10h30 - 10h55
Limited Memory Subset Row Cuts (lm-SRC) for Routing Problems
Subset Row Cuts are frequently used to improve linear relaxation bounds for routing problems and often significantly impact the pricing efficiency. We propose the lm-SRC a weakening of the SRCs. They are decisive in the optimal resolution of CVRP instances with 300 or more clients. Extensive experiments are reported.
10h55 - 11h20
Global Optimization of the Vehicle Routing and Scheduling Problem with Delivery and Installation
In the context of household appliance distribution, the delivery and installation activities are usually separated and synchronized, which imposes great difficulty on vehicle routing and scheduling. In this paper, a mixed integer program is proposed to model the synchronized vehicle routing and scheduling problem of delivery and installation (VRPDI) of these products. We develop a two-step exact algorithm to solve the problem. The first step includes a two-stage hybrid heuristics based on Clarke and Wright Savings algorithm, and the second step is based on Augmented Lagrangian method. The effectiveness and efficiency of this algorithm are tested and verified by several numerical tests.
11h20 - 11h45
A Priori Optimization with Recourse for the Vehicle Routing Problem with Hard Time Windows and Stochastic Service Times
he VRPTW-ST differs from other routing problems with stochastic times for the presence of hard time windows. We model the VRPTW-ST as a two-stage stochastic program and define two recourse policies to recover first stage infeasibility. We solve the VRPTW-ST by exact branch-cut-and-price algorithms. Our development included finding tight bounds on partial route reduced costs to efficiently prune dominated labels in the column generation subproblem. Results on benchmark data show that our methods are able to solve instances with up to 50 customers for both recourse policies.
11h45 - 12h10
The Vehicle Routing Problem with Stochastic Two-Dimensional Items
We consider a stochastic vehicle routing problem where a discrete probability distribution characterizes the two-dimensional size of a subset of the items to be delivered to customers. Although some item sizes are not known with certainty when the routes are planned, they become known when it is time to load the vehicles.