Title
Intelligent and Sustainable Transportation through Multi-Objective Model for the Logistic Route-Order Dispatching System
Abstract
Solution of multi-objective optimization in the logistics sector have become an integral important part of the Intelligent Transportation System (ITS). In this work we focus on the intelligent and sustainable transportation processes through the design of the multi-objective model for the logistic route-order dispatching system. We consider transportation costs, emissions, order importance and risks for failures, for the logistic route-order dispatching system. We present an Integer Linear Programming (ILP) optimization model and apply state-of-the-art techniques as a part of SCIP framework to solve pilot problem instances and evaluate the performance of the model. We obtain results of solving the model on a single monolithic Google Cloud Compute (GCP) to estimate the time complexity of the solving process in relation to the various problem sizes. The results from the experiments show low complexity of the problems of various sizes. Therefore scalability of the model looks promising for the applicability in various industry-related scenarios and computing environments. In particular, using hybrid-cloud systems and state-of-the-art optimization frameworks such as IBM CPLEX or Gurobi.
This work has been supported by FFG Project Grant Agreement No. 899648. We acknowledge ARCOS Research Group for provisioning of TUCAN HPC cluster for data analysis and computations. This work was partially supported by the Spanish Ministry of Science and Innovation Project “New Data Intensive Computing Methods for High-End and Edge Computing Platforms (DECIDE)” Ref. PID2019-107858GB-I00.
Further Information
GoodIT ’23: Proceedings of the 2023 ACM Conference on Information Technology for Social Good – September 2023 – Pages 530–536 – DOI: https://doi.org/10.1145/3582515.3609578