Combining Distributed Matchmaking and Clustering to Prune the Solution Space in Distributed Optimization Problems - Demonstrated in the RailCab System

Abstract

The joined travelling of vehicles is an important instrument of cost reduction in the innovative RailCab concept. While the problem of joining groups of vehicles into convoys and determining convoy routes can be easily understood as optimization problem, the distributed nature and large number of vehicles and stops inhibits the direct application of operations research methods and problems. In this paper we introduce a combination of multiagent planning techniques like distributed matchmaking and filtering, data clustering from computational intelligence and heuristics from operations research to solve the complex task of convoy formation in the RailCab system. It is shown how the solution space of the optimization problem can be efficiently reduced during the matchmaking by applying filtering mechanisms and clustering to identify groups of agents with compatible optimization constraints.