An Auction-Based Multiagent Simulation for the Matching Problem in Dynamic Vehicle Routing Problem with Occasional Drivers

Abstract

This research incorporated an auction mechanism into the vehicle routing problem with occasional drivers and produced simulations in an agent-based environment. Auctions were used to match online orders with potential occasional drivers. While a centralized system optimizes system performance under global objectives, the novel decentralized approach presented here illustrates emergent phenomena resulting from the interaction of individual entities in highly dynamic cases. In the simulations, the auctions were executed after a fixed time interval called a rolling time horizon. Our results suggest that the appropriate rolling time horizon produces a lower average unit compensation cost because better matches can be found when the accumulation of online orders and occasional drivers is maintained at a certain level. The simulation results also indicate that the use of an auction mechanism instead of simple nonauction rules can improve the average unit compensation cost by up to 25.1%.