Approximate Collaborative Fleet Routing with a Pointer Generation Neural Network Approach

With rapid emergence of high-performance computing platforms, the availability of client big data and new machine learning techniques, the application domain of platform-based mobility services supports the research of new optimization techniques for discrete combinatorial optimization problems. Within this research field, particularly large-scale transportation domain specific problems, e.g. multi-vehicle-request matching and collaborative vehicle fleet routing problems are of high interest. In this contribution we present our novel combinatorial Deep Reinforcement Learning for solving symmetric and asymmetric multi-vehicle-request weighted assignment or matching problems by learning and predicting an efficient solution heuristic automatically. The solved assignment problem is characterized by defining different node classes for vehicles, requests and service stations. Our results contain algorithm benchmarks based on reproducible artificial data and statistical evaluations towards solution accuracy and efficiency with respect to different problem complexities. We contribute additional comparisons between different algorithms and heuristics such as naive Greedy, k-Regret and exact (globally optimal) solutions with the simplex method by the Mixed-Integer Programming (MIP) solver Cplex. Further, we compare the results with our model with respect to solution accuracy and efficiency We conclude that our proposed model solves the presented problem setting globally optimal up to an upper graph complexity bound defined via node degree. Furthermore, our results show that our proposed method outperforms all other algorithms with respect to the solution time required.