The myopic policy and the rolling horizon policy offer different approaches to solving the MCRP, each with its own set of trade-offs in terms of solution quality and computational efficiency.
The myopic policy, by solving each stage as a separate subproblem without considering future stages, simplifies the optimization process and reduces the computational complexity of each subproblem. This can lead to faster computation times and easier implementation, especially for large-scale instances. However, the myopic policy may suffer from suboptimality, as decisions made in earlier stages are not reconsidered in light of future information.
On the other hand, the rolling horizon policy considers a lookahead of multiple stages, allowing for more informed decisions in the current stage based on future expectations. This can potentially lead to higher solution quality by taking into account the impact of current decisions on future stages. However, the rolling horizon policy may require more computational resources and time to solve due to the larger subproblem size and the need to iterate through multiple stages.
In summary, the myopic policy prioritizes computational efficiency and simplicity, while the rolling horizon policy focuses on solution quality and long-term optimization. The choice between the two policies would depend on the specific requirements of the MCRP instance, balancing the need for optimal solutions with computational resources available.