An Adaptive Data-Driven Approach to Solve Real-World Vehicle Routing Problems in Logistics

Abstract

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.Keywords Vehicle routing problem  Multi-step algorithm  Data-driven approach  Parameter setting problem  Real-world constraints in logistics

Comparison Analysis of Facebook’s Prophet, Amazon’s DeepAR+ and CNN-QR Algorithms for Successful Real-World Sales Forecasting

“…As a third segment of the SCM, it is necessary to implement an efficient Vehicle Routing Problem (VRP) solver. A multiphase approach improved by the GPS data analysis is described in two papers by Žunić et al [18,19]. Several machine learning models have been used to predict unloading times for each customer (Žunić et al [20]) as well as other parameters of practical vehicle routing problems with realistic constraints (Zunic et al [21]).…”

Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data

“…Based on the knowledge about the sales forecasting, it is worth mentioning that we are currently working on integrating this framework into our previously developed products for the retail industry as a big Smart supply chain management (SCM) concept (Zunic et al [17,18,19]). Also, the proposed forecasting method together with the global positioning system (GPS) data could be successfully used to determine some parameters and constants of the real-world vehicle routing problems (VRP), such as unloading time, road and time distances between customers and so on (Zunic et al [20,21,22]).…”

Cluster-based approach for successful solving real-world vehicle routing problems

“…This work was the starting point for the approach presented later in this paper, with significant improvements in both performance and algorithm terms. It was also used as the integral part of the algorithms proposed in works [10]- [16]. All these facts indicate that the clusterbased approach is exceptionally suitable for solving such problems, both as an individual algorithm, as well as the starting point of the more complex multiphase algorithms.…”

“…Delete node j from the lists L and L' and proceed to the following step (9). 9) If the L' list is empty, keep the K n list that defines the cluster C n and proceed to the step (10). Otherwise, turn back to the step (4).…”