Spatial-temporal-demand clustering for solving large-scale vehicle routing problems with time windows

Several metaheuristics use decomposition and pruning strategies to solve large-scale instances of the vehicle routing problem (VRP). Those complexity reduction techniques often rely on simple, problem-specific rules. However, the growth in available data and advances in computer hardware enable data-based approaches that use machine learning (ML) to improve scalability of solution algorithms. We propose a decompose-route-improve (DRI) framework that groups customers using clustering. Its similarity metric incorporates customers' spatial, temporal, and demand data and is formulated to reflect the problem's …

advances clustering complexity computer computer hardware cs.ai cs.lg cs.ne data demand growth hardware instances machine machine learning math.oc metaheuristics pruning routing rules scalability scale simple solve spatial strategies temporal windows