A robust fuzzy multi-objective location-routing problem for hazardous waste under uncertain conditions

Area of Science:

• Operations Research
• Environmental Management
• Computational Intelligence

Background:

• Industrialization and population growth exacerbate global waste management issues.
• Effective waste management and reduction are critical for national sustainability.
• Hazardous waste presents unique challenges requiring specialized solutions.

Purpose of the Study:

• To develop and solve a multi-objective location-routing problem for hazardous wastes.
• To compare the efficiency of various meta-heuristic algorithms for hazardous waste management.
• To incorporate novel objectives such as income generation from waste incineration and COVID-19 risk reduction.

Main Methods:

• A robust fuzzy optimization model was applied to handle parameter uncertainty.
• The problem was modeled as a multi-objective location-routing problem.
• Six meta-heuristic algorithms were employed: NSGA-II, MOPSO, MOIWO, PEA, MOEA/D, and MMOGWO.

Main Results:

• The Multi-Objective Invasive Weed Optimization (MOIWO) algorithm demonstrated superior performance and efficiency.
• MOIWO outperformed other algorithms in solving the hazardous waste location-routing problem.
• The study successfully integrated waste-to-energy income and COVID-19 risk mitigation into the model.

Conclusions:

• MOIWO is highly effective for complex hazardous waste management problems.
• The integration of economic and health objectives enhances waste management strategies.
• The study highlights the importance of robust optimization and meta-heuristic comparisons for environmental challenges.