A robust bi-objective multi-trip periodic capacitated arc routing problem for urban waste collection using a...

Area of Science:

• Operations Research
• Environmental Management
• Urban Planning

Background:

• Urban waste collection faces significant challenges due to high costs and operational complexity.
• Uncertainty in waste generation complicates efficient collection, transportation, and disposal processes.
• Effective decision-making for waste management requires addressing these uncertainties.

Purpose of the Study:

• To develop a novel robust bi-objective model for the periodic capacitated arc routing problem in urban waste collection.
• To minimize total operational costs and the longest vehicle tour distance (makespan) under demand uncertainty.
• To provide managers with a tool for improved decision-making in urban waste management.

Main Methods:

• Formulation of a robust bi-objective multi-trip periodic capacitated arc routing problem.
• Implementation of the ε-constraint method with CPLEX solver for model validation.
• Development and parameter tuning (Taguchi design) of a multi-objective invasive weed optimization algorithm for large-scale problems.

Main Results:

• The ε-constraint method effectively solves small-sized problems.
• The multi-objective invasive weed optimization algorithm generates high-quality solutions for large-scale urban waste collection scenarios.
• Sensitivity analysis provides insights into objective function behavior and optimal policy determination.

Conclusions:

• The proposed robust bi-objective model and optimization algorithms offer efficient solutions for urban waste collection under uncertainty.
• The study demonstrates the capability of advanced optimization techniques to enhance municipal waste management efficiency.
• Findings support better planning and resource allocation in urban waste collection systems.