Self-adaptive Tabu Search for multiple demands vehicle routing and scheduling problems with time windows

Multiple demands vehicle routing problems with time windows (MVRPTW) are proposed by the organisation of transportation in industrial production process. In MVRPTW, goods, such as raw and processed materials, are sent to their destinations at the scheduled time. By giving its transformed model, this paper presents a self-adaptive tabu search for MVRPTW with one vehicle. Our algorithm can memory the search process comprehensively, and applies automatically intensification and diversification in proper time. The algorithm converges rapidly from our computing experiences.