Solution of a practical Vehicle Routing Problem for monitoring Water Distribution Networks

In this work, we introduce a generalisation of the Vehicle Routing Problem for a specific application in the monitoring of a Water Distribution Network (WDN). In this problem, multiple technicians must visit a sequence of nodes in the WDN and perform a series of tests to check the quality of water. Some special nodes (i.e., wells) require technicians to first collect a key from a key centre. The key must then be returned to the same key centre after the test has been performed, thus introducing precedence constraints and multiple visits in the routes. To solve the problem, a Mixed Integer Linear Programming model and an Iterated Local Search have been implemented. The efficiency of the proposed methods is demonstrated by means of extensive computational tests on randomly created and real-world instances

Vehicle Routing Problems with Fuel Consumption and Stochastic Travel Speeds

Conventional vehicle routing problems (VRP) always assume that the vehicle travel speed is fixed or time-dependent on arcs. However, due to the uncertainty of weather, traffic conditions, and other random factors, it is not appropriate to set travel speeds to fixed constants in advance. Consequently, we propose a mathematic model for calculating expected fuel consumption and fixed vehicle cost where average speed is assumed to obey normal distribution on each arc which is more realistic than the existing model. For small-scaled problems, we make a linear transformation and solve them by existing solver CPLEX, while, for large-scaled problems, an improved simulated annealing (ISA) algorithm is constructed. Finally, instances from real road networks of England are performed with the ISA algorithm. Computational results show that our ISA algorithm performs well in a reasonable amount of time. We also find that when taking stochastic speeds into consideration, the fuel consumption is always larger than that with fixed speed model

A hybrid metaheuristic algorithm for the multi-depot covering tour vehicle routing problem

We propose a generalization of themulti-depot capacitated vehicle routing problem where the assumption of visiting each customer does not hold. In this problem, called the Multi-Depot Covering Tour Vehicle Routing Problem (MDCTVRP), the demand of each customer could be satisfied in two different ways: either by visiting the customer along the tour or by "covering" it. When a customer is visited, the corresponding demand is delivered at its location. A customer is instead covered when it is located within an acceptable distance from at least one visited customer from which it can receive its demand. For this problem we develop two mixed integer programming formulations and a hybrid metaheuristic combining GRASP, iterated local search and simulated annealing. Extensive computational tests on this problem and some of its variants clearly indicate the effectiveness of the developed solution methods

Emergency rapid mapping with drones: models and solution approaches for offline and online mission planning

Die Verfügbarkeit von unbemannten Luftfahrzeugen (unmanned aerial vehicles oder UAVs) und die Fortschritte in der Entwicklung leichtgewichtiger Sensorik eröffnen neue Möglichkeiten für den Einsatz von Fernerkundungstechnologien zur Schnellerkundung in Großschadenslagen. Hier ermöglichen sie es beispielsweise nach Großbränden, Einsatzkräften in kurzer Zeit ein erstes Lagebild zur Verfügung zu stellen. Die begrenzte Flugdauer der UAVs wie auch der Bedarf der Einsatzkräfte nach einer schnellen Ersteinschätzung bedeuten jedoch, dass die betroffenen Gebiete nur stichprobenartig überprüft werden können. In Kombination mit Interpolationsverfahren ermöglichen diese Stichproben anschließend eine Abschätzung der Verteilung von Gefahrstoffen. Die vorliegende Arbeit befasst sich mit dem Problem der Planung von UAV-Missionen, die den Informationsgewinn im Notfalleinsatz maximieren. Das Problem wird dabei sowohl in der Offline-Variante, die Missionen vor Abflug bestimmt, als auch in der Online-Variante, bei der die Pläne während des Fluges der UAVs aktualisiert werden, untersucht. Das übergreifende Ziel ist die Konzeption effizienter Modelle und Verfahren, die Informationen über die räumliche Korrelation im beobachteten Gebiet nutzen, um in zeitkritischen Situationen Lösungen von hoher Vorhersagegüte zu bestimmen. In der Offline-Planung wird das generalized correlated team orienteering problem eingeführt und eine zweistufige Heuristik zur schnellen Bestimmung explorativer UAV-Missionen vorgeschlagen. In einer umfangreichen Studie wird die Leistungsfähigkeit und Konkurrenzfähigkeit der Heuristik hinsichtlich Rechenzeit und Lösungsqualität bestätigt. Anhand von in dieser Arbeit neu eingeführten Benchmarkinstanzen wird der höhere Informationsgewinn der vorgeschlagenen Modelle im Vergleich zu verwandten Konzepten aufgezeigt. Im Bereich der Online-Planung wird die Kombination von lernenden Verfahren zur Modellierung der Schadstoffe mit Planungsverfahren, die dieses Wissen nutzen, um Missionen zu verbessern, untersucht. Hierzu wird eine breite Spanne von Lösungsverfahren aus unterschiedlichen Disziplinen klassifiziert und um neue effiziente Modellierungsvarianten für die Schnellerkundung ergänzt. Die Untersuchung im Rahmen einer ereignisdiskreten Simulation zeigt, dass vergleichsweise einfache Approximationen räumlicher Zusammenhänge in sehr kurzer Zeit Lösungen hoher Qualität ermöglichen. Darüber hinaus wird die höhere Robustheit genauerer, aber aufwändigerer Modelle und Lösungskonzepte demonstriert

Métodos heurísticos para un problema multicriterio de distribución de ayuda humanitaria

Large scale disasters, such as the one caused by the Typhoon Haiyan, which devastated portions of the Philippines in 2013, or the catastrophic 2010 Haiti earthquake, which caused major damage in Port-au-Prince and other settlements in the region, have massive and lasting effects on populations. Nowadays, disasters can be considered as a consequence of inappropriately managed risk. These risks are the product of hazards and vulnerability, which refers to the extent to which a community can be affected by the impact of a hazard. In this way, developing countries, due to their greater vulnerability, suffer the highest costs when a disaster occurs. Disaster relief is a challenge for politics, economies, and societies worldwide. Humanitarian organizations face multiple decision problems when responding to disasters. In particular, once a disaster strikes, the distribution of humanitarian aid to the population affected is one of the most fundamental operations in what is called humanitarian logistics. This term is defined as the process of planning, implementing and controlling the effcient, cost-effective ow and storage of goods and materials as well as related information, from the point of origin to the point of consumption, for the purpose of meeting the end bene- ciaries' requirements and alleviate the suffering of vulnerable people, [the Humanitarian Logistics Conference, 2004 (Fritz Institute)]. During the last decade there has been an increasing interest in the OR/MS community in studying this topic, pointing out the similarities and differences between humanitarian and business logistics, and developing models suited to handle the special characteristics of these problems. Several authors have pointed out that traditional logistic objectives, such as minimizing operation cost, are not the most relevant goals in humanitarian operations. Other factors, such as the time of operation, or the design of safe and equitable distribution plans, come to the front, and new models and algorithms are needed to cope with these special features. Up to six attributes related to the distribution plan are considered in our multi-criteria approach. Even though there are usually simple ways to measure the cost of an operation, the evaluation of some other attributes such as security or equity is not easy. As a result, several attribute measures are proposed and developed, focusing on different aspects of the solutions. Furthermore, when metaheuristic solution methods are used, considering non linear objective functions does not increase the complexity of the algorithms significantly, and thus more accurate measures can be utilized..