scholarly journals Combining a hierarchical task network planner with a constraint satisfaction solver for assembly operations involving routing problems in a multi-robot context

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141878208 ◽  
Author(s):  
Jorge Muñoz-Morera ◽  
Francisco Alarcon ◽  
Ivan Maza ◽  
Anibal Ollero
Keyword(s):  
Vehicle Routing ◽  
Constraint Satisfaction ◽  
Vehicle Routing Problem ◽  
Problem Solver ◽  
Routing Problem ◽  
Hierarchical Task Network ◽  
The Cost ◽  
Assembly Operations ◽  
Structure Assembly ◽  
Multi Robot

This work addresses the combination of a symbolic hierarchical task network planner and a constraint satisfaction solver for the vehicle routing problem in a multi-robot context for structure assembly operations. Each planner has its own problem domain and search space, and the article describes how both planners interact in a loop sharing information in order to improve the cost of the solutions. The vehicle routing problem solver gives an initial assignment of parts to robots, making the distribution based on the distance among parts and robots, trying also to maximize the parallelism of the future assembly operations evaluating during the process the dependencies among the parts assigned to each robot. Then, the hierarchical task network planner computes a scheduling for the given assignment and estimates the cost in terms of time spent on the structure assembly. This cost value is then given back to the vehicle routing problem solver as feedback to compute a better assignment, closing the loop and repeating again the whole process. This interaction scheme has been tested with different constraint satisfaction solvers for the vehicle routing problem. The article presents simulation results in a scenario with a team of aerial robots assembling a structure, comparing the results obtained with different configurations of the vehicle routing problem solver and showing the suitability of using this approach.

scholarly journals Brief communication: Vehicle routing problem and UAV application in the post-earthquake scenario

2017 ◽  
Author(s):  
Marco Cannioto ◽  
Antonino D'Alessandro ◽  
Giosuè Lo Bosco ◽  
Salvatore Scudero ◽  
Giovanni Vitale
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Seismic Event ◽  
Computing Time ◽  
Research Problem ◽  
Routing Problem ◽  
Earthquake Scenario ◽  
Short Computing Time ◽  
The Cost ◽  
The Town

Abstract. In this paper we simulate a Unmanned Aerial Vehicle's (UAV) recognition after a possible case of diffuse damage after a seismic event in the town of Acireale (Sicily, Italy). Given a set of sites (84 relevant buildings) and the range of the UAV, we are able to find the number of vehicles to employ and the shortest survey path. The problem of finding the shortest survey path is an operational research problem called Vehicle Routing Problem (VRP) whose solution is known to be computationally time-consuming. We used the Simulated Annealing (SA) heuristic that is able to provide stable solutions in relatively short computing time. We also examined the distribution of the cost of the solutions varying the depot on a regular grid in order to assess the best area where to execute the survey.

The Research on VRP Based on Max-Min Ant Colony Algorithm

2011 ◽  
Vol 219-220 ◽  
pp. 1285-1288 ◽  
Author(s):  
Chang Min Chen ◽  
Wei Cheng Xie ◽  
Song Song Fan
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Search Time ◽  
Ant Colony ◽  
Local Optimum ◽  
Simulation Data ◽  
Routing Problem ◽  
Np Hard Problem ◽  
The Cost

Vehicle routing problem (VRP) is the key to reducing the cost of logistics, and also an NP-hard problem. Ant colony algorithm is a very effective method to solve the VRP, but it is easy to fall into local optimum and has a long search time. In order to overcome its shortcomings, max-min ant colony algorithm is adopted in this paper, and its simulation system is designed in GUI of MATLAB7.0. The results show that the vehicle routing problem can well achieves the optimization of VRP by accessing the simulation data of database.

scholarly journals The Joint Network Vehicle Routing Game

2020 ◽  
Author(s):  
Mathijs van Zon ◽  
Remy Spliet ◽  
Wilco van den Heuvel
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Cost Allocation ◽  
Allocation Problem ◽  
Routing Problem ◽  
Generation Algorithm ◽  
Core Allocation ◽  
New Variant ◽  
Delivery Routes ◽  
The Cost

Collaborative transportation can significantly reduce transportation costs as well as greenhouse gas emissions. However, allocating the cost to the collaborating companies remains difficult. We consider the cost-allocation problem, which arises when companies, each with multiple delivery locations, collaborate by consolidating demand and combining delivery routes. We model the corresponding cost-allocation problem as a cooperative game: the joint network vehicle routing game (JNVRG). We propose a row generation algorithm to either determine a core allocation for the JNVRG or show that no such allocation exists. In this approach, we encounter a row generation subproblem, which we model as a new variant of a vehicle routing problem with profits. Moreover, we propose two main acceleration strategies for the row generation algorithm. First, we generate rows by relaxing the row generation subproblem, exploiting the tight linear programming (LP) bounds for our formulation of the row generation subproblem. Secondly, we propose to also solve the row generation subproblem heuristically and to only solve it to optimality when the heuristic fails. We demonstrate the effectiveness of the proposed row generation algorithm and the acceleration strategies by means of numerical experiments for both the JNVRG as well as the traditional vehicle routing game, which is a special case of the JNVRG. We create instances based on benchmark instances of the capacitated vehicle routing problem from the literature. We are able to either determine a core allocation or show that no core allocation exists, for instances ranging from 5 companies with a total of 79 delivery locations to 53 companies with a total of 53 delivery locations.

scholarly journals Analysis of an Automated Vehicle Routing Problem in Logistics considering Path Interruption

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yong Zhang ◽  
Lei Shi ◽  
Jing Chen ◽  
Xuefeng Li
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Initial Period ◽  
Pso Algorithm ◽  
Automated Vehicles ◽  
Routing Problem ◽  
Automated Vehicle ◽  
Last Mile ◽  
The Cost

The application of automated vehicles in logistics can efficiently reduce the cost of logistics and reduce the potential risks in the last mile. Considering the path restriction in the initial stage of the application of automated vehicles in logistics, the conventional model for a vehicle routing problem (VRP) is modified. Thus, the automated vehicle routing problem with time windows (AVRPTW) model considering path interruption is established. Additionally, an improved particle swarm optimisation (PSO) algorithm is designed to solve this problem. Finally, a case study is undertaken to test the validity of the model and the algorithm. Four automated vehicles are designated to execute all delivery tasks required by 25 stores. Capacities of all of the automated vehicles are almost fully utilised. It is of considerable significance for the promotion of automated vehicles in last-mile situations to develop such research into real problems arising in the initial period.

scholarly journals CAPACITATED VEHICLE ROUTING PROBLEM

2019 ◽  
Vol 7 (3) ◽  
pp. 310-327
Author(s):  
Ibrahim A.A ◽  
Lo N. ◽  
Abdulaziz R.O ◽  
Ishaya J.A
Keyword(s):  
Reinforcement Learning ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Goods And Services ◽  
Capacitated Vehicle Routing Problem ◽  
Large Size ◽  
The Cost ◽  
Cost Of Transportation ◽  
Capacitated Vehicle

Cost of transportation of goods and services is an interesting topic in today’s society. The  Capacitated vehicle routing problem, which is been consider in this research, is one of the variants of the vehicle routing problem. In this research we develop a reinforcement learning technique to find optimal paths from a depot to the set of customers while also considering the capacity of the vehicles, in order to reduce the cost of transportation of goods and services. Our basic assumptions are; each vehicle originates from a depot, service the customers and return to the depot, the vehicles are homogeneous. We solve the CVRP with an exact method; column generation, goole’s operation research tool and reinforcement learning and compare their solutions. Our objective is to solve a large-size of vehicle routing problem to optimality.

An Improved Genetic Algorithm for Solving Multi Depot Vehicle Routing Problems

2021 ◽  
pp. 375-402
Author(s):  
Varimna Singh ◽  
L. Ganapathy ◽  
Ashok K. Pundir
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Carrying Capacity ◽  
Vehicle Routing Problem ◽  
Np Hard ◽  
Vehicle Routing Problems ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Hybrid Clustering ◽  
The Cost

The classical Vehicle Routing Problem (VRP) tries to minimise the cost of dispatching goods from depots to customers using vehicles with limited carrying capacity. As a generalisation of the TSP, the problem is known to be NP-hard and several authors have proposed heuristics and meta-heuristics for obtaining good solutions. The authors present genetic algorithm-based approaches for solving the problem and compare the results with available results from other papers, in particular, the hybrid clustering based genetic algorithm. The authors find that the proposed methods give encouraging results on all these instances. The approach can be extended to solve multi depot VRPs with heterogeneous fleet of vehicles.

An Improved Genetic Algorithm for Solving Multi Depot Vehicle Routing Problems

2019 ◽  
Vol 12 (4) ◽  
pp. 1-26 ◽  
Author(s):  
Varimna Singh ◽  
L. Ganapathy ◽  
Ashok K. Pundir
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Carrying Capacity ◽  
Vehicle Routing Problem ◽  
Np Hard ◽  
Vehicle Routing Problems ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Hybrid Clustering ◽  
The Cost

The classical Vehicle Routing Problem (VRP) tries to minimise the cost of dispatching goods from depots to customers using vehicles with limited carrying capacity. As a generalisation of the TSP, the problem is known to be NP-hard and several authors have proposed heuristics and meta-heuristics for obtaining good solutions. The authors present genetic algorithm-based approaches for solving the problem and compare the results with available results from other papers, in particular, the hybrid clustering based genetic algorithm. The authors find that the proposed methods give encouraging results on all these instances. The approach can be extended to solve multi depot VRPs with heterogeneous fleet of vehicles.

scholarly journals Distribusi Batik Madura Melalui Penerapan Generalized Vehicle Routing Problem (GVRP)

2017 ◽  
Vol 3 (2) ◽  
pp. 101-104
Author(s):  
Faisol Faisol ◽  
Masdukil Makruf
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Product Distribution ◽  
Cluster Method ◽  
Routing Problem ◽  
Maximum Capacity ◽  
Distribution Process ◽  
The Cost ◽  
Distribution Cost ◽  
Generalized Vehicle Routing Problem

Product distribution process is an effort to convey a product of consumer handlebar with a planned and programmed system. Cluster method is a grouping of the nearest market location, then analyzed the location of potential facilities through center of gravity. GVRP (Generalized Vehicle Routing Problem) is one of the algorithms in the cluster method [1]. In the GVRP describes the route determination to minimize the required distribution costs. GVRP is a generalization of VRP, so the point of the graph is partitioned into several sets of specific points, called clusters [2]. In this research, modification of GVRP model for multi-capacity vehicle case can determine the route and minimize the cost of distribution. Taken case on UD. Damai Asih for the distribution of Madura writes batik to 25 districts in East Java. From the results of running using MATLAB 7.8.0 obtained the efficiency of the distribution cost of 8.71% of the initial cost before doing the clustering based on distance and maximum capacity of the car of Rp. 6,969,480.00. After the filtering based on the distance and maximum capacity of the car obtained a cost of Rp. 6.365.500.00. The highest value of efficiency is obtained in cluster four, while the lowest efficiency value is obtained in cluster eight. The existence of cost efficiency is due to the different mileage in the clustering process.

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