To handle dynamic or stochastic variants of the VRPODTW problem, the BRKGA-VM and VM+L approaches can be extended or adapted in several ways:
Dynamic Customer Requests: Implement a mechanism to dynamically update the customer requests during the optimization process. This could involve adjusting the chromosome representation and decoding process to accommodate changing customer demands.
Real-time Traffic Information: Integrate real-time traffic data into the optimization algorithm to dynamically adjust routes based on current traffic conditions. This could involve incorporating a feedback loop that continuously updates the routes based on the latest information.
Stochastic Time Windows: Modify the algorithm to handle stochastic time windows by introducing probabilistic constraints in the mathematical model. This would require incorporating uncertainty into the decision-making process and optimizing for robust solutions.
Adaptive Mutant Population: Develop an adaptive mechanism for the mutant population size based on the dynamic or stochastic nature of the problem. This could involve adjusting the mutant percentage based on the problem instance characteristics or the algorithm's performance.