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

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Emir Žunić ◽  
Dženana Đonko ◽  
Emir Buza
Keyword(s):  
Vehicle Routing ◽  
Real World ◽  
Prediction Method ◽  
Transportation Systems ◽  
Data Driven ◽  
Support Vector ◽  
Control Parameters ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Benchmark Instances

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 (VRPs) in the field of logistics. The work consists of two basic units: (i) an innovative multistep algorithm for successful and entirely feasible solving of the VRPs in logistics and (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 (GLMs) and support vector machine (SVM). The algorithm, along with the control parameters, which uses the prediction method, was acquired and 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.

Variable Fixing for Two-Arc Sequences in Branch-Price-and-Cut Algorithms on Path-Based Models

2020 ◽  
Vol 54 (5) ◽  
pp. 1170-1188 ◽  
Author(s):  
Guy Desaulniers ◽  
Timo Gschwind ◽  
Stefan Irnich
Keyword(s):  
Vehicle Routing ◽  
Time Windows ◽  
Solution Process ◽  
Mixed Integer ◽  
Linear Programs ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Routing Problem ◽  
Benchmark Instances ◽  
Variable Fixing

Variable fixing by reduced costs is a popular technique for accelerating the solution process of mixed-integer linear programs. For vehicle-routing problems solved by branch-price-and-cut algorithms, it is possible to fix to zero the variables associated with all routes containing at least one arc from a subset of arcs determined according to the dual solution of a linear relaxation. This is equivalent to removing these arcs from the network used to generate the routes. In this paper, we extend this technique to routes containing sequences of two arcs. Such sequences or their arcs cannot be removed directly from the network because routes traversing only one arc of a sequence might still be allowed. For some of the most common vehicle-routing problems, we show how this issue can be overcome by modifying the route-generation labeling algorithm in order to remove indirectly these sequences from the network. The proposed acceleration strategy is tested on benchmark instances of the vehicle-routing problem with time windows (VRPTW) and four variants of the electric VRPTW. The computational results show that it yields a significant speedup, especially for the most difficult instances.

scholarly journals A heuristic algorithm for multiple trip vehicle routing problems with time window constraint and outside carrier selection

2019 ◽  
Vol 4 (3) ◽  
pp. 256-273
Author(s):  
Ching-Wu Chu ◽  
Hsiu-Li Hsu
Keyword(s):  
Cost Function ◽  
Vehicle Routing ◽  
Heuristic Algorithm ◽  
Real World ◽  
Time Windows ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Content Type ◽  
Total Cost ◽  
Total Cost Function

Purpose In this paper, the authors introduced a real world new problem, the multi-trip vehicle routing problem with time windows and the possible use of a less-than-truckload carrier to satisfy customer demands. The purpose of this paper is to develop a heuristic algorithm to route the private trucks with time windows and to make a selection between truckload and less-than-truckload carriers by minimizing a total cost function. Design/methodology/approach Both mathematical model and heuristic algorithm are developed for routing the private trucks with time windows and for selecting of less-than-truckload carriers by minimizing the total cost function. Findings In all, 40 test problems were examined with the heuristics. Computational results show that the algorithm obtains the optimal or near-optimal solutions efficiently in terms of time and accuracy. Originality/value The research described in this paper differs from the previous one on fleet planning or vehicle routing, in that it modifies the Clarke and Wright method by shifting the performance measure from a distance to cost and also incorporates the fixed cost of different types of trucks into the model. In addition, the authors simultaneously consider the multiple trip vehicle routing problems with time windows and the selection of less-than-truckload carriers that is an integrated scenario of real-world application. To the best of the authors’ knowledge, this scenario has not been considered in the literature.

scholarly journals A Proposal and Analysis of New Realistic Sets of Benchmark Instances for Vehicle Routing Problems with Asymmetric Costs

2021 ◽  
Vol 11 (11) ◽  
pp. 4790
Author(s):  
Keyju Lee ◽  
Junjae Chae
Keyword(s):  
Vehicle Routing ◽  
Spatial Data ◽  
Urban Transportation ◽  
Vehicle Routing Problems ◽  
Distribution Centers ◽  
Routing Problems ◽  
Benchmark Instances ◽  
Application Programming ◽  
Asymmetric Costs ◽  
The Cost

Despite their importance, relatively little attention has been paid to vehicle routing problems with asymmetric costs (ACVRPs), or their benchmark instances. Taking advantage of recent advances in map application programming interfaces (APIs) and shared spatial data, this paper proposes new realistic sets of ACVRP benchmark instances. The spatial data of urban distribution centers, postal hubs, large shopping malls, residential complexes, restaurant businesses and convenience stores are used. To create distance and time matrices, the T map API, one of the most frequently used real time path analysis and distance measurement tools in Korea, is used. This paper also analyzes some important issues prevailing in urban transportation environments. These include the challenges of accounting for the frequency and distance in which air travel differs from reality when measuring closeness, the differences in distance and time for outgoing and return trips, and the rough conversion ratios from air distance to road distance and to road time. This paper contributes to the research community by providing more realistic ACVRP benchmark instances that reflect urban transportation environments. In addition, the cost matrix analyses provide insights into the behaviors of urban road networks.

Ant colony optimization for real-world vehicle routing problems

2007 ◽  
Vol 1 (2) ◽  
pp. 135-151 ◽  
Author(s):  
A. E. Rizzoli ◽  
R. Montemanni ◽  
E. Lucibello ◽  
L. M. Gambardella
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Real World ◽  
Ant Colony ◽  
Vehicle Routing Problems ◽  
Routing Problems

A Genetic Algorithm's Approach to the Optimization of Capacitated Vehicle Routing Problems

2016 ◽  
pp. 209-235 ◽  
Author(s):  
Mariano Frutos ◽  
Fernando Tohmé ◽  
Fabio Miguel
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Real World ◽  
Selective Pressure ◽  
Fresh Fruit ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
The Family ◽  
Capacitated Vehicle ◽  
Real World Problems

This chapter addresses the family of problems known in the literature as Capacitated Vehicle Routing Problems (CVRP). A procedure is introduced for the optimization of a version of the generic CVRP. It generates feasible clusters and, in a first step, yields a coding of their ordering. The next stage provides this information to a genetic algorithm for its optimization. A selective pressure process is added in order to improve the selection and subsistence of the best candidates. This arrangement allows improving the performance of the algorithm. We test it using Van Breedam and Taillard's problems, yielding similar results as other algorithms in the literature. Besides, we test the algorithm on real-world problems, corresponding to an Argentinean company distributing fresh fruit. Four instances, with 50, 100, 150 and 200 clients were examined, giving better results than the current plans of the company.

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