A Rich Vehicle Routing Problem with Multiple Trips and Driver Shifts

Let g tj = 1 or 0 depending on whether customer x t is an element of S, or not respectively, and let C (Sj ) be the cost of the TSP solution through the set of customers in

In particular, among removal operators, the group removal is the best operator on the VRP-RPR instances while the joint segment removal is the best when being used to solve the

We also describe lower bounds derived from a set-partitioning based formulation (SP ) of the problem, and computed using two efficient dual ascent heuristics that use two new

In this article, we proposed to represent uncertainty on customer demands in the capacitated vehicle routing problem via the theory of evidence. We tackled this problem by

Based on a given ordering, each customer is considered in turn and inserted at its best feasible insertion place over every route in every workday, including a new (empty) workday

The vehicle routing problem with stochastic demands (VRPSD) consists in designing routes with a minimal expected travel time to satisfy a set of customers with

Our main contributions are (i) the study of techniques to reduce the number of extreme points of the uncertainty polytope that must be considered in the model, and (ii)

A column generation based algorithm is proposed to solve this rich arc routing problem.. Its performance is analysed by benchmarking a real world dataset from the French