An adaptive data-driven approach to solve real-world vehicle routing
  problems in logistics
  Transportation occupies one-third of the amount in the logistics costs, and
accordingly transportation systems largely influence the performance of the
logistics system. This work presents an adaptive data-driven innovative modular
approach for solving the real-world Vehicle Routing Problems (VRP) in the field
of logistics. The work consists of two basic units: (i) an innovative
multi-step algorithm for successful and entirely feasible solving of the VRP
problems in logistics, (ii) an adaptive approach for adjusting and setting up
parameters and constants of the proposed algorithm. The proposed algorithm
combines several data transformation approaches, heuristics and Tabu search.
Moreover, as the performance of the algorithm depends on the set of control
parameters and constants, a predictive model that adaptively adjusts these
parameters and constants according to historical data is proposed. A comparison
of the acquired results has been made using the Decision Support System with
predictive models: Generalized Linear Models (GLM) and Support Vector Machine
(SVM). The algorithm, along with the control parameters, which using the
prediction method were acquired, was incorporated into a web-based enterprise
system, which is in use in several big distribution companies in Bosnia and
Herzegovina. The results of the proposed algorithm were compared with a set of
benchmark instances and validated over real benchmark instances as well. The
successful feasibility of the given routes, in a real environment, is also
presented.
