SavingsAnts for the Vehicle Routing Problem

Abstract. 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 show that our results are competitive

Metaheuristics for the Order Batching Problem in Manual Order Picking Systems

In manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations effciently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem; the rst one is based on Iterated Local Search, the second one on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods, but provide solutions which may allow for operating distribution warehouses signicantly more effcient.Warehouse Management, Order Picking, Order Batching, Iterated Local Search, Ant Colony Optimization

Intelligente Wechselbrückensteuerung für die Logistik von Morgen

Die Logistik ist einer der wichtigsten Zweige der Volkswirtschaft. Die effiziente Gestaltung der in sie involvierten Prozesse ist daher, gerade angesichts des zu erwartenden Rückgangs der Ölfördermenge sowie des bekanntermaßen schädlichen Einflusses von CO2 auf das Klima, von hoher Wichtigkeit. Dennoch gibt es auf diesem Bereich viele bisher nur unzureichend gelöste Probleme. In diesem Beitrag wird das INWEST-System vorgestellt, das sowohl eine praktische Transportplanung als auch ein Informationssystem, welche alle an der Transportkette beteiligte Nutzer und Komponenten verbindet, zur Verfügung stellt

Metaheuristics for the Order Batching Problem in Manual Order Picking Systems

In manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations effciently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem; the rst one is based on Iterated Local Search, the second one on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods, but provide solutions which may allow for operating distribution warehouses signi cantly more effcient

A Guided Neighborhood Search Applied to the Split Delivery Vehicle Routing Problem

The classic vehicle routing problem considers the distribution of goods to geographically scattered customers from a central depot using a homogeneous fleet of vehicles with finite capacity. Each customer has a known demand and can be visited by exactly one vehicle. Each vehicle services the assigned customers in such a way that all customers are fully supplied and the total service does not exceed the vehicle capacity. In the split delivery vehicle routing problem, a customer can be visited by more than one vehicle, i.e., a customer demand can be split between various vehicles. Allowing split deliveries has been proven to potentially reduce the operational costs of the fleet. This study efficiently solves the split delivery vehicle routing problem using three new approaches. In the first approach, the problem is solved in two stages. During the first stage, an initial solution is found by means of a greedy approach that can produce high quality solutions comparable to those obtained with existing sophisticated approaches. The greedy approach is based on a novel concept called the route angle control measure that helps to produce spatially thin routes and avoids crossing routes. In the second stage, this constructive approach is extended to an iterative approach using adaptive memory concepts, and then a variable neighborhood descent process is added to improve the solution obtained. A new solution diversification scheme is presented in the second approach based on concentric rings centered at the depot that partitions the original problem. The resulting sub-problems are then solved using the greedy approach with route angle control measures. Different ring settings produce varied partitions and thus different solutions to the original problem are obtained and improved via a variable neighborhood descent. The third approach is a learning procedure based on a set or population of solutions. Those solutions are used to find attractive attributes and construct new solutions within a tabu search framework. As the search progresses, the existing population evolves, better solutions are included in it whereas bad solutions are removed from it. The initial set is constructed using the greedy approach with the route angle control measure whereas new solutions are created using an adaptation of the well known savings algorithm of Clarke and Wright (1964) and improved by means of an enhanced version of the variable neighborhood descent process. The proposed approaches are tested on benchmark instances and results are compared with existing implementations

Solution Biases and Pheromone Representation Selection in Ant Colony Optimisation.

Combinatorial optimisation problems (COPs) pervade human society: scheduling, design, layout, distribution, timetabling, resource allocation and project management all feature problems where the solution is some combination of elements, the overall value of which needs to be either maximised or minimised (i.e., optimised), typically subject to a number of constraints. Thus, techniques to efficiently solve such problems are an important area of research. A popular group of optimisation algorithms are the metaheuristics, approaches that specify how to search the space of solutions in a problem independent way so that high quality solutions are likely to result in a reasonable amount of computational time. Although metaheuristic algorithms are specified in a problem independent manner, they must be tailored to suit each particular problem to which they are applied. This thesis investigates a number of aspects of the application of the relatively new Ant Colony Optimisation (ACO) metaheuristic to different COPs. The standard ACO metaheuristic is a constructive algorithm loosely based on the foraging behaviour of ant colonies, which are able to find the shortest path to a food source by indirect communication through pheromones. ACO’s artificial pheromone represents a model of the solution components that its artificial ants use to construct solutions. Developing an appropriate pheromone representation is a key aspect of the application of ACO to a problem. An examination of existing ACO applications and the constructive approach more generally reveals how the metaheuristic can be applied more systematically across a range of COPs. The two main issues addressed in this thesis are biases inherent in the constructive process and the systematic selection of pheromone representations. The systematisation of ACO should lead to more consistently high performance of the algorithm across different problems. Additionally, it supports the creation of a generalised ACO system, capable of adapting itself to suit many different combinatorial problems without the need for manual intervention

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 show that our results are competitive.Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science