# Open Competency Optimization: A Human-Inspired Optimizer for the Dynamic Vehicle-Routing Problem
## 2 Dynamic VRP
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Researchers** & **Year** & **Title** \\ \hline [PERSON] et al. [12] & 2003 & A tabu search heuristic for the static \\  & & multi-vehicle dial-a-ride problem \\ \hline [PERSON] et al. [22] & 2014 & A hybrid granular tabu search algorithm for the \\  & & multi-depot vehicle routing problem \\ \hline [PERSON] et al. [11] & 2021 & An improved simulated annealing algorithm \\  & & with crossover operator for capacitated vehicle \\  & & routing problem \\ \hline [PERSON] et al. [14] & 2015 & Hybridized ant colony algorithm for the multi \\  & & compartment vehicle routing problem \\ \hline [PERSON] et al. [15] & 2016 & Novel ant colony optimization methods for \\  & & simplifying solution construction in vehicle \\  & & routing problems \\ \hline [PERSON] et al. [16] & 2014 & A hybrid genetic algorithm for the multi-depot \\  & & open vehicle routing problem \\ \hline [PERSON] et al. [17] & 2017 & Solving vehicle routing problem by using \\  & & improved genetic algorithm for optimal solution \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some recent research related to the Static VRP. [PERSON] et al. investigated the services of multiple populations in order to update their new Multi-Adaptive Particle Swarm Optimization method [32]. In recent years, a number of innovative metaheuristic strategies have been developed and applied to the DVRP, demonstrating substantial improvements in efficiency and solution quality. Among these, hybrid metaheuristics have gained considerable attention for their ability to combine the strengths of multiple algorithms. For example, recent studies have explored the integration of genetic algorithms (GAs) with elastic strategies, creating hybrid models that leverage the global search capabilities of GAs with the precision of local optimization methods [33]. This approach has shown promise in rapidly adapting to dynamic changes, offering superior performance in highly variable environments. Another promising development in the field is the application of reinforcement learning (RL) in conjunction with traditional metaheuristics. RL-based methods enable algorithms to learn and improve their decision-making processes over time, making them highly effective in dynamic scenarios where conditions are continuously changing. For instance, the recent work by [PERSON] et al. [34] demonstrated the integration of RL with genetic algorithms (GAs) to tackle the DVRP, yielding significant improvements in route optimization and computational efficiency. The RL component allows the algorithm to learn from past experiences, enhancing its ability to anticipate and react to future changes. The rise of swarm intelligence techniques has also contributed to advancements in solving the DVRP. Algorithms inspired by the collective behavior of social organisms, such as ant colony optimization (ACO) and bee colony optimization (BCO), have been refined and adapted for the DVRP. ACO, in particular, has been enhanced with mechanisms for better handling real-time data, allowing it to dynamically update routes as new information becomes available. The pheromone-based communication in ACO ensures that the system remains robust and responsive to changes, making it an ideal candidate for dynamic optimization problems [35]. The continued evolution of metaheuristic approaches for the DVRP is critical as logistics systems become more complex and data-driven. The integration of different metaheuristics, the incorporation of learning mechanisms, and the application of predictive analytics are all indicative of a broader trend towards more intelligent and adaptive optimization frameworks. As the landscape of logistics continues to evolve, these advanced metaheuristic techniques will play an increasingly important role in ensuring efficient and responsive transportation networks. Figure 2: The VRP with dynamically planned routes and customers.