Spiking neural firefly optimization scheme for the capacitated dynamic vehicle routing problem with time windows

A number of technological improvements have prompted a great concern on 'dynamism' in vehicle routing problems (VRP). In real-world applications, the dynamic information happens simultaneously with the plan being carried out. In order to effectively solve dynamic VRP (DVRP), many optimization strategies have been introduced in the literature. A new variant of vehicle routing problem is proposed which combines DVRP with time windows and capacity constraints, named capacitated DVRP with time windows (CDVRPTW). Apart from the traditional way of handling the problem, this paper proposes a novel strategy that incorporates improved firefly algorithm (IFA) into the framework of spiking neural P (SN P) systems, named spiking neural firefly optimization (SFO). A mathematical model of the problem is formulated, and the solution scheme is designed by associating a number of SN P systems that work in parallel to find optimal solutions in a reasonable time. Additionally, the parameters in the IFA are optimized by adjusting the rule probabilities using SN P systems. Being a NP-hard problem with real-world applications, the benefits of this study are far-reaching. The proposed scheme has been tested on benchmark instances and proved novelty, feasibility, and potentiality of the system.

Automated summary

The paper introduces a new variant of vehicle routing problem that combines improved algorithm and neural systems, proposing spiking neural firefly optimization to solve dynamic VRP. By working in parallel across multiple neural systems, the proposed method has made significant progress in finding optimal solutions.