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 (VRPs) in the field of logistics. The work consists of two basic units: (i) an innovative multistep algorithm for successful and entirely feasible solving of the VRPs in logistics and (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 (GLMs) and support vector machine (SVM). The algorithm, along with the control parameters, which uses the prediction method, was acquired and 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.