Efficient Framework for Resource Allocation in Vehicle Scheduling

摘要

This paper considers vehicle scheduling problem as a set partitioning problem with side constraints. In this paper we emphasis to set partitioning formulation which provides a much tighter lower bound. We propose a genetic algorithm focusing on optimization algorithm with time limitations of total vehicle travel time, tardiness, and fleet size are treated explicitly. All the vehicles visit all the nodes (cities) with penalty. However they must be come under allowable time given to them. The proposed algorithm implements a migration strategy by learning from the routes which are penalized earlier. The proposed algorithm is extensively evaluated on the various types of test problems, including a analysis with varied genetic parameters on the weighted sum of three objectives of a solution. Computational results illustrate the robustness of the proposed algorithm and shows its higher efficiency as compared to greedy approaches.