Problema de localización y ruteo en centros urbanos considerando demanda estocástica

Las ciudades no siempre han presentado problemas relacionados con la distribución de mercancías como: la congestión vial, la contaminación, el daño de las vías, las restricciones de movilidad e incluso peajes urbanos, entre otras medidas; pues anteriormente las urbes eran menos pobladas y no tenían la misma cantidad de vehículos como existen hoy en día (Antún, 2013); además, las dinámicas de compra eran diferentes. Las tiendas físicas están llegando a un punto de estancamiento en el que deben diversificar la forma de ofrecer sus productos para mantener su nivel de ventas. Ahora todo se quiere pedir online, a domicilio, personalizado, por medio de dispositivos móviles y en el menor tiempo posible. En otras palabras, la logística ya es un factor real de competitividad..

The capacitated vehicle routing problem with evidential demands

International audienceWe propose to represent uncertainty on customer demands in the Capacitated Vehicle Routing Problem (CVRP) using the theory of evidence. To tackle this problem, we extend classical stochastic programming modelling approaches. Specifically, we propose two models for this problem. The first model is an extension of the chance-constrained programming approach, which imposes certain minimum bounds on the belief and plausibility that the sum of the demands on each route respects the vehicle capacity. The second model extends the stochas-tic programming with recourse approach: for each route, it represents by a belief function the uncertainty on its recourses, i.e., corrective actions performed when the vehicle capacity is exceeded, and defines the cost of a route as its classical cost (without recourse) plus the worst expected cost of its recourses. We solve the proposed models using a metaheuristic algorithm and present experimental results on instances adapted from a well-known CVRP data set

A Recourse Approach for the Capacitated Vehicle Routing Problem with Evidential Demands

International audienceThe capacitated vehicle routing problem with stochastic demands can be modelled using either the chance-constrained approach or the recourse approach. In previous works, we extended the former approach to address the case where uncertainty on customer demands is represented by belief functions, that is where customers have so-called evidential demands. In this paper, we propose an extension of the recourse approach for this latter case. We also provide a technique that makes computations tractable for realistic situations. The feasibility of our approach is then shown by solving instances of this difficult problem using a metaheuristic algorithm

A Recourse Approach for the Capacitated Vehicle Routing Problem with Evidential Demands

International audienceThe capacitated vehicle routing problem with stochastic demands can be modelled using either the chance-constrained approach or the recourse approach. In previous works, we extended the former approach to address the case where uncertainty on customer demands is represented by belief functions, that is where customers have so-called evidential demands. In this paper, we propose an extension of the recourse approach for this latter case. We also provide a technique that makes computations tractable for realistic situations. The feasibility of our approach is then shown by solving instances of this difficult problem using a metaheuristic algorithm

The Capacitated Vehicle Routing Problem with Evidential Demands: a Belief-Constrained Programming Approach

International audienceThis paper studies a vehicle routing problem, where vehicles have a limited capacity and customer demands are uncertain and represented by belief functions. More specifically, this problem is formalized using a belief function based extension of the chance-constrained programming approach, which is a classical modeling of stochastic mathematical programs. In addition, it is shown how the optimal solution cost is influenced by some important parameters involved in the model. Finally, some instances of this difficult problem are solved using a simulated annealing metaheuristic, demonstrating the feasibility of the approach