Skip to main content

Advertisement

SpringerLink
Log in

A multi-objective and multi-period optimization model for urban healthcare waste’s reverse logistics network design

  • Published:
Journal of Combinatorial Optimization Aims and scope Submit manuscript

Abstract

Various types of healthcare waste (or medical waste) generated by urban healthcare activities have increased due to the expansion of urban population and medical needs. As healthcare wastes are harmful to both the environment and human health, managing medical waste is becoming progressively more important. Constructing an optimized medical waste recycling network is one of the key problems in the management of urban healthcare waste. This paper conducts a two-stage reverse logistics network design for urban healthcare waste. The first stage involves the prediction of the amount of medical waste. Based on the Grey GM(1,1) prediction model, the amount of medical waste in multi-period of the target hospitals is predicted. In the second stage, a multi-objective model aimed at minimizing operating costs and minimizing environmental impact is developed for facilities allocation decisions, which include the configuration of key facilities such as hospitals, collection centers, transshipment centers, processing centers, and disposal sites, as well as medical waste flow control among facilities. A dynamic approach for the healthcare waste reverse logistics network is constructed by combining the Grey GM(1,1) prediction method with multi-objective optimization model. Sensitivity analysis of key parameters has been performed to analyze their impact on network performance. Some insightful management practices have been revealed.

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.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Adamovic VM, Antanasijevic DZ, Ristic MD, Peric-Grujic AA, Pocajt VV (2018) An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level. J Mater Cycles Waste Manag 20(3):1736–1750

    Google Scholar 

  • Alagöz BAZ, Kocasoy G (2007) Treatment and disposal alternatives for health-care waste in developing countries—a case study in Istanbul, Turkey. Waste Manag Res 25(1):83–89

    Google Scholar 

  • Bai YQ, Han X, Chen T, Yu H (2015) Quadratic kernel-free least squares support vector machine for target diseases classification. J Comb Optim 30(4):850–870

    MathSciNet  MATH  Google Scholar 

  • Birpinar ME, Bilgili MS, Erdogan T (2009) Medical waste management in Turkey: a case study of istanbul. Waste Manag (Oxf) 29(1):445–448

    Google Scholar 

  • Cai S, Yang K, Liu K (2018) Multi-objective optimization of the distributed permutation flow shop scheduling problem with transportation and eligibility constraints. J Oper Res Soc China 6:391–416

    MathSciNet  MATH  Google Scholar 

  • Chang J, Zhang L (2019) Case mix index weighted multi-objective optimization of inpatient bed allocation in general hospital. J Comb Optim 37(1):1–19

    MathSciNet  MATH  Google Scholar 

  • Chen Y, Yang J, Xu Y, Jiang S, Liu X, Wang Q (2016) Status self-validation of sensor arrays using gray forecasting model and bootstrap method. IEEE Trans Instrum Meas 65(7):1626–1640

    Google Scholar 

  • Chen YY, Liu HT, Hsieh HL (2019) Time series interval forecast using GM (1, 1) and NGBM (1, 1) models. Soft Comput 23(5):1541–1555

    MATH  Google Scholar 

  • Cruz-Rivera R, Ertel J (2009) Reverse logistics network design for the collection of end-of-life vehicles in Mexico. Eur J Oper Res 196(3):930–939

    MATH  Google Scholar 

  • Cui J, Sf Liu, Zeng B, Nm Xie (2013) A novel grey forecasting model and its optimization. Appl Math Model 37(6):4399–4406

    MathSciNet  Google Scholar 

  • Dowlatshahi S (2000) Developing a theory of reverse logistics. Interfaces 30(3):143–155

    Google Scholar 

  • Drugan MM (2019) Estimating the number of basins of attraction of multi-objective combinatorial problems. J Comb Optim 37(4):1367–1407

    MathSciNet  MATH  Google Scholar 

  • Ene S, Öztürk N (2017) Grey modelling based forecasting system for return flow of end-of-life vehicles. Technol Forecast Soc Change 115:155–166

    Google Scholar 

  • Fan J, Lu XW (2015) Supply chain scheduling problem in the hospital with periodic working time on a single machine. J Comb Optim 30(4):892–905

    MathSciNet  MATH  Google Scholar 

  • Fleischmann M, Bloemhof-Ruwaard JM, Dekker R, Van der Laan E, Van Nunen JA, Van Wassenhove LN (1997) Quantitative models for reverse logistics: a review. Eur J Oper Res 103(1):1–17

    MATH  Google Scholar 

  • Floudas CA (1995) Nonlinear and mixed-integer optimization: fundamentals and applications. Oxford University Press, Oxford

    MATH  Google Scholar 

  • Ghaderi A, Jabalameli MS (2013) Modeling the budget-constrained dynamic uncapacitated facility location-network design problem and solving it via two efficient heuristics: a case study of health care. Math Comput Model 57(3–4):382–400

    MATH  Google Scholar 

  • Giannouli M, de Haan P, Keller M, Samaras Z (2007) Waste from road transport: development of a model to predict waste from end-of-life and operation phases of road vehicles in Europe. J Clean Prod 15(11–12):1169–1182

    Google Scholar 

  • Govindan K, Soleimani H, Kannan D (2015) Reverse logistics and closed-loop supply chain: a comprehensive review to explore the future. Eur J Oper Res 240(3):603–626

    MathSciNet  MATH  Google Scholar 

  • Hao H, Wang Z, Lin H, Zhang Q, Huang M, Zhu J (2017) Fifth profit source: commercial value and mode of reverse logistics in China. Logist Technol 36(8):47–50

    Google Scholar 

  • Hao H, Zhang Q, Wang Z, Zhang J (2018) Forecasting the number of end-of-life vehicles using a hybrid model based on grey model and artificial neural network. J Clean Prod 202:684–696

    Google Scholar 

  • Hasani A, Zegordi SH, Nikbakhsh E (2015) Robust closed-loop global supply chain network design under uncertainty: the case of the medical device industry. Int J Prod Res 53(5):1596–1624

    Google Scholar 

  • He YL, Wang PJ, Zhang MQ, Zhu QX, Xu Y (2018) A novel and effective nonlinear interpolation virtual sample generation method for enhancing energy prediction and analysis on small data problem: a case study of ethylene industry. Energy 147:418–427

    Google Scholar 

  • He C, Wu Y, Chen T (2019) Prognostics and health management of life-supporting medical instruments. J Comb Optim 37(1):183–195

    MathSciNet  MATH  Google Scholar 

  • Hu B, Pan F, Wang L (2019) A scheduling algorithm for medical emergency rescue aircraft trajectory based on hybrid estimation and intent inference. J Comb Optim 37(1):40–61

    MathSciNet  MATH  Google Scholar 

  • Jang YC, Lee C, Yoon OS, Kim H (2006) Medical waste management in Korea. J Environ Manag 80(2):107–115

    Google Scholar 

  • Jin H, Song BD, Yih Y, Sutherland JW (2019) A bi-objective network design for value recovery of neodymium-iron-boron magnets: a case study of the united states. J Clean Prod 211:257–269

    Google Scholar 

  • Julong D (1989) Introduction to grey system theory. J Grey Syst 1(1):1–24

    MathSciNet  MATH  Google Scholar 

  • Klangsin P, Harding AK (1998) Medical waste treatment and disposal methods used by hospitals in Oregon, Washington, and Idaho. J Air Waste Manag Assoc 48(6):516–526

    Google Scholar 

  • Lee BK, Ellenbecker MJ, Moure-Ersaso R (2004) Alternatives for treatment and disposal cost reduction of regulated medical wastes. Waste Manag (Oxf) 24(2):143–151

    Google Scholar 

  • Liao TY (2018) Reverse logistics network design for product recovery and remanufacturing. Appl Math Model 60:145–163

    MathSciNet  MATH  Google Scholar 

  • Liu H, Yao Z (2018) Research on mixed and classification simulation models of medical waste—a case study in Beijing, China. Sustainability 10(11):16

    Google Scholar 

  • Liu HC, Wu J, Li P (2013) Assessment of health-care waste disposal methods using a VIKOR-based fuzzy multi-criteria decision making method. Waste Manag (Oxf) 33(12):2744–2751

    Google Scholar 

  • Lv J, Xie J (2015) The prediction of returns in weee reverse logistics based on the spatial correlation. J Ind Eng Eng Manag 29(4):152–161

    Google Scholar 

  • Makajic-Nikolic D, Petrovic N, Belic A, Rokvic M, Radakovic JA, Tubic V (2016) The fault tree analysis of infectious medical waste management. J Clean Prod 113:365–373

    Google Scholar 

  • Marler RT, Arora JS (2010) The weighted sum method for multi-objective optimization: new insights. Struct Multidiscip Optim 41(6):853–862

    MathSciNet  MATH  Google Scholar 

  • Naiming X, Ruizhi W (2017) A historic review of grey forecasting models. J Grey Syst 29(4):1–29

    Google Scholar 

  • Niu W, Cheng J, Wang G (2013) Applications of extension grey prediction model for power system forecasting. J Comb Optim 26(3):555–567

    MathSciNet  MATH  Google Scholar 

  • Oliveira MD, Bevan G (2006) Modelling the redistribution of hospital supply to achieve equity taking account of patient’s behaviour. Health Care Manag Sci 9(1):19–30

    Google Scholar 

  • Özkan A (2013) Evaluation of healthcare waste treatment/disposal alternatives by using multi-criteria decision-making techniques. Waste Manag Res 31(2):141–149

    Google Scholar 

  • Pishvaee MS, Farahani RZ, Dullaert W (2010) A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Comput Oper Res 37(6):1100–1112

    MATH  Google Scholar 

  • Quariguasi Frota Neto J, Walther G, Bloemhof J, Van Nunen J, Spengler T (2010) From closed-loop to sustainable supply chains: the WEEE case. Int J Prod Res 48(15):4463–4481

    MATH  Google Scholar 

  • Roelen C, Thorsen S, Heymans M, Twisk J, Bültmann U, Bjørner J (2018) Development and validation of a prediction model for long-term sickness absence based on occupational health survey variables. Disabil Rehabil 40(2):168–175

    Google Scholar 

  • Si-feng L (2003) Emergence and development of grey system theory and its forward trends. J Zhejiang Wanli Univ 16(4):266–272

    Google Scholar 

  • Srivastava SK (2008) Network design for reverse logistics. Omega 36(4):535–548

    Google Scholar 

  • Tawarmalani M, Sahinidis NV (2004) Global optimization of mixed-integer nonlinear programs: a theoretical and computational study. Math Program 99(3):563–591

    MathSciNet  MATH  Google Scholar 

  • Tesfahun E, Kumie A, Beyene A (2016) Developing models for the prediction of hospital healthcare waste generation rate. Waste Manag Res 34(1):75–80

    Google Scholar 

  • Thakur V, Anbanandam R (2017) Management practices and modeling the seasonal variation in health care waste a case study of Uttarakhand, India. J Model Manag 12(1):162–174

    Google Scholar 

  • Tian G, Zhou M, Chu J, Wang B (2013) Prediction models of the number of end-of-life vehicles in China-annotated. In: International conference on advanced mechatronic systems, ICAMechS, p 5

  • Urioste A, Zajac MAL, Aquino S, Ribeiro AP (2018) Reverse logistics of surgical explant in a philanthropic hospital: implementation of a new ecoefficient model of management hospital waste. Revista De Gestao Em Sistemas De Saude-Rgss 7(3):257–273

    Google Scholar 

  • Wang B, Han XB, Zhang XX, Zhang SH (2015) Predictive–reactive scheduling for single surgical suite subject to random emergency surgery. J Comb Optim 30(4):949–966

    MathSciNet  MATH  Google Scholar 

  • Wang Z, Hao H, Gao F, Zhang Q, Zhang J, Zhou Y (2019) Multi-attribute decision making on reverse logistics based on DEA-TOPSIS: a study of the Shanghai end-of-life vehicles industry. J Clean Prod 214:730–737

    Google Scholar 

  • Xiong G, Wang Y (2014) Best routes selection in multimodal networks using multi-objective genetic algorithm. J Comb Optim 28(3):655–673

    MathSciNet  MATH  Google Scholar 

  • Yanik S (2015) Reverse logistics network design under the risk of hazardous materials transportation. Hum Ecol Risk Assess 21(5):1277–1298

    Google Scholar 

  • Yubo S (2014) Xi’an medical waste recycling network planning based on the theory of reverse logistics. Thesis

  • Zhao M, Zhao D, Jiang Z, Cui D, Li J, Shi X (2015) The gray prediction GM (1, 1) model in traffic forecast application. Math Model Eng Probl 2(1):17–22

    Google Scholar 

  • Zhen L, Huang L, Wang W (2019) Green and sustainable closed-loop supply chain network design under uncertainty. J Clean Prod 227:1195–1209

    Google Scholar 

  • Zhou W, He JM (2013) Generalized GM (1, 1) model and its application in forecasting of fuel production. Appl Math Model 37(9):6234–6243

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This research was supported by Research Center of Resource Recycling Science and Engineering, Shanghai Polytechnic University and Gaoyuan Discipline of Shanghai—Environmental Science and Engineering (Resource Recycling Science and Engineering) (A30DB182602); Shanghai Polytechnic University Management Science and Engineering Discipline Construction Fund (XXKPY1606); Key Project of Shanghai Soft Science Research Program (19692107700).

Author information

Authors and Affiliations

  1. School of Management, Shanghai University, Shanghai, 200444, People’s Republic of China

    Zhiguo Wang & Lufei Huang

  2. Department of Management, University of Otago, Dunedin, 9054, New Zealand

    Cici Xiao He

Authors
  1. Zhiguo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lufei Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cici Xiao He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lufei Huang.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Huang, L. & He, C.X. A multi-objective and multi-period optimization model for urban healthcare waste’s reverse logistics network design. J Comb Optim 42, 785–812 (2021). https://doi.org/10.1007/s10878-019-00499-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10878-019-00499-7

Keywords

Search

Navigation