A solution approach for multi?trip vehicle routing problems with time windows, fleet sizing, and depot location

We present a solution approach for multi-trip vehicle routing problem with time windows in which the locations of prescribed number depots and fleet sizes must also be optimized. Given complexity task, we divide into subproblems that are solved sequentially. First, address strategic decisions, once remain constant thereafter. Depots allocated by solving p-median determined identifying requirements several worst-case demand instances. Then, operational planning aspect: optimizing routes on daily basis to satisfy fluctuating customer demand. assign customers based distance “routing effort,” combine tailor-made branch-and-cut algorithm heuristic consisting route construction phase packing trips. Our decision models robust sense when applied unseen data, all could visited depot locations. methods both cost-effective. The exact method yields acceptable optimality gaps 20 min runs less than 2 min, finding optimal or near-optimal solutions small Finally, explore trade-off between costs, costs make recommendations depots. was entered 12th AIMMS-MOPTA Optimization Modeling Competition awarded first prize.