A Modelling and Optimization Framework for Real-World Vehicle Routing Problems

Carić, Tonči; Galić, Ante; Fosin, Juraj; Gold, Hrvoje; Reinholz, Andreas

doi:10.5772/5790

Cited by 21 publications

(9 citation statements)

References 18 publications

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“…There are several available papers which describe the application of statistical methods for these causes. Some kinds of interesting real examples are presented in papers [32]- [33]. An especially interesting example of the classic application of real data is presented in research [34] where the term data-driven solving of the VRP problem is introduced.…”

Section: Related Workmentioning

confidence: 99%

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

2020

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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

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Section: Related Workmentioning

confidence: 99%

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

2020

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“…After the routes have been constructed, the following local search procedures are executed. These include intra-and inter-route relocations and exchanges as proposed in [24], but these were adapted to our VRwBP-specific problem.…”

Section: Applying Local Search Proceduresmentioning

confidence: 99%

Planning of Vehicle Routing with Backup Provisioning Using Wireless Sensor Technologies

2017

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Wireless sensor technologies can be used by intelligent transportation systems to provide innovative services that lead to improvements in road safety and congestion, increasing end-user satisfaction. In this article, we address vehicle routing with backup provisioning, where the possibility of reacting to overloading/overcrowding of vehicles at certain stops is considered. This is based on the availability of vehicle load information, which can be captured using wireless sensor technologies. After discussing the infrastructure and monitoring tool, the problem is mathematically formalized, and a heuristic algorithm using local search procedures is proposed. Results show that planning routes with backup provisioning can allow fast response to overcrowding while reducing costs. Therefore, sustainable urban mobility, with efficient use of resources, can be provided while increasing the quality of service perceived by users.

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“…Mathematically, a CVRP is defined as In this formulation, the objective function is expressed by (1) which states that the total traveling distance of all vehicles (i.e., CVRP cost) is to be minimized. Equation (2) represents the constraint that each customer must be visited once by one vehicle, where v travels from customer i to customer j, and 0 otherwise. A constraint in (5) ensures that the total delivery demands of vehicle v do not exceed the vehicle capacity.…”

Section: Introductionmentioning

confidence: 99%

“…A number of methods are available that optimizes customer assignment under vehicles and routes of the vehicles together [5]. On the other hand, the most popular way of solving CVRP is splitting the task into two different phases: firstly, assigning customers under different vehicles and secondly, finding optimal route for each vehicle [2]. www.ijacsa.thesai.org Among several ways for customer node assignment, Sweep clustering algorithm is well studied due to its simplicity.…”

Section: Introductionmentioning

confidence: 99%

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Capacitated Vehicle Routing Problem Solving using Adaptive Sweep and Velocity Tentative PSO

Akhand¹,

Peya²,

Murase³

2017

ijacsa

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Abstract-Vehicle Routing Problem (VRP) has become an integral part in logistic operations which determines optimal routes for several vehicles to serve customers. The basic version of VRP is Capacitated VRP (CVRP) which considers equal capacities for all vehicles. The objective of CVRP is to minimize the total traveling distance of all vehicles to serve all the customers. Various methods are used to solve CVRP, among them the most popular way is splitting the task into two different phases: assigning customers under different vehicles and then finding optimal route of each vehicle. Sweep clustering algorithm is well studied for clustering nodes. On the other hand, route optimization is simply a traveling salesman problem (TSP) and a number of TSP optimization methods are applied for this purpose. In Sweep, cluster formation staring angle is identified as an element of CVRP performance. In this study, a heuristic approach is developed to identify appropriate starting angle in Sweep clustering. The proposed heuristic approach considers angle difference of consecutive nodes and distance between the nodes as well as distances from the depot. On the other hand, velocity tentative particle swarm optimization (VTPSO), the most recent TSP method, is considered for route optimization. Finally, proposed adaptive Sweep (i.e., Sweep with proposed heuristic) plus VTPSO is tested on a large number of benchmark CVRP problems and is revealed as an effective CVRP solving method while outcomes compared with other prominent methods.

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A Modelling and Optimization Framework for Real-World Vehicle Routing Problems

Cited by 21 publications

References 18 publications

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

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

Planning of Vehicle Routing with Backup Provisioning Using Wireless Sensor Technologies

Capacitated Vehicle Routing Problem Solving using Adaptive Sweep and Velocity Tentative PSO

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