An integer L-shaped algorithm for the vehicle routing problem with time windows and stochastic demands - De La Vega, Jonathan; Gendreau, Michel; Morabito, Reinaldo; Munari, Pedro; Ordóñez, Fernando (European journal of operational research, 2023)
•First Integer L-shaped method for the VRPTWSD under reactive and preventive returns.•New recursive equation to compute the recourse cost related to violated time windows.•Effective procedures to detect violated k-path inequalities and LBF cuts.•Preventive returns are advantageous and may result in significant savings. This paper addresses the vehicle routing problem with time windows and stochastic demands (VRPTWSD). The problem is modeled as a two-stage stochastic program with recourse, in which routes are designed in the first stage and executed in the second. A failure occurs if the load of the vehicle is insufficient to meet the observed demand of a customer, implying recourse actions to recover feasibility. We consider the classical recourse policy where reactive trips to the depot are made in case of failures and a fixed rule-based recourse policy where, in addition, preventive trips are allowed. These recourse actions delay the vehicle and may cause further failures if the arrival times on the remaining customers of the planned route do not satisfy their time windows. An additional recourse action is used to service the customers whose time windows would be violated in the planned routes. We propose an Integer L-shaped algorithm considering the mentioned recourse actions. To the best of our knowledge, this is the first tailored exact approach for the VRPTWSD. Computational experiments using 112 benchmark instances evaluate the performance of this algorithm as well as the quality of the stochastic problem solutions. The results indicate significant savings in the solutions when using the fixed rule-based policy and round-trip recourse actions instead of the classical policy. Additionally, the algorithm performed better with the fixed rule-based policy, solving to optimality all instances with up to 34 customers, and taking less time on instances that were solved to optimality with both policies.