%0 Generic
%A Solano Charris, Elyn Lizeth
%A Prins, Christian
%A Andréa Cynthia Santos
%8 2016-05-22
%U http://hdl.handle.net/10818/37441
%X In this paper, a bi-objective Vehicle Routing Problem (bi-RVRP) with uncertainty in
both demands and travel times is studied by means of robust optimization. Uncertain demands per
customer are modeled by a discrete set of scenarios representing the deviations from an expected
demand, while uncertain travel times are independent from customer demands. Then, traffic records
are considered to get discrete scenarios to each arc of the transportation network. Here, the bi-RVRP
aims at minimizing the worst total cost of traversed arcs and minimizing the maximum total unmet
demand over all scenarios. As far as we know, this is the first study for the bi-RVRP which finds
practical applications in urban transportation, e.g., serving small retail stores. To solve the problem,
different variations of solution approaches, coupled with a local search procedure are proposed: the
Multiobjective Evolutionary Algorithm (MOEA) and the Non-dominated Sorting Genetic Algorithm
(NSGAII). Different metrics are used to measure the algorithmic performance, the convergence, as well
as the diversity of solutions for the different methods
%I RAIRO Operations Research
%T Solving the bi-objective Robust Vehicle Routing Problem with uncertain costs and demands
%R 10.1051/ro/2016048
%~ Intellectum