Experiments on the Node, Edge, and Arc Routing Problem

-The Node, Edge, and Arc Routing Problem (NEARP) was defined by Prins and Bouchenoua in 2004 along with the first benchmark called CBMix. The NEARP generalizes the classical Capacitated Vehicle Routing Problem (CVRP), the Capacitated Arc Routing Problem (CARP), and the General Routing Problem. It is also denoted the Mixed Capacitated General Routing Problem (MCGRP). The NEARP removes the strict and unwarranted dichotomy that previously existed in the literature between arc routing and node routing. In real applications, there are many cases where the pure node or arc routing models are not adequate. In fundamentally node-based routing applications such as newspaper delivery and communal waste management that have typically been modeled as arc routing problems in the literature, the number of points is often so large that demand aggregation is necessary. Aggregation heuristics will normally give a NEARP instance, possibly with side constraints. Hence, the NEARP is a scientifically challenging problem with high industrial relevance. In this report we present experiments with Spider, SINTEF’s industrial VRP solver, on the three NEARP benchmarks that have been published so far: CBMix, BHW, and DI-NEARP. Bach, Hasle, and Wøhlk have developed a combinatorial lower bound for the NEARP and defined the two latter benchmarks. Here, we present an experimental study with Spider on the three existing NEARP benchmarks. Upper and lower bounds are given for all instances. Three of the BHW instances have been solved to optimality. SINTEF has developed a web page for NEARP results on http://www.sintef.no/NEARP

Scheduling Vehicles with Spatial Conflicts

When scheduling the movement of individual vehicles on a traffic network, one must ensure that they never get too close to one another. This is normally modelled by segmenting the network and forbidding two vehicles to occupy the same segment at the same time. This approximation is often insufficient or too restraining. This study develops and systematises the use of conflict regions to model spatial proximity constraints. By extending the classical disjunctive programming approach to job-shop scheduling problems, we demonstrate how conflict regions can be exploited to efficiently schedule the collective movements of a set of vehicles, in this case aircraft moving on an airport ground network. We also show how conflict regions can be used in the short-term control of vehicle speeds to avoid collisions and deadlocks. The overall approach was implemented in a software system for air traffic management at airports and successfully evaluated for scheduling and guiding airplanes during an extensive human in the loop simulation exercise for the Budapest airport. Through simulations, we also provide numerical results to assess the computational efficiency of our scheduling algorithm.acceptedVersio

A Solution to the Angel Problem

Abstract. We solve the Angel Problem, by describing a strategy that guarantees an Angel of power 2 or greater to win. Basically, the Angel should move north as quickly as possible. However, he should detour around eaten squares, as long as the extra distance does not exceed twice the number of eaten squares evaded. We show that an Angel following this strategy will always spot a trap early enough to avoid it

Using Heterogeneous Computing for Solving Vehicle Routing Problems

In the talk, we briefly explain modern PC architectures and the general principles of heterogeneous computing. We illustrate how multi-core and GPU computing may be utilized for higher performance and more robust VRP solvers, and explain the details of our solution method for the DVRP. We present the results of computational experiments on standard CVRP/DVRP benchmarks from the literature as well as industrial test instances from newspaper distribution. Perspectives and directions for future work are given.Using Heterogeneous Computing for Solving Vehicle Routing Problem

A Capacitated Clustering-based Method for Newspaper Delivery Routing

We present an efficient solver that produces clustered, balanced, and cost effective routes for distribution in a given geographical area. Through cloud computing, the optimization functionality is used by more than 30 Nordic newspaper distribution companies for solving Large-scale Node Edge Arc Routing Problems (NEARP) with route duration, route balancing, and route compactness constraints. First, we solve a capacitated clustering problem. The corresponding NEARP solution is further optimized through a combination of Iterated Local Search, Variable Neighborhood Search, and Large Neighborhood Search.A Capacitated Clustering-based Method for Newspaper Delivery Routin

Experiments on the Node, Edge, and Arc Routing Problem

-The Node, Edge, and Arc Routing Problem (NEARP) was defined by Prins and Bouchenoua in 2004 along with the first benchmark called CBMix. The NEARP generalizes the classical Capacitated Vehicle Routing Problem (CVRP), the Capacitated Arc Routing Problem (CARP), and the General Routing Problem. It is also denoted the Mixed Capacitated General Routing Problem (MCGRP). The NEARP removes the strict and unwarranted dichotomy that previously existed in the literature between arc routing and node routing. In real applications, there are many cases where the pure node or arc routing models are not adequate. In fundamentally node-based routing applications such as newspaper delivery and communal waste management that have typically been modeled as arc routing problems in the literature, the number of points is often so large that demand aggregation is necessary. Aggregation heuristics will normally give a NEARP instance, possibly with side constraints. Hence, the NEARP is a scientifically challenging problem with high industrial relevance. In this report we present experiments with Spider, SINTEF’s industrial VRP solver, on the three NEARP benchmarks that have been published so far: CBMix, BHW, and DI-NEARP. Bach, Hasle, and Wøhlk have developed a combinatorial lower bound for the NEARP and defined the two latter benchmarks. Here, we present an experimental study with Spider on the three existing NEARP benchmarks. Upper and lower bounds are given for all instances. Three of the BHW instances have been solved to optimality. SINTEF has developed a web page for NEARP results on http://www.sintef.no/NEARP

The Node Edge Arc Routing Problem - applications and heuristics

In the VRP literature, the Arc Routing Problem is often advocated as an adequate model for routing applications such as newspaper delivery and garbage collection. We argue that a better model is the Node Edge Arc Routing Problem (NEARP), combining demand on nodes, edges, and arcs in a transportation network. We describe how we have extended a node based VRP solver to enable modeling and resolution of the NEARP, and network based heuristics for aggregating node-based demand into demands on arcs and edges. Experimental results on standard benchmarks and industrial cases are presented.The Node Edge Arc Routing Problem - applications and heuristic

Solving Node Edge Arc Routing Problems in the Distribution of Media Products

There is a strong pressure on economy in the distribution of media products. Two important remedies are more efficient carrier routes and distribution of side products. Both call for effective and dynamic route design and revision processes. These processes are complex, time-consuming, and costly. The size of industrial carrier route planning instances may cause performance problems for VRP algorithms. In the VRP literature, the Capacitated Arc Routing Problem (CARP) is often advocated as an adequate model for applications such as newspaper delivery and garbage collection. We argue that a better model is the Node Edge Arc Routing Problem (NEARP). We describe how we have extended a VRP solver to enable modeling of the NEARP, and extended it with a framework for multi-level aggregation of demand. An aggregation heuristic that is based on the underlying road topology is presented. The resulting solver has been integrated in a commercial web based system for route management and tested by pilot users. We present experimental results on real-life data from newspaper distribution. Results on standard CARP and NEARP instances from the literature are given, including several new best known solutions.Solving Node Edge Arc Routing Problems in the Distribution of Media Product

Solving Node Edge Arc Routing Problems in the Distribution of Media Products

There is a strong pressure on economy in the distribution of media products. Two important remedies are more efficient carrier routes and distribution of side products. Both call for effective and dynamic route design and revision processes. These processes are complex, time-consuming, and costly. The size of industrial carrier route planning instances may cause performance problems for VRP algorithms. In the VRP literature, the Capacitated Arc Routing Problem (CARP) is often advocated as an adequate model for applications such as newspaper delivery and garbage collection. We argue that a better model is the Node Edge Arc Routing Problem (NEARP). We describe how we have extended a VRP solver to enable modeling of the NEARP, and extended it with a framework for multi-level aggregation of demand. An aggregation heuristic that is based on the underlying road topology is presented. The resulting solver has been integrated in a commercial web based system for route management and tested by pilot users. We present experimental results on real-life data from newspaper distribution. Results on standard CARP and NEARP instances from the literature are given, including several new best known solutions.Solving Node Edge Arc Routing Problems in the Distribution of Media Product