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A stochastic network design problem for hazardous waste management.

Hao Yu¹, Xu Sun¹, Wei Deng Solvang¹

¹Department of Industrial Engineering, UiT the Arctic University of Norway, Lodve Langesgate 2, Narvik, 8514, Norway.

Journal of Cleaner Production

|August 25, 2020

Summary

This summary is machine-generated.

This study introduces a new stochastic model for hazardous waste management, optimizing facility location and transportation to minimize public risk and costs. The model provides robust decisions that account for uncertainties in cost, demand, and population exposure.

Keywords:

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

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Area of Science:

Environmental Engineering
Operations Research
Public Health

Background:

Hazardous waste management is critical for environmental protection and public safety.
Designing effective systems requires strategic decisions on facility location, size, and waste transportation.
Existing models often struggle to account for inherent uncertainties in planning.

Purpose of the Study:

To develop a stochastic bi-objective mixed integer linear program (MILP) for hazardous waste management.
To minimize population risk and optimize cost-efficiency in waste treatment and transportation.
To provide robust strategic decisions for network design under uncertainty.

Main Methods:

Formulated a novel stochastic bi-objective mixed integer linear program (MILP).
Incorporated stochastic parameters for cost, demand, and affected population.
Employed a sample average approximation based goal programming (SAA-GP) approach for solving the model.
Validated the model using numerical experiments and a real-world case study.

Main Results:

Uncertainty significantly impacts objective values and strategic decisions in hazardous waste management networks.
The stochastic model yields more robust decisions compared to deterministic approaches.
The model demonstrated applicability in a healthcare waste management case study in Wuhan, China.

Conclusions:

The proposed stochastic model effectively balances risk reduction and cost efficiency in hazardous waste management.
Accounting for uncertainty leads to more resilient and adaptable network designs.
The approach is valuable for real-world applications, including healthcare waste management planning.