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A novel order evaluation model with nested probabilistic-numerical linguistic information applied to traditional order grabbing mode

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Abstract

With the popularization of information technology and the acceleration of the people’s pace of life, the takeout food industry is prevailing. The choice of order allocation mode plays an important role in order delivery efficiencies. This paper firstly reviews the whole process of the order grabbing mode and its internal logic, and then analyzes the qualitative and quantitative factors that influence the order distribution efficiency. Next, the order evaluation model is established based on the nested probabilistic-numerical linguistic information. After that, influencing factors of the order allocation modes are established, and the weights of the factors are determined by the AHP method. Finally, the order distribution results are obtained by traditional mode and the novel mode respectively. The comparative analysis and further analysis verify the validity and operability of the novel mode. By comparing the final values of multi-criteria functions between two modes, we conclude that the novel mode improves the allocation efficiency of order grabbing mode. In addition, the proposed mode significantly reduces the service distance and the standard deviation of service distance. The completion rate of delivery orders and the consistency of service level are also greatly improved. The takeout order allocation problem is optimized through the order evaluation model based on the nested probabilistic-numerical linguistic information. The proposed method has guiding effect on similar platforms.

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References

  1. Jiang YH et al (2019) Association between take-out food consumption and obesity among Chinese university students: a cross-sectional study. Int J Env Res Pub He 16. https://doi.org/10.3390/ijerph16061071

  2. Hammami R, Frein Y, Albana AS (2020) Delivery time quotation and pricing in two-stage supply chains: centralized decision-making with global and local managerial approaches. Eur J Oper Res 286:164–177. https://doi.org/10.1016/j.ejor.2020.03.006

    Article  MathSciNet  MATH  Google Scholar 

  3. Chen J, Du L, Guo YC (2021) Label constrained convolutional factor analysis for classification with limited training samples. Inf Sci 544:372–394. https://doi.org/10.1016/j.ins.2020.08.048

    Article  MathSciNet  Google Scholar 

  4. Xie J, Chen W, Zhang D, Zu S, Chen Y (2017) Application of principal component analysis in weighted stacking of seismic data. IEEE Geosci Remote S 14:1213–1217. https://doi.org/10.1109/lgrs.2017.2703611

    Article  Google Scholar 

  5. Ishizaka A, Labib A (2011) Review of the main developments in the analytic hierarchy process. Expert Syst Appl https://doi.org/10.1016/j.eswa.2011.04.143

  6. Gronmo R, Runde RK, Moller-Pedersen B (2013) Confluence of aspects for sequence diagrams. Softw Syst Model 12:789–824. https://doi.org/10.1007/s10270-011-0212-1

    Article  Google Scholar 

  7. Zhu YX, Tian DZ, Yan F (2020) Effectiveness of entropy weight method in decision-making. Math Probl Eng. https://doi.org/10.1155/2020/3564835

  8. Diakoulaki D, Mavrotas G, Papayannakis L (1995) Determining objective weights in multiple criteria problems - the critic method. Comput Oper Res 22:763–770. https://doi.org/10.1016/0305-0548(94)00059-H

    Article  MATH  Google Scholar 

  9. Chen GF, Xu C, Wang JY, Feng JW, Feng JQ (2019) Graph regularization weighted nonnegative matrix factorization for link prediction in weighted complex network. Neurocomputing 369:50–60. https://doi.org/10.1016/j.neucom.2019.08.068

    Article  Google Scholar 

  10. Wei GW (2010) GRA method for multiple attribute decision making with incomplete weight information in intuitionistic fuzzy setting. Knowl-Based Syst 23:243–247. https://doi.org/10.1016/j.knosys.2010.01.003

    Article  Google Scholar 

  11. Wang XX, Xu ZS, Gou XJ (2019) Nested probabilistic-numerical linguistic term sets in two-stage multi-attribute group decision making. Appl Intell 49:2582–2602. https://doi.org/10.1007/s10489-018-1392-y

    Article  Google Scholar 

  12. Yu DJ, Xu ZS, Wang WR (2019) A bibliometric analysis of fuzzy optimization and decision making (2002-2017). Fuzzy Optim Decis Ma 18:371–397. https://doi.org/10.1007/s10700-018-9301-8

    Article  Google Scholar 

  13. Liao HC, Xu ZS, Zeng XJ, Merigo JM (2015) Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets. Knowl-Based Syst 76:127–138. https://doi.org/10.1016/j.knosys.2014.12.009

    Article  Google Scholar 

  14. Liao HC, Xu Z, Herrera F, Merigo JM (2018) Editorial message: special issue on hesitant fuzzy linguistic decision making: algorithms, theory and applications. Int J Fuzzy Syst 20:2083–2083. https://doi.org/10.1007/s40815-018-0561-9

    Article  MathSciNet  Google Scholar 

  15. Wu P, Zhou LG, Chen HY, Tao ZF (2019) Additive consistency of hesitant fuzzy linguistic preference relation with a new expansion principle for hesitant fuzzy linguistic term sets. IEEE T Fuzzy Syst 27:716–730. https://doi.org/10.1109/Tfuzz.2018.2868492

    Article  Google Scholar 

  16. Farhadinia B, Xu ZS (2018) Novel hesitant fuzzy linguistic entropy and cross-entropy measures in multiple criteria decision making. Appl Intell 48:3915–3927. https://doi.org/10.1007/s10489-018-1186-2

    Article  Google Scholar 

  17. Lei F, Wei GW, Gao H, Wu J, Wei C (2020) Topsis method for developing supplier selection with probabilistic linguistic information. Int J Fuzzy Syst 22:749–759. https://doi.org/10.1007/s40815-019-00797-6

    Article  Google Scholar 

  18. Li Y, Zhang YX, Xu ZS (2020) A decision-making model under probabilistic linguistic circumstances with unknown criteria weights for online customer reviews. Int J Fuzzy Syst 22:777–789. https://doi.org/10.1007/s40815-020-00812-1

    Article  Google Scholar 

  19. Xu ZS, He Y, Wang XZ (2019) An overview of probabilistic-based expressions for qualitative decision-making: techniques, comparisons and developments. Int J Mach Learn Cybern 10:1513–1528. https://doi.org/10.1007/s13042-018-0830-9

    Article  Google Scholar 

  20. Wang XX, Xu ZS, Gou XJ, Xu M (2019) Distance and similarity measures for nested probabilistic-numerical linguistic term sets applied to evaluation of medical treatment. Int J Fuzzy Syst 21:1306–1329. https://doi.org/10.1007/s40815-019-00625-x

    Article  Google Scholar 

  21. He ZS, Chen YH, Shang ZH, Li CH, Li L, Xu ML (2019) A novel wind speed forecasting model based on moving window and multi-objective particle swarm optimization algorithm. Appl Math Model 76:717–740. https://doi.org/10.1016/j.apm.2019.07.001

    Article  MathSciNet  MATH  Google Scholar 

  22. Jalil SA, Javaid S, Muneeb SM (2018) A decentralized multi-level decision making model for solid transportation problem with uncertainty. Int J Syst Assur Eng 9:1022–1033. https://doi.org/10.1007/s13198-018-0720-2

    Article  Google Scholar 

  23. Wang XX, Xu ZS, Gou XJ, Trajkovic L (2020) Tracking a maneuvering target by multiple sensors using extended kalman filter with nested probabilistic-numerical linguistic information. IEEE T Fuzzy Syst 28:346–360. https://doi.org/10.1109/Tfuzz.2019.2906577

    Article  Google Scholar 

  24. Gallego-Schmid A, Mendoza JMF, Azapagic A (2019) Environmental impacts of takeaway food containers. J Clean Prod 211:417–427. https://doi.org/10.1016/j.jclepro.2018.11.220

    Article  Google Scholar 

  25. Renna P, Perrone G (2015) Order allocation in a multiple suppliers-manufacturers environment within a dynamic cluster. Int J Adv Manuf Technol 80:171–182. https://doi.org/10.1007/s00170-015-6999-0

    Article  Google Scholar 

  26. Thomas A, Krishnamoorthy M, Venkateswaran J, Singh G (2016) Decentralised decision-making in a multi-party supply chain. Int J Prod Res 54:405–425. https://doi.org/10.1080/00207543.2015.1096977

    Article  Google Scholar 

  27. Cebi F, Otay I (2016) A two-stage fuzzy approach for supplier evaluation and order allocation problem with quantity discounts and lead time. Inf Sci 339:143–157. https://doi.org/10.1016/j.ins.2015.12.032

    Article  Google Scholar 

  28. Liu WH, Liang ZC, Liu Y, Wang YJ, Wang Q (2015) A multi-period order allocation model of two-echelon logistics service supply chain based on inequity aversion theory. Int J Ship Trans Log 7:197–220. https://doi.org/10.1504/Ijstl.2015.067851

    Article  Google Scholar 

  29. Wang J, Miao HM, Yu MZ (2019) Interdependent order allocation in the two-echelon competitive and cooperative supply chain. Int J Prod Res 57:1190–1213. https://doi.org/10.1080/00207543.2018.1504171

    Article  Google Scholar 

  30. Marand AJ, Tang O, Li HY (2019) Quandary of service logistics: fast or reliable? Eur J Oper Res 275:983–996. https://doi.org/10.1016/j.ejor.2018.12.007

    Article  MathSciNet  MATH  Google Scholar 

  31. Vaidya OS, Kumar S (2006) Analytic hierarchy process: an overview of applications. Eur J Oper Res 169:1–29. https://doi.org/10.1016/j.ejor.2004.04.028

    Article  MathSciNet  MATH  Google Scholar 

  32. Podvezko V (2009) Application of AHP technique. J Bus Econ Manag 10:181–189. https://doi.org/10.3846/1611-1699.2009.10.181-189

    Article  Google Scholar 

  33. ZhiYan.org (2018) 2018–2024 China internet food delivery industry in-depth research and investment prospect forecast report. IOP Publishing IBaogao. http://www.ibaogao.com/baogao/10242544Z2018.html. Accessed 3 Nov 2019

  34. Hansson K, Ludwig T (2019) Crowd dynamics: conflicts, contradictions, and community in crowdsourcing. Comput Supp Coop W J 28:791–794. https://doi.org/10.1007/s10606-018-9343-z

    Article  Google Scholar 

  35. Wang Q, Ding GZ, Yu SQ (2019) Crowdsourcing mode-based learning activity flow approach to promote subject ontology generation and evolution in learning. Interact Learn Environ 27:965–983. https://doi.org/10.1080/10494820.2018.1509875

    Article  Google Scholar 

  36. Saab F, Elhajj IH, Kayssi A, Chehab A (2019) Modelling cognitive cias in crowdsourcing systems. Cogn Syst Res 58:1–18. https://doi.org/10.1016/j.cogsys.2019.04.004

    Article  Google Scholar 

  37. Xiong P, Zhu DY, Zhang LF, Ren W, Zhu TQ (2019) Optimizing rewards allocation for privacy-preserving spatial crowdsourcing. Comput Commun 146:85–94. https://doi.org/10.1016/j.comcom.2019.07.020

    Article  Google Scholar 

  38. Zheng HC, Li DH, Hou WH (2011) Task design, motivation, and participation in crowdsourcing contests. Int J Electron Commer 15:57–88. https://doi.org/10.2753/Jec1086-4415150402

    Article  Google Scholar 

  39. Lei XJ, Fang M, Fujita H (2019) Moth-flame optimization-based algorithm with synthetic dynamic PPI networks for discovering protein complexes. Knowl-Based Syst 172:76–85. https://doi.org/10.1016/j.knosys.2019.02.011

    Article  Google Scholar 

  40. Lei XJ, Ding YL, Fujita H, Zhang AD (2016) Identification of dynamic protein complexes based on fruit fly optimization algorithm. Knowl-Based Syst 105:270–277. https://doi.org/10.1016/j.knosys.2016.05.019

    Article  Google Scholar 

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (Nos. 71771155) and the Fundamental Research Funds for the Central Universities (Nos. YJ202063).

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Correspondence to Zeshui Xu.

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Ge, Z., Wang, X. & Xu, Z. A novel order evaluation model with nested probabilistic-numerical linguistic information applied to traditional order grabbing mode. Appl Intell 51, 4470–4489 (2021). https://doi.org/10.1007/s10489-020-02088-2

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