Multi-objective Optimization For The Dynamic Multi-Pickup and Delivery Problem with Time Windows

TL;DR

This paper introduces a genetic algorithm for solving the dynamic multi-pickup and delivery problem with time windows, optimizing for cost and tardiness in vehicle routing under dynamic conditions.

Contribution

It presents a novel multi-objective genetic algorithm using Pareto dominance for the dynamic m-PDPTW, balancing cost and tardiness effectively.

Findings

Algorithm achieves zero total tardiness in tests

Provides good trade-offs between cost and tardiness

Effective for dynamic vehicle routing problems

Abstract

The PDPTW is an optimization vehicles routing problem which must meet requests for transport between suppliers and customers satisfying precedence, capacity and time constraints. We present, in this paper, a genetic algorithm for multi-objective optimization of a dynamic multi pickup and delivery problem with time windows (Dynamic m-PDPTW). We propose a brief literature review of the PDPTW, present our approach based on Pareto dominance method and lower bounds, to give a satisfying solution to the Dynamic m-PDPTW minimizing the compromise between total travel cost and total tardiness time. Computational results indicate that the proposed algorithm gives good results with a total tardiness equal to zero with a tolerable cost.