Variable Neighborhood Search Algorithms for the multi-depot dial-a-ride problem with heterogeneous vehicles and users
This work addresses a complex routing problem for healthcare or transportation services, but it is incremental as it focuses on optimizing existing VNS methods rather than introducing a new paradigm.
The study tackled the multi-depot dial-a-ride problem with heterogeneous vehicles and users by developing and comparing Variable Neighborhood Search algorithms to minimize total routing cost while respecting constraints like time-windows and capacity, resulting in the identification of optimal algorithm configurations through testing on literature and real-world instances.
In this work, a study on Variable Neighborhood Search algorithms for multi-depot dial-a-ride problems is presented. In dial-a-ride problems patients need to be transported from pre-specified pickup locations to pre-specified delivery locations, under different considerations. The addressed problem presents several constraints and features, such as heterogeneous vehicles, distributed in different depots, and heterogeneous patients. The aim is of minimizing the total routing cost, while respecting time-window, ride-time, capacity and route duration constraints. The objective of the study is of determining the best algorithm configuration in terms of initial solution, neighborhood and local search procedures. At this aim, two different procedures for the computation of an initial solution, six different type of neighborhoods and five local search procedures, where only intra-route changes are made, have been considered and compared. We have also evaluated an "adjusting procedure" that aims to produce feasible solutions from infeasible solutions with small constraints violations. The different VNS algorithms have been tested on instances from literature as well as on random instances arising from a real-world healthcare application.