Collaborating Authors

C. H. Robinson Uses Heuristics to Solve Rich Vehicle Routing Problems Artificial Intelligence

We consider a wide family of vehicle routing problem variants with many complex and practical constraints, known as rich vehicle routing problems, which are faced on a daily basis by C.H. Robinson (CHR). Since CHR has many customers, each with distinct requirements, various routing problems with different objectives and constraints should be solved. We propose a set partitioning framework with a number of route generation algorithms, which have shown to be effective in solving a variety of different problems. The proposed algorithms have outperformed the existing technologies at CHR on 10 benchmark instances and since, have been embedded into the company's transportation planning and execution technology platform.

Efficient algorithms for autonomous electric vehicles' min-max routing problem Artificial Intelligence

Increase in greenhouse gases emission from the transportation sector has led companies and government to elevate and support the production of electric vehicles. The natural synergy between increased support for electric and emergence of autonomous vehicles possibly can relieve the limitations regarding access to charging infrastructure, time management, and range anxiety. In this work, a fleet of Autonomous Electric Vehicles (AEV) is considered for transportation and logistic capabilities with limited battery capacity and scarce charging station availability are considered while planning to avoid inefficient routing strategies. We introduce a min-max autonomous electric vehicle routing problem (AEVRP) where the maximum distance traveled by any AEV is minimized while considering charging stations for recharging. We propose a genetic algorithm based meta-heuristic that can efficiently solve a variety of instances. Extensive computational results, sensitivity analysis, and data-driven simulation implemented with the robot operating system (ROS) middleware are performed to corroborate the efficiency of the proposed approach, both quantitatively and qualitatively.

An adaptive data-driven approach to solve real-world vehicle routing problems in logistics Artificial Intelligence

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 inno vative 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 da ta transformation approac hes, heuristics and Tabu search . Moreover, as the performance of the algorithm depend s on the set of control parameters and constants, a predictive model that adaptively adjusts these parameters and constan ts according to historica l 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 instance s and validated over real benchmark instance s as well . The successful feasibility of the given routes, in a real environment, is also presented.

A Hybrid Pricing and Cutting Approach for the Multi-Shift Full Truckload Vehicle Routing Problem Artificial Intelligence

Full truckload transportation (FTL) in the form of freight containers represents one of the most important transportation modes in international trade. Due to large volume and scale, in FTL, delivery time is often less critical but cost and service quality are crucial. Therefore, efficiently solving large scale multiple shift FTL problems is becoming more and more important and requires further research. In one of our earlier studies, a set covering model and a three-stage solution method were developed for a multi-shift FTL problem. This paper extends the previous work and presents a significantly more efficient approach by hybridising pricing and cutting strategies with metaheuristics (a variable neighbourhood search and a genetic algorithm). The metaheuristics were adopted to find promising columns (vehicle routes) guided by pricing and cuts are dynamically generated to eliminate infeasible flow assignments caused by incompatible commodities. Computational experiments on real-life and artificial benchmark FTL problems showed superior performance both in terms of computational time and solution quality, when compared with previous MIP based three-stage methods and two existing metaheuristics. The proposed cutting and heuristic pricing approach can efficiently solve large scale real-life FTL problems.

A Multi-Agent System for Solving the Dynamic Capacitated Vehicle Routing Problem with Stochastic Customers using Trajectory Data Mining Artificial Intelligence

The worldwide growth of e-commerce has created new challenges for logistics companies, one of which is being able to deliver products quickly and at low cost, which reflects directly in the way of sorting packages, needing to eliminate steps such as storage and batch creation. Our work presents a multi-agent system that uses trajectory data mining techniques to extract territorial patterns and use them in the dynamic creation of last-mile routes. The problem can be modeled as a Dynamic Capacitated Vehicle Routing Problem (VRP) with Stochastic Customer, being therefore NP-HARD, what makes its implementation unfeasible for many packages. The work's main contribution is to solve this problem only depending on the Warehouse system configurations and not on the number of packages processed, which is appropriate for Big Data scenarios commonly present in the delivery of e-commerce products. Computational experiments were conducted for single and multi depot instances. Due to its probabilistic nature, the proposed approach presented slightly lower performances when compared to the static VRP algorithm. However, the operational gains that our solution provides making it very attractive for situations in which the routes must be set dynamically.