2020/01/05 by Emir Žunić, Emir Zunic, Dženana Đonko +5
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Maritime Ports and Logistics #Vehicle Routing Optimization Methods #cs.AI
paper · pdf · doi:10.48550/arxiv.2001.02094
arxiv created 2020/01/05 · openalex publication_date 2020/01/05 · arxiv updated 2020/01/08 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
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.