Marc Reimann

D-Ants: savings based ants divide and conquer the vehicle routing problem

This paper presents an algorithm that builds on the Savings based Ant System presented in [Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), Morgan Kaufmann, San Francisco, 2002] and enhances its performance in terms of computational effort. This is achieved by decomposing the problem and solving only the much smaller subproblems resulting from the decomposition.The computational study and statistical analysis conducted both on standard benchmark problem instances as well as on new large scale Vehicle Routing Problem instances will show that the approach does not only improve the efficiency, but also improves the effectiveness of the algorithm leading to a fast and powerful problem solving tool for real world sized Vehicle Routing Problems.

A Variable Neighborhood Search for the Multi Depot Vehicle Routing Problem with Time Windows

The aim of this paper is to propose an algorithm based on the philosophy of the Variable Neighborhood Search (VNS) to solve Multi Depot Vehicle Routing Problems with Time Windows. The paper has two main contributions. First, from a technical point of view, it presents the first application of a VNS for this problem and several design issues of VNS algorithms are discussed. Second, from a problem oriented point of view the computational results show that the approach is competitive with an existing Tabu Search algorithm with respect to both solution quality and computation times.

New savings based algorithms for time constrained pickup and delivery of full truckloads

Abstract In this paper we deal with the pickup and delivery of full truckloads under time window constraints. Our objective function is to minimize empty vehicle movements, as these use resources without directly adding value to the products transported. We first give an exact formulation of the problem. After that we present a relaxed problem formulation based on network flows, which can be used to calculate a lower bound to the solution value. Furthermore, we propose four different savings based heuristics for the problem. Our results show that these heuristics find very good solutions quickly. We also perform a sensitivity analysis by studying the impact of time window tightness on the performance of our heuristics.

Insertion Based Ants for Vehicle Routing Problems with Backhauls and Time Windows

In this paper we present and analyze the application of an Ant System to the Vehicle Routing Problem with Backhauls and Time Windows (VRPBTW). At the core of the algorithm we use an Insertion procedure to construct solutions. We provide results on the learning and runtime behavior of the algorithm as well as a comparison with a custom made heuristic for the problem.

Exact and heuristic algorithms for the vehicle routing problem with multiple interdependent time windows

In this paper a model and several solution procedures for a novel type of vehicle routing problems where time windows for the pickup of perishable goods depend on the dispatching policy used in the solution process are presented. This problem is referred to as Vehicle Routing Problem with multiple interdependent time windows (VRPmiTW) and is motivated by a project carried out with the Austrian Red Cross blood program to assist their logistics department. Several variants of a heuristic constructive procedure as well as a branch-and-bound based algorithm for this problem were developed and implemented. Besides finding the expected reduction in costs when compared with the current procedures of the Austrian Red Cross, the results show that the heuristic algorithms find solutions reasonably close to the optimum in fractions of a second. Another important finding is that increasing the number of pickups at selected customers beyond the theoretical minimum number of pickups yields significantly greater potential for cost reductions.

Comparing backhauling strategies in vehicle routing using Ant Colony Optimization

In the Vehicle Routing Problem with Backhauls and Time Windows (VRPBTW) customers either receive goods from the depot or send goods to the depot and pickup or delivery at a customer has to occur within a pre-specified time window. The main objective is to minimize the total required fleet size for serving all customers. Secondary objectives are to minimize the total distance travelled or to minimize the total route duration of all vehicles. In this paper we consider a variant of the mixed VRPBTW where backhauls may be served before linehauls on any given route. Besides the modelling aspect of this variant we will study its performance implications when compared to the standard VRPBTW using a heuristic algorithm based on Ant Colony Optimization.

Vehicle routing with multiple deliverymen: Modeling and heuristic approaches for the VRPTW

In real life distribution of goods, relatively long service times may make it difficult to serve all requests during regular working hours. These difficulties are even greater if the beginning of the service in each demand site must occur within a time window and violations of routing time restrictions are particularly undesirable. We address this situation by considering a variant of the vehicle routing problem with time windows for which, besides routing and scheduling decisions, a number of extra deliverymen can be assigned to each route in order to reduce service times. This problem appears, for example, in the distribution of beverage and tobacco in highly dense Brazilian urban areas. We present a mathematical programming formulation for the problem, as well as a tabu search and an ant colony optimization heuristics for obtaining minimum cost routes. The performance of the model and the heuristic approaches are evaluated using instances generated from a set of classic examples from the literature.

SavingsAnts for the Vehicle Routing Problem

In this paper we propose a hybrid approach for solving vehicle routing problems. The main idea is to combine an Ant System (AS) with a problem specific constructive heuristic, namely the well known Savings algorithm. This differs from previous approaches, where the subordinate heuristic was the Nearest Neighbor algorithm initially proposed for the TSP. We compare our approach with some other classic, powerful meta-heuristics and showthat our results are competitive.

Analyzing a unified ant system for the VRP and some of its variants

In this paper we analyze the application of an Ant System to different vehicle routing problems. More specifically, we study the robustness of our Unified Ant System by evaluating its performance on four different problem classes within the domain of vehicle routing.

Cooperative Ant Colonies for Optimizing Resource Allocation in Transportation

In this paper we propose an ACO approach, where two colonies of ants aim to optimize total costs in a transportation network. This main objective consists of two sub goals, namely fleet size minimization and minimization of the vehicle movement costs, which are conflicting for some regions of the solution space. Thus, our two ant colonies optimize one of these sub-goals each and communicate information concerning solution quality. Our results show the potential of the proposed method.