IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i19p7955-d419672.html
   My bibliography  Save this article

Multi-Objective Optimization for Order Assignment in Food Delivery Industry with Human Factor Considerations

Author

Listed:
  • Zhilan Lou

    (School of Data Sciences, Zhejiang University of Finance & Economics, Hangzhou 310018, Zhejiang, China)

  • Wanchen Jie

    (Department of Information Management and Artificial Intelligence, Zhejiang University of Finance & Economics, Hangzhou 310018, Zhejiang, China)

  • Shuzhu Zhang

    (Department of Information Management and Artificial Intelligence, Zhejiang University of Finance & Economics, Hangzhou 310018, Zhejiang, China)

Abstract

The order assignment in the food delivery industry is of high complexity due to the uneven distribution of order requirements and the large-scale optimization of workforce resources. The delivery performance of employees varies in different conditions, which further exacerbates the difficulty of order assignment optimization. In this research, a non-linear multi-objective optimization model is proposed with human factor considerations in terms of both deteriorating effect and learning effect, in order to acquire the optimal solutions in practice. The objectives comprised the minimization of the operational cost in multiple periods and the workload balancing among multiple employees. The proposed model is further transformed to a standardized mixed-integer linear model by the exploitation of linearization procedures and normalization operations. Numerical experiments show that the proposed model can be easily solved using commercial optimization softwares. The results indicate that the variance of employee performance can affect the entire delivery performance, and significant improvement of workload balancing can be achieved at the price of slight increase of the operational cost. The proposed model can facilitate the decision-making process of order assignment and workforce scheduling in the food delivery industry. Moreover, it can provide managerial insights for other labor-intensive service-oriented industries.

Suggested Citation

  • Zhilan Lou & Wanchen Jie & Shuzhu Zhang, 2020. "Multi-Objective Optimization for Order Assignment in Food Delivery Industry with Human Factor Considerations," Sustainability, MDPI, vol. 12(19), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:7955-:d:419672
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/19/7955/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/19/7955/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Theo Arentze & Tao Feng & Harry Timmermans & Jops Robroeks, 2012. "Context-dependent influence of road attributes and pricing policies on route choice behavior of truck drivers: results of a conjoint choice experiment," Transportation, Springer, vol. 39(6), pages 1173-1188, November.
    2. Oded Berman & Richard C. Larson & Edieal Pinker, 1997. "Scheduling Workforce and Workflow in a High Volume Factory," Management Science, INFORMS, vol. 43(2), pages 158-172, February.
    3. Elbert, R. & Franzke, T. & Glock, C. H. & Grosse, E. H., 2017. "The effects of human behavior on the efficiency of routing policies in order picking: the case of route deviations," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 84508, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. S. Srivatsa Srinivas & M. S. Gajanand, 2017. "Vehicle routing problem and driver behaviour: a review and framework for analysis," Transport Reviews, Taylor & Francis Journals, vol. 37(5), pages 590-611, September.
    5. Ernst, A. T. & Jiang, H. & Krishnamoorthy, M. & Sier, D., 2004. "Staff scheduling and rostering: A review of applications, methods and models," European Journal of Operational Research, Elsevier, vol. 153(1), pages 3-27, February.
    6. Xuran Gong & Qianwang Deng & Guiliang Gong & Wei Liu & Qinghua Ren, 2018. "A memetic algorithm for multi-objective flexible job-shop problem with worker flexibility," International Journal of Production Research, Taylor & Francis Journals, vol. 56(7), pages 2506-2522, April.
    7. Chung-Yee Lee & George L. Vairaktarakis, 1997. "Workforce Planning in Mixed Model Assembly Systems," Operations Research, INFORMS, vol. 45(4), pages 553-567, August.
    8. Van den Bergh, Jorne & Beliën, Jeroen & De Bruecker, Philippe & Demeulemeester, Erik & De Boeck, Liesje, 2013. "Personnel scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 226(3), pages 367-385.
    9. Valls, Vicente & Pérez, Ángeles & Quintanilla, Sacramento, 2009. "Skilled workforce scheduling in Service Centres," European Journal of Operational Research, Elsevier, vol. 193(3), pages 791-804, March.
    10. Vairaktarakis, George L. & Cai, Xiaoqiang & Lee, Chung-Yee, 2002. "Workforce planning in synchronous production systems," European Journal of Operational Research, Elsevier, vol. 136(3), pages 551-572, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sunarso Radhitya V.P. & Wibowo Budhi S., 2023. "The Impact of Consolidating On-Demand Food Delivery on Sustainability: A Simulation Study," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 14(1), pages 286-297, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. George Vairaktarakis & Joseph G. Szmerekovsky & Jiayan Xu, 2016. "Level workforce planning for multistage transfer lines," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(7), pages 577-590, October.
    2. Fang, Kan & Wang, Shijin & Pinedo, Michael L. & Chen, Lin & Chu, Feng, 2021. "A combinatorial Benders decomposition algorithm for parallel machine scheduling with working-time restrictions," European Journal of Operational Research, Elsevier, vol. 291(1), pages 128-146.
    3. Volland, Jonas & Fügener, Andreas & Brunner, Jens O., 2017. "A column generation approach for the integrated shift and task scheduling problem of logistics assistants in hospitals," European Journal of Operational Research, Elsevier, vol. 260(1), pages 316-334.
    4. De Bruecker, Philippe & Van den Bergh, Jorne & Beliën, Jeroen & Demeulemeester, Erik, 2015. "Workforce planning incorporating skills: State of the art," European Journal of Operational Research, Elsevier, vol. 243(1), pages 1-16.
    5. Dolgui, Alexandre & Kovalev, Sergey & Kovalyov, Mikhail Y. & Malyutin, Sergey & Soukhal, Ameur, 2018. "Optimal workforce assignment to operations of a paced assembly line," European Journal of Operational Research, Elsevier, vol. 264(1), pages 200-211.
    6. Jaime Miranda & Pablo A. Rey & Antoine Sauré & Richard Weber, 2018. "Metro Uses a Simulation-Optimization Approach to Improve Fare-Collection Shift Scheduling," Interfaces, INFORMS, vol. 48(6), pages 529-542, November.
    7. Dalia Attia & Reinhard Bürgy & Guy Desaulniers & François Soumis, 2019. "A decomposition-based heuristic for large employee scheduling problems with inter-department transfers," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 7(4), pages 325-357, December.
    8. David Rea & Craig Froehle & Suzanne Masterson & Brian Stettler & Gregory Fermann & Arthur Pancioli, 2021. "Unequal but Fair: Incorporating Distributive Justice in Operational Allocation Models," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2304-2320, July.
    9. Ellen Bockstal & Broos Maenhout, 2019. "A study on the impact of prioritising emergency department arrivals on the patient waiting time," Health Care Management Science, Springer, vol. 22(4), pages 589-614, December.
    10. Arpan Rijal & Marco Bijvank & Asvin Goel & René de Koster, 2021. "Workforce Scheduling with Order-Picking Assignments in Distribution Facilities," Transportation Science, INFORMS, vol. 55(3), pages 725-746, May.
    11. Emir Demirović & Nysret Musliu & Felix Winter, 2019. "Modeling and solving staff scheduling with partial weighted maxSAT," Annals of Operations Research, Springer, vol. 275(1), pages 79-99, April.
    12. Melanie Erhard, 2021. "Flexible staffing of physicians with column generation," Flexible Services and Manufacturing Journal, Springer, vol. 33(1), pages 212-252, March.
    13. X Zhang & A Chakravarthy & Q Gu, 2009. "Equipment scheduling problem under disruptions in mail processing and distribution centres," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(5), pages 598-610, May.
    14. Ferdinand Kiermaier & Markus Frey & Jonathan F. Bard, 2020. "The flexible break assignment problem for large tour scheduling problems with an application to airport ground handlers," Journal of Scheduling, Springer, vol. 23(2), pages 177-209, April.
    15. Wallace J. Hopp & Eylem Tekin & Mark P. Van Oyen, 2004. "Benefits of Skill Chaining in Serial Production Lines with Cross-Trained Workers," Management Science, INFORMS, vol. 50(1), pages 83-98, January.
    16. Young-Chae Hong & Amy Cohn & Stephen Gorga & Edmond O’Brien & William Pozehl & Jennifer Zank, 2019. "Using Optimization Techniques and Multidisciplinary Collaboration to Solve a Challenging Real-World Residency Scheduling Problem," Interfaces, INFORMS, vol. 49(3), pages 201-212, May.
    17. Sayin, Serpil & Karabati, Selcuk, 2007. "Assigning cross-trained workers to departments: A two-stage optimization model to maximize utility and skill improvement," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1643-1658, February.
    18. Jonas Ingels & Broos Maenhout, 2018. "The impact of overtime as a time-based proactive scheduling and reactive allocation strategy on the robustness of a personnel shift roster," Journal of Scheduling, Springer, vol. 21(2), pages 143-165, April.
    19. Lotfi Hidri & Achraf Gazdar & Mohammed M. Mabkhot, 2020. "Optimized Procedure to Schedule Physicians in an Intensive Care Unit: A Case Study," Mathematics, MDPI, vol. 8(11), pages 1-24, November.
    20. Damcı-Kurt, Pelin & Zhang, Minjiao & Marentay, Brian & Govind, Nirmal, 2019. "Improving physician schedules by leveraging equalization: Cases from hospitals in U.S," Omega, Elsevier, vol. 85(C), pages 182-193.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:7955-:d:419672. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.