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Distributed mixed-integer fuzzy hierarchical programming for municipal solid waste management. Part II: scheme analysis and mechanism revelation.

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Abstract

As presented in the first companion paper, distributed mixed-integer fuzzy hierarchical programming (DMIFHP) was developed for municipal solid waste management (MSWM) under complexities of heterogeneities, hierarchy, discreteness, and interactions. Beijing was selected as a representative case. This paper focuses on presenting the obtained schemes and the revealed mechanisms of the Beijing MSWM system. The optimal MSWM schemes for Beijing under various solid waste treatment policies and their differences are deliberated. The impacts of facility expansion, hierarchy, and spatial heterogeneities and potential extensions of DMIFHP are also discussed. A few of findings are revealed from the results and a series of comparisons and analyses. For instance, DMIFHP is capable of robustly reflecting these complexities in MSWM systems, especially for Beijing. The optimal MSWM schemes are of fragmented patterns due to the dominant role of the proximity principle in allocating solid waste treatment resources, and they are closely related to regulated ratios of landfilling, incineration, and composting. Communities without significant differences among distances to different types of treatment facilities are more sensitive to these ratios than others. The complexities of hierarchy and heterogeneities pose significant impacts on MSWM practices. Spatial dislocation of MSW generation rates and facility capacities caused by unreasonable planning in the past may result in insufficient utilization of treatment capacities under substantial influences of transportation costs. The problems of unreasonable MSWM planning, e.g., severe imbalance among different technologies and complete vacancy of ten facilities, should be gained deliberation of the public and the municipal or local governments in Beijing. These findings are helpful for gaining insights into MSWM systems under these complexities, mitigating key challenges in the planning of these systems, improving the related management practices, and eliminating potential socio-economic and eco-environmental issues resulting from unreasonable management.

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References

  • Allesch, A., & Brunner, P. H. (2014). Assessment methods for solid waste management: a literature review. Waste Management & Research, 0734242X14535653.

  • Anghinolfi D, Paolucci M, Robba M, Taramasso AC (2013) A dynamic optimization model for solid waste recycling. Waste Manag 33(2):287–296

    Article  Google Scholar 

  • Antmann ED, Shi X, Celik N, Dai Y (2013) Continuous-discrete simulation-based decision making framework for solid waste management and recycling programs. Comput Ind Eng 65(3):438–454

    Article  Google Scholar 

  • Casado RR, Rivera JA, García EB, Cuadrado RE, Llorente MF, Sevillano RB, Delgado AP (2016) Classification and characterisation of SRF produced from different flows of processed MSW in the Navarra region and its co-combustion performance with olive tree pruning residues. Waste Manag 47:206–216

    Article  Google Scholar 

  • Chang NB, Pires A, Martinho G (2011) Empowering systems analysis for solid waste management: challenges, trends, and perspectives. Crit Rev Environ Sci Technol 41(16):1449–1530

    Article  CAS  Google Scholar 

  • Cheng GH, Huang GH, Li YP, Cao MF, Fan YR (2009) Planning of municipal solid waste management systems under dual uncertainties: a hybrid interval stochastic programming approach. Stoch Env Res Risk A 23(6):707–720

    Article  Google Scholar 

  • Cheng, G. H., Huang, G. H., & Dong, C. (2015a). Interval recourse linear programming for resources and environmental systems management under uncertainty. J Environ Inform (Int Soc Environ Inform Sci).

  • Cheng G, Huang G, Dong C (2015b) Convex contractive interval linear programming for resources and environmental systems management. Stoch Env Res Risk A:1–20

  • Cheng G, Huang GG, Dong C (2015c) Synchronic interval Gaussian mixed-integer programming for air quality management. Sci Total Environ 538:986–996

    Article  CAS  Google Scholar 

  • Dai C, Li YP, Huang GH (2011) A two-stage support-vector-regression optimization model for municipal solid waste management—a case study of Beijing, China. J Environ Manag 92(12):3023–3037

    Article  CAS  Google Scholar 

  • Datta A, Banerjee S, Finley AO, Gelfand AE (2016) Hierarchical nearest-neighbor Gaussian process models for large geostatistical datasets. J Am Stat Assoc 111(514):800–812

    Article  CAS  Google Scholar 

  • Dong C, Huang GH, Cai YP, Xu Y (2011) An interval-parameter minimax regret programming approach for power management systems planning under uncertainty. Appl Energy 88(8):2835–2845

    Article  Google Scholar 

  • Dong C, Huang GH, Cai YP, Liu Y (2012) An inexact optimization modeling approach for supporting energy systems planning and air pollution mitigation in Beijing city. Energy 37(1):673–688

    Article  Google Scholar 

  • Dong C, Huang GH, Cai YP, Liu Y (2013) Robust planning of energy management systems with environmental and constraint-conservative considerations under multiple uncertainties. Energy Convers Manag 65:471–486

    Article  Google Scholar 

  • Dong C, Tan Q, Huang GH, Cai YP (2014a) A dual-inexact fuzzy stochastic model for water resources management and non-point source pollution mitigation under multiple uncertainties. Hydrol Earth Syst Sci 18(5):1793–1803

    Article  CAS  Google Scholar 

  • Dong C, Huang G, Tan Q, Cai Y (2014b) Coupled planning of water resources and agricultural landuse based on an inexact-stochastic programming model. Frontiers of Earth Science 8(1):70–80

    Article  Google Scholar 

  • Dong C, Huang G, Cai Y, Li W, Cheng G (2014c) Fuzzy interval programming for energy and environmental systems management under constraint-violation and energy-substitution effects: a case study for the city of Beijing. Energy Econ 46:375–394

    Article  Google Scholar 

  • Dong C, Huang GH, Tan Q (2015) A robust optimization modelling approach for managing water and farmland use between anthropogenic modification and ecosystems protection under uncertainties. Ecol Eng 76:95–109

    Article  Google Scholar 

  • Eriksson O, Reich MC, Frostell B, Björklund A, Assefa G, Sundqvist JO, Granath J, Baky A, Thyselius L (2005) Municipal solid waste management from a systems perspective. J Clean Prod 13(3):241–252

    Article  Google Scholar 

  • Land AH, Doig AG (1960) An automatic method of solving discrete programming problems. Econometrica 28(3):497–520

    Article  Google Scholar 

  • Marshall RE, Farahbakhsh K (2013) Systems approaches to integrated solid waste management in developing countries. Waste Manag 33(4):988–1003

    Article  Google Scholar 

  • Pianosi F, Beven K, Freer J, Hall JW, Rougier J, Stephenson DB, Wagener T (2016) Sensitivity analysis of environmental models: a systematic review with practical workflow. Environ Model Softw 79:214–232

    Article  Google Scholar 

  • Tavares G, Zsigraiova Z, Semiao V et al (2009) Optimisation of MSW collection routes for minimum fuel consumption using 3D GIS modelling. Waste Manag 29(3):1176–1185

    Article  CAS  Google Scholar 

  • Xi BD, Su J, Huang GH, Qin XS, Jiang YH, Huo SL, Ji DF, Yao B (2010) An integrated optimization approach and multi-criteria decision analysis for supporting the waste-management system of the City of Beijing, China. Eng Appl Artif Intell 23(4):620–631

    Article  Google Scholar 

  • Yang, J., & He, Q. (2017). Scheduling parallel computations by work stealing: a survey. International Journal of Parallel Programming, 1–25.

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Acknowledgements

This research was supported by the Program for Innovative Research Team in University (IRT1127), the 111 Project (B14008) and the Natural Science and Engineering Research Council of Canada. We are very grateful for the editor and the peer reviewer who provided many constructive comments on how to improve our manuscript.

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Correspondence to Guohe Huang.

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Responsible editor: Philippe Garrigues

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Cheng, G., Huang, G., Dong, C. et al. Distributed mixed-integer fuzzy hierarchical programming for municipal solid waste management. Part II: scheme analysis and mechanism revelation.. Environ Sci Pollut Res 24, 8711–8721 (2017). https://doi.org/10.1007/s11356-017-8574-8

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  • DOI: https://doi.org/10.1007/s11356-017-8574-8

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