no. 6
Construction of supply chain coordination and optimization model of fresh food e-commerce platform based on improved bacterial foraging algorithm
RAIRO. Operations Research, Tome 56 (2022) no. 6, pp. 3853-3869

In order to maximize the overall profit of the supply chain of fresh food e-commerce platform, the supply chain coordination and optimization model of fresh food e-commerce platform based on the improved bacterial foraging algorithm is constructed. The basic model of bacterial foraging algorithm is constructed through chemotaxis, reproduction, elimination & dispersal, and the bacterial foraging algorithm is improved by using four parts: bacterial individual and parameter initialization, chemotaxis behavior, reproduction behavior and migration behavior, so as to realize the coordination and optimization of the supply chain of fresh food e-commerce platform. Use the Internet service platform to promote the electronization of the supply chain transaction process and improve the overall operation efficiency. Through the cooperation among fresh food suppliers, fresh food e-commerce and upstream fresh food suppliers, the supply chain coordination and optimization model of fresh food e-commerce platform is constructed to improve the overall profit of the supply chain. The experimental results show that using the improved bacterial foraging algorithm to solve the supply chain coordination and optimization model of fresh food e-commerce platform has high effectiveness, and can maximize the overall profit of the supply chain of fresh food e-commerce platform.

Reçu le :
Accepté le :
Première publication :
Publié le :
DOI : 10.1051/ro/2022179
Classification : 00A05, 11A41
Keywords: Bacterial foraging algorithm, fresh food, e-commerce platform, supply chain, coordination 
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     title = {Construction of supply chain coordination and optimization model of fresh food e-commerce platform based on improved bacterial foraging algorithm},
     journal = {RAIRO. Operations Research},
     pages = {3853--3869},
     year = {2022},
     publisher = {EDP-Sciences},
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     number = {6},
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     url = {https://www.numdam.org/articles/10.1051/ro/2022179/}
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He, Juan. Construction of supply chain coordination and optimization model of fresh food e-commerce platform based on improved bacterial foraging algorithm. RAIRO. Operations Research, Tome 56 (2022) no. 6, pp. 3853-3869. doi: 10.1051/ro/2022179

[1] B. K. Debnath, P. Majumder and U. K. Bera, Two Ware-house Inventory models of breakable items with stock dependent demand under trade credit policy with respect to both supplier and retailer. Int. J. Logist. Syst. Manag. 31 (2018) 151–166.

[2] B. K. Debnath, P. Majumder, U. K. Bera and M. Maiti, Inventory model with demand as type-2 fuzzy number: a fuzzy differential equation approach. Iran. J. Fuzzy Syst. 15 (2018) 1–24. | MR | Zbl

[3] O. Durowoju, H. K. Chan and X. Wang, Investigation of the effect of e-platform information security breaches: a small and medium enterprise supply chain perspective. IEEE Trans. Eng. Manag. PP(99) (2020) 1–16.

[4] B. K. Debnath, P. Majumder and U. K. Bera, A fuzzy economic production quantity model of sustainable items with time and stock dependent demand under trade credit policy. Int. J. Oper. Res. 41 (2021) 27–52. | MR | DOI

[5] B. K. Debnath, P. Majumder and U. K. Bera, Multi-objective sustainable fuzzy economic production quantity (SFEPQ) model with demand as type-2 fuzzy number: a fuzzy differential equation approach. Hacet. J. Math. Stat. 48 (2021) 1–28. | MR | Zbl

[6] P. D. Giovanni, Closed-loop supply chain coordination through incentives with asymmetric information. Ann. Oper. Res. 253 (2018) 1–35. | MR | Zbl

[7] L. Hsiao, Y. J. Chen and H. Xiong, Supply chain coordination with product line design and a revenue sharing scheme. Nav. Res. Logist. (NRL) 66 (2019) 213–229. | MR | Zbl | DOI

[8] H. Li and C. L. E. Swartz, Dynamic real-time optimization of distributed MPC systems using rigorous closed-loop prediction. Comput. Chem. Eng. 122 ((MAR. 4) 2019) 356–371. | DOI

[9] P. Liu and S. P. Yi, Investment decision-making and coordination of a three-stage supply chain considering data company in the big data era. Ann. Oper. Res. 270 (2018) 1–17. | MR | Zbl

[10] H. Lalin, I. W. Mustika and N. A. Setiawan, Discrete bacterial foraging optimization for resource allocation in macrocell-femtocell networks. Etri J. 40 (2018) 726–735. | DOI

[11] Y. Liu, L. J. Zhang, Y. N. Han, L. Y. Pang and S. Wang, Financial credit risk evaluation model of supply chain finance based on particle swarm cooperative optimization algorithm. J. Jilin. Univ. (Sci. Edn.) 229 (2018) 119–125. | Zbl

[12] J. Lin, T. Li and J. Guo, Factors influencing consumers’ continuous purchase intention on fresh food e-commerce platforms: An organic foods-centric empirical investigation. Electron. Commer. Res. Appl. 50 (2021) 101103. | DOI

[13] S. Mahata and B. K. Debnath, A profit maximization single item inventory problem considering deterioration during carrying for price dependent demand and preservation technology investment. RAIRO-Oper. Res. 56 (2022) 1841–1856. | MR | Zbl | Numdam | DOI

[14] A. Majumder, D. Laha and P. N. Suganthan, Bacterial foraging optimization algorithm in robotic cells with sequence-dependent setup times. Knowl. Based Syst. 172 (MAY 15 2019) 104–122. | DOI

[15] M. Overbay, R. Sengupta, J. Aronoff et al., Using HP nanofinger SERS sensors to identify and monitor the health of bacteria through metabolite detection, in Applied Industrial Spectroscopy. Optical Society of America. AW1I3 (2020). | DOI

[16] E. A. Seward and K. Steven, Selection-driven cost-efficiency optimization of transcripts modulates gene evolutionary rate in bacteria. Genome Biol. 19 (2018) 102. | DOI

[17] M. Shibiao and C. Zhijun, Crowd evacuation model based on bacterial foraging algorithm. Int. J. Mod. Phys. C 29 (2018) 60–66. | DOI

[18] J. Scholz, M. A. De, A. S. Marques, T. M. Pinho, B. C. Jose, C. Rosset et al., Digital technologies for forest supply chain optimization: existing solutions and future trends. Environ. Manage. 62 (2018) 1108–1133. | DOI

[19] Y. Saif, M. Rizwan, A. A. Almansoori and A. Elkamel, Municipality solid waste supply chain optimization to power production under uncertainty. Comput. Chem. Eng. 121 (FEB 2 2019) 338–353. | DOI

[20] H. Su, E. Zio, J. Zhang, X. Li, L. Chi, L. Fan et al., A method for the multi-objective optimization of the operation of natural gas pipeline networks considering supply reliability and operation efficiency. Comput. Chem. Eng. 131 (Dec 5 2019) 106584.1–106584.10.

[21] A. Saha, P. Majumder, D. Dutta and B. K. Debnath, Multi-attribute decision making using q-rung orthopair fuzzy weighted fairly aggregation operators. J. Ambient. Intell. Humaniz. Comput. 12 (2021) 8149–8171. | DOI

[22] Z. P. Tong, Q. G. Xu and L. Ren, Simulation of supply chain equilibrium optimization management in multi-layer logistics storage facilities. Comput. Simul. 35 (2018) 361–364,453.

[23] Y. Wang, Bottleneck and countermeasures of cross-border e-commerce enterprises in Coastal Cities with application of big data technology. J. Coast. Res. 103 (2020) 705. | DOI

[24] Q. Wu, Y. Mu and Y. Feng, Coordinating contracts for fresh product outsourcing logistics channels with power structures. Int. J. Prod. Econ. 160 (2015) 94–105. | DOI

[25] Y. Wang, Z. Yu and L. Shen, Study on the decision-making and coordination of an e-commerce supply chain with manufacturer fairness concerns. Int. J. Prod. Res. 57 (2018) 1–21.

[26] G. S. Xu, Z. L. Song, S. O. Logistics and B. W. University, Coordinating contract between fresh agricultural products e-business enterprise and logistics service provider – an analysis based on fresh agricultural products home delivery mode. Comm. Res. (2017) 151–159.

[27] L. Xu, Y. Li, K. Govindan and X. Yue, Return policy and supply chain coordination with network-externality effect. Int. J. Prod. Res. 56 (2018) 3714–3732. | DOI

[28] X. Xiong, F. Yuan, M. Huang, M. Cao and X. Xiong, Comparative evaluation of web page and label presentation for imported seafood products sold on Chinese e-commerce platform and molecular identification using DNA barcoding. J Food Protect. 83 (2020) 256–265. | DOI

[29] X. Xu, M. Zhang, G. Dou and Y. Yu, Coordination of a supply chain with an online platform considering green technology in the blockchain eraPlease check and approve the volume number in the ref. [29]. Int. J. Prod. Res. 18 (2021) 1–18.

[30] C. Yang, B. Dan, Q. Wu and X. Zhang, Coordinating contract between retailer of fresh agricultural product and logistics service provider based on fresh-keeping effort. Technol. Econ. (2010) 124–128.

[31] S. Yin, L. Bai and R. Zhang, A supply chain-oriented perspective to prevent future COVID-19: Mathematical model and experience of guaranteeing quality and safety of fresh agricultural products. Res. Sq. 19 (2021) 1–26.

[32] M. Zheng, J. Lin, X. M. Yuan and E. Pan, Impact of an emergency order opportunity on supply chain coordination. Int. J. Prod. Res. 57 (2019) 3504–3521. | DOI

[33] J. Zhang, S. Zhao, T. C. E. Cheng and G. Hua, Optimisation of online retailer pricing and carrier capacity expansion during low-price promotions with coordination of a decentralised supply chain. Int. J. Prod. Res. 57 (2019) 2809–2827. | DOI

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