Fleet sizing and allocation for on-demand last-mile transportation systems

The last-mile problem refers to the provision of travel service from nearest public transportation node home or other destination. Last-Mile Transportation Systems (LMTS), which have recently emerged, provide on-demand shared transportation. In this paper, we investigate fleet sizing and allocation for LMTS. Specifically, consider perspective a provider who wants determine number servicing vehicles allocate multiple regions in particular city. each region, passengers demanding services arrive batches, allocated deliver their final destinations. passenger demand (i.e., size batch passengers) is random hard predict advance, especially with limited data during planning process. quality fleet-allocation decisions function vehicle fixed cost plus weighted sum passenger’s waiting time before boarding in-vehicle riding time. We propose analyze two models – stochastic programming model distributionally robust optimization solve problem, assuming known unknown distribution demand, respectively. conduct extensive numerical experiments evaluate discuss insights implications into optimal LMTS under uncertainty.