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 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 COST ALLOCATION ON VEHICLE ROUTING PROBLEM

1997 ◽  
Vol 40 (4) ◽  
pp. 451-465
Author(s):  
Hiroaki Mohri ◽  
Takahiro Watanabe ◽  
Masao Mori ◽  
Mikio Kubo
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Cost Allocation ◽  
Routing Problem

A Column Generation Algorithm for a Rich Vehicle-Routing Problem

2009 ◽  
Vol 43 (1) ◽  
pp. 56-69 ◽  
Author(s):  
Alberto Ceselli ◽  
Giovanni Righini ◽  
Matteo Salani
Keyword(s):  
Vehicle Routing ◽  
Column Generation ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Generation Algorithm

Exact Branch-Price-and-Cut for a Hospital Therapist Scheduling Problem with Flexible Service Locations and Time-Dependent Location Capacity

2021 ◽  
Author(s):  
Alexander Jungwirth ◽  
Guy Desaulniers ◽  
Markus Frey ◽  
Rainer Kolisch
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Cutting Planes ◽  
Time Windows ◽  
Time Dependent ◽  
Planning Problem ◽  
Routing Problem ◽  
Number Of Patients ◽  
New Variant ◽  
Synchronization Constraints

We study a new variant of the vehicle routing problem, which arises in hospital-wide scheduling of physical therapists. Multiple service locations exist for patients, and resource synchronization for the location capacities is required as only a limited number of patients can be treated at one location at a time. Additionally, operations synchronization between treatments is required as precedence relations exist. We develop an innovative exact branch-price-and-cut algorithm including two approaches targeting the synchronization constraints (1) based on branching on time windows and (2) based on adding combinatorial Benders cuts. We optimally solve realistic hospital instances with up to 120 treatments and find that branching on time windows performs better than adding cutting planes. Summary of Contribution: We present an exact branch-price-and-cut (BPC) algorithm for the therapist scheduling and routing problem (ThSRP), a daily planning problem arising at almost every hospital. The difficulty of this problem stems from its inherent structure that features routing and scheduling while considering multiple possible service locations with time-dependent location capacities. We model the ThSRP as a vehicle routing problem with time windows and flexible delivery locations and synchronization constraints, which are properties relevant to other vehicle routing problem variants as well. In our computational study, we show that the proposed exact BPC algorithm is capable of solving realistic hospital instances and can, thus, be used by hospital planners to derive better schedules with less manual work. Moreover, we show that time window branching can be a valid alternative to cutting planes when addressing synchronization constraints in a BPC algorithm.

scholarly journals Advanced loading constraints for 3D vehicle routing problems

OR Spectrum ◽  
2021 ◽  
Author(s):  
Corinna Krebs ◽  
Jan Fabian Ehmke ◽  
Henriette Koch
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Comprehensive Evaluation ◽  
Time Windows ◽  
Complex Loading ◽  
Solution Quality ◽  
Routing Problem ◽  
Loading Constraints ◽  
New Variant ◽  
Neighbourhood Search

AbstractGiven automated order systems, detailed characteristics of items and vehicles enable the detailed planning of deliveries including more efficient and safer loading of distribution vehicles. Many vehicle routing approaches ignore complex loading constraints. This paper focuses on the comprehensive evaluation of loading constraints in the context of combined Capacitated Vehicle Routing Problem and 3D Loading (3L-CVRP) and its extension with time windows (3L-VRPTW). To the best of our knowledge, this paper considers the currently largest number of loading constraints meeting real-world requirements and reducing unnecessary loading efforts for both problem variants. We introduce an approach for the load bearing strength of items ensuring a realistic load distribution between items. Moreover, we provide a new variant for the robust stability constraint enabling better performance and higher stability. In addition, we consider axle weights of vehicles to prevent overloaded axles for the first time for the 3L-VRPTW. Additionally, the reachability of items, balanced loading and manual unloading of items are taken into account. All loading constraints are implemented in a deepest-bottom-left-fill algorithm, which is embedded in an outer adaptive large neighbourhood search tackling the Vehicle Routing Problem. A new set of 600 instances is created, published and used to evaluate all loading constraints in terms of solution quality and performance. The efficiency of the hybrid algorithm is evaluated by three well-known instance sets. We outperform the benchmarks for most instance sets from the literature. Detailed results and the implementation of loading constraints are published online.

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.

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