entire supply chain system. Inventory control is not only closely related to a single enterprise itself but also closely related to all enterprises in the supply chain. Aiming at the problems of low standardization in the field of power material management, material procurement plans, and material demand plans are out of touch, and material inventory is too high. Fuzzy logic is used to model and manage demand patterns, lead times, and other factors in a sophisticated way. Meanwhile, particle swarm optimization is employed to find optimal or near-optimal solutions in complex spaces. Therefore, the novel fuzzy hybrid particle swarm optimization (FHPSO) algorithm is proposed to analyze the problems of power material inventory management, improve effectiveness of the inventory management, and cost reduction of logistic management. The demand characteristics of electric power materials are analyzed and extracted based on historical consumption data. The overall inventory control strategy of electric power materials is designed to build its dynamic inventory control strategy for various power materials. Realize the optimization and adjustment of inventory management, which can effectively enhance the efficiency of inventory management and reduce the logistics management cost of the enterprise. The experimental result proved that the FHPSO approach achieved superior performance by evaluating it with other existing approaches using various evaluation measures such as computational time, computational cost, error rate, and prediction accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Algorithm 1
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Availability of data and material

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Ma S, Zhang Y, Liu Y, Yang H, Lv J, Ren S (2020) Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries. J Clean Prod 274:123155

    Article  Google Scholar 

  2. Stavrakas V, Flamos A (2020) A modular high-resolution demand-side management model to quantify benefits of demand-flexibility in the residential sector. Energy Convers Manage 205:112339

    Article  Google Scholar 

  3. Okur Ö, Voulis N, Heijnen P, Lukszo Z (2019) Aggregator-mediated demand response: minimizing imbalances caused by uncertainty of solar generation. Appl Energy 247:426–437

    Article  Google Scholar 

  4. Tsay C, Kumar A, Flores-Cerrillo J, Baldea M (2019) Optimal demand response scheduling of an industrial air separation unit using data-driven dynamic models. Comput Chem Eng 126:22–34

    Article  Google Scholar 

  5. Vázquez-Canteli JR, Nagy Z (2019) Reinforcement learning for demand response: a review of algorithms and modeling techniques. Appl Energy 235:1072–1089

    Article  Google Scholar 

  6. Nazari L, Seifbarghy M, Setak M (2018) Modeling and analyzing pricing and inventory problem in a closed-loop supply chain with return policy and multiple manufacturers and multiple sales channels using game theory. ScientiaIranica 25(5):2759–2774

    Google Scholar 

  7. Raza SA (2018) Supply chain coordination under a revenue-sharing contract with corporate social responsibility and partial demand information. Int J Prod Econ 205:1–14

    Article  Google Scholar 

  8. Li Z, Hai J (2019) Inventory management for one warehouse multi-retailer systems with carbon emission costs. Comput Ind Eng 130:565–574

    Article  Google Scholar 

  9. Guo Jun (2018) Analysis of emergency material supply network of electric power enterprises based on reserve mode. University of Chinese Academy of Sciences

  10. Ahmad S, Ahmad A, Naeem M, Ejaz W, Kim HS (2018) A compendium of performance metrics, pricing schemes, optimization objectives, and solution methodologies of demand side management for the smart grid. Energies 11(10):2801

    Article  Google Scholar 

  11. Guo Z, Zhang R, Wang L, Zeng S, Li Y (2021) Optimal operation of regional integrated energy system considering demand response. Appl Therm Eng 191:116860

    Article  Google Scholar 

  12. Wang F, Lu X, Chang X, Cao X, Yan S, Li K, Duić N, Shafie-Khah M, Catalão JP (2022) Household profile identification for behavioral demand response: A semi-supervised learning approach using smart meter data. Energy 238:121728

    Article  Google Scholar 

  13. Ding Y, Bai Y, Tian Z, Wang Q, Su H (2023) Coordinated optimization of robustness and flexibility of building heating systems for demand response control considering prediction uncertainty. Appl Thermal Eng 120024

  14. Alamir N, Kamel S, Megahed TF, Hori M, Abdelkader SM (2023) Developing hybrid demand response technique for energy management in microgrid based on pelican optimization algorithm. Electric Power Systems Res 214:108905

    Article  Google Scholar 

  15. Wang M, Wang R, Liu J, Ju W, Zhou Q, Zhang G, Wang M (2022) Operation optimization for park with integrated energy system based on integrated demand response. Energy Rep 8:249–259

    Article  Google Scholar 

  16. Kirkerud JG, Nagel NO, Bolkesjø TF (2021) The role of demand response in the future renewable northern European energy system. Energy 235:121336

    Article  Google Scholar 

  17. Peinado-Guerrero MA, Villalobos JR (2022) Using inventory as energy storage for demand-side management of manufacturing operations. J Clean Prod 375:134213

    Article  Google Scholar 

  18. Morales-España G, Martínez-Gordón R, Sijm J (2022) Classifying and modelling demand response in power systems. Energy 242:122544

    Article  Google Scholar 

  19. Song Q, Zhao Q, Wang S, Liu Q, Chen X (2020) Dynamic path planning for unmanned vehicles based on fuzzy logic and improved ant colony optimization. IEEE Access 8:62107–62115

    Article  Google Scholar 

  20. Wang R, Hao K, Chen L, Wang T, Jiang C (2021) A novel hybrid particle swarm optimization using adaptive strategy. Inf Sci 579:231–250

    Article  MathSciNet  Google Scholar 

  21. Kaasgari MA, Imani DM, Mahmoodjanloo M (2017) Optimizing a vendor managed inventory (VMI) supply chain for perishable products by considering discount: Two calibrated meta-heuristic algorithms[J]. Comput Ind Eng 103:227–241

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. State Grid Electric Power Research Institute, Nanjing, China

    Huixuan Shi

  2. Wuhan Nari Limited Liability Company of State Grid Electric Power Research Institute, Wuhan, China

    Huixuan Shi

  3. State Grid Jiangsu Electric Power Co., Ltd., Nanjing, China

    Zhengping Gao & Li Fang

  4. State Grid Fujian Electric Power Co., Ltd., Fuzhou, China

    Jiqing Zhai

  5. State Grid Shandong Electric Power Co., Ltd., Jinan, China

    Hongzhi Sun

Authors
  1. Huixuan Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhengping Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiqing Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hongzhi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

HS, ZG, LF, JZ, and HS agreed on the content of the study. HS, ZG, LF, JZ, and HS collected all the data for analysis. HS, ZG, LF, JZ, and HS agreed on the methodology. HS, ZG, LF, JZ, and HS completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.

Corresponding author

Correspondence to Huixuan Shi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, H., Gao, Z., Fang, L. et al. Research on inventory control method based on demand response in power system fuzzy hybrid particle swarm optimization algorithm. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02303-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00202-024-02303-0

Keywords

Search

Navigation