Machine Learning

A Two-Phase Safe Vehicle Routing and Scheduling Problem: Formulations and Solution Algorithms

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  • arXiv
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    A Two-Phase Safe Vehicle Routing and Scheduling Problem: Formulations and Solution Algorithms

    We propose a two phase time dependent vehicle routing and scheduling optimization model that identifies the safest routes, as a substitute for the classical objectives given in the literature such as shortest distance or travel time, through (1) avoiding recurring congestions, and (2) selecting routes that have a lower probability of crash occurrences and non-recurring congestion caused by those crashes. In the first phase, we solve a mixed-integer programming model which takes the dynamic speed variations into account on a graph of roadway networks according to the time of day, and identify the routing of a fleet and sequence of nodes on the safest feasible paths. Second phase considers each route as an independent transit path (fixed route with fixed node sequences), and tries to avoid congestion by rescheduling the departure times of each vehicle from each node, and by adjusting the sub-optimal speed on each arc. A modified simulated annealing (SA) algorithm is formulated to solve both complex models iteratively, which is found to be capable of providing solutions in a considerably short amount of time.

    A Two-Phase Safe Vehicle Routing and Scheduling Problem: Formulations and Solution Algorithms
    by Aschkan Omidvar, Eren Erman Ozguven, O. Arda Vanli, R. Tavakkoli-Moghaddam
    https://arxiv.org/pdf/1710.07147v1.pdf

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