Hybridizing large neighborhood search and exact methods for generalized vehicle routing problems with time windows

Small and large neighborhood search for the park-and-loop routing problem with parking selection

“…Dumez et al (2021a) combines small and large destruction sizes for the LNS. This approach is improved in Dumez et al (2021b) to solve variants of the generalized VRP. The LNS variant of Dumez et al (2021a) and Dumez et al (2021b) was further detailed in Dumez (2021).…”

A matheuristic for a 2-echelon vehicle routing problem with capacitated satellites and reverse flows

“…If the best solution has not been improved for several iterations, a large destruction step is performed for diversification. This small-and-large strategy has been proved highly successful on variants of the generalized vehicle routing problem with time windows (Dumez et al, 2021). A short synopsis of the types of operators used in our LNS algorithms is provided in Table 3 indicating whether the operator is used in LNS 1 and LNS 2 and who first introduced it.…”

Adaptive Large Neighborhood Search for Vehicle Routing Problems with Transshipment Facilities Arising in City Logistics

“…In order to solve the problem, the authors present a large neighborhood search (LNS). Extending their work on the VRPDO, Dumez et al (2021b) introduce an adapted Balas-Simonetti neighborhood to their LNS to further improve the solution quality. Moreover, Tilk et al (2020), also address the VRPDO and present a branch-price-and-cut algorithm that solves instances with up to 50 customers and 100 options to optimality.…”

“…In order to solve both the VRPRDL and VRPHRDL, Ozbaygin et al (2017) developed a branch-and-price algorithm. Later, Dumez et al (2021a), Dumez et al (2021b), as well as Tilk et al (2020) also study both problems to validate their algorithms for the VRPDO, leading to new best solutions and large time improvements on the instances of Ozbaygin et al (2017) and Reyes et al (2017).…”

“…However, for the fleet size and mix problem variants, constraint ( 15) is left out because the number of vehicles per type is not restricted. Furthermore, as Dumez et al (2021b) point out, the set-partitioning constraint ( 14) can be modified to an easier to solve set-covering constraint (≥ 1 instead of = 1) if the triangle inequality holds true for the problem instance that is considered. If the resulting solution of the set-covering problem contains a customer request in more than one route, we remove the redundant occurrences based on a simple greedy procedure.…”