A stochastic network design problem for hazardous waste management

Hao Yu, Xu Sun, Wei Deng Solvang, Gilbert Laporte, Carman Ka Man Lee

Research output: Contribution to journalArticlepeer-review

45 Citations (SciVal)


Hazardous waste management is of paramount importance due to the potential threats posed to the environment and local residents. The design of a hazardous waste management system involves several important decisions, i.e., the determination of the locations and sizes of treatment, recycling and disposal facilities, and organizing the transportation of hazardous waste among different facilities. In this paper, we proposed a novel stochastic bi-objective mixed integer linear program (MILP) to support these decisions in order to reduce the population exposure to risk while simultaneously maintaining a high cost efficiency of the transportation and treatment of hazardous waste. Moreover, considering the inherent uncertainty within the planning horizon, the cost, demand and affected population are defined as stochastic parameters. A sample average approximation based goal programming (SAA-GP) approach is used to solve the mathematical model. The proposed model and solution method are validated through numerical experiments whose results show that uncertainty may not only affect the objective value but also lead to different strategic decisions in the network design of a hazardous waste management system. In this regard, the strategic decisions obtained by the stochastic model is more robust to the change of external environment. Finally, the model is applied in a real-world case study of healthcare waste management in Wuhan, China, in order to show its applicability.

Original languageEnglish
Article number123566
JournalJournal of Cleaner Production
Early online date5 Aug 2020
Publication statusPublished - 20 Dec 2020


  • Hazardous materials
  • Hazardous waste
  • Location problem
  • Multi-objective optimization
  • Network design
  • Stochastic optimization
  • Sustainability

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Environmental Science(all)
  • Strategy and Management
  • Industrial and Manufacturing Engineering


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