Robust Dynamic Multi-Objective Vehicle Routing Optimization Method.

Area of Science:

• Operations Research
• Transportation Science
• Environmental Engineering

Background:

• Traditional vehicle routing problems often overlook fuel consumption and dynamic customer demands.
• Existing methods for dynamic routing can be computationally expensive due to frequent re-optimization.

Purpose of the Study:

• To develop a robust, two-phase method for dynamic multi-objective vehicle routing problems.
• To incorporate fuel consumption and carbon emissions as key optimization objectives.
• To reduce computational time by minimizing global re-optimization for dynamic customer arrivals.

Main Methods:

• A novel two-phase approach for dynamic multi-objective vehicle routing problems.
• Phase 1: Multi-objective particle swarm optimization to find robust virtual routes.
• Phase 2: Formation of static routes and dynamic customer integration with limited re-optimization.

Main Results:

• The proposed method yields routes with improved stability and robustness.
• Significant reduction in computational time by avoiding global re-optimization for dynamic customers.
• Carbon emissions and fuel consumption are effectively integrated as optimization objectives.

Conclusions:

• The robust two-phase method offers a more efficient and environmentally conscious solution for dynamic vehicle routing.
• The approach balances route optimality with computational efficiency in dynamic environments.
• This method provides a valuable alternative for logistics operations facing unpredictable demands and environmental concerns.