The Million-Variable "March" for Stochastic Combinatorial Optimization
Journal of Global Optimization
On a stochastic sequencing and scheduling problem
Computers and Operations Research
Computations with disjunctive cuts for two-stage stochastic mixed 0-1 integer programs
Journal of Global Optimization
A general algorithm for solving two-stage stochastic mixed 0-1 first-stage problems
Computers and Operations Research
A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands
Computers and Operations Research
The Vehicle Routing Problem with Stochastic Demand and Duration Constraints
Transportation Science
An integer L-shaped algorithm for the Dial-a-Ride Problem with stochastic customer delays
Discrete Applied Mathematics
The capacitated vehicle routing problem with stochastic demands and time windows
Computers and Operations Research
A branch-and-cluster coordination scheme for selecting prison facility sites under uncertainty
Computers and Operations Research
A branch-and-price algorithm for the capacitated vehicle routing problem with stochastic demands
Operations Research Letters
A Branch-and-Price Algorithm for the Capacitated Arc Routing Problem with Stochastic Demands
Operations Research Letters
Hardness results for the probabilistic traveling salesman problem with deadlines
ISCO'12 Proceedings of the Second international conference on Combinatorial Optimization
Computers and Operations Research
The vehicle rescheduling problem
Computers and Operations Research
A two-stage approach to the orienteering problem with stochastic weights
Computers and Operations Research
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The classical Vehicle Routing Problem consists ofdetermining optimal routes form identical vehicles, starting and leaving at the depot, such that every customer is visited exactly once. In the capacitated version (CVRP) the total demand collected along a route cannot exceed the vehicle capacity. This article considers the situation where some ofthe demands are stochastic. This implies that the level of demand at each customer is not known before arriving at the customer. In some cases, the vehicle may thus be unable to load the customer's demand, even ifthe expected demand along the route does not exceed the vehicle capacity. Such a situation is referred to as a failure. The capacitated vehicle routing problem with stochastic demands (SVRP) then consists ofminimizing the total cost ofthe planned routes and of expected failures. Here, penalties for failures correspond to return trips to the depot. The vehicle first returns to the depot to unload, then resumes its trip as originally planned. This article studies an implementation of the IntegerL-shaped method for the exact solution of the SVRP. It develops new lower bounds on the expected penalty for failures. In addition, it provides variants of the optimality cuts for the SVRP that also hold at fractional solutions. Numerical experiments indicate that some instances involving up to 100 customers and few vehicles can be solved to optimality within a relatively short computing time.