Hybrid Metaheuristics for the Clustered Vehicle Routing Problem

The Clustered Vehicle Routing Problem (CluVRP) is a variant of the Capacitated Vehicle Routing Problem in which customers are grouped into clusters. Each cluster has to be visited once, and a vehicle entering a cluster cannot leave it until all customers have been visited. This article presents two alternative hybrid metaheuristic algorithms for the CluVRP. The first algorithm is based on an Iterated Local Search algorithm, in which only feasible solutions are explored and problem-specific local search moves are utilized. The second algorithm is a Hybrid Genetic Search, for which the shortest Hamiltonian path between each pair of vertices within each cluster should be precomputed. Using this information, a sequence of clusters can be used as a solution representation and large neighborhoods can be efficiently explored by means of bi-directional dynamic programming, sequence concatenations, by using appropriate data structures. Extensive computational experiments are performed on benchmark instances from the literature, as well as new large scale ones. Recommendations on promising algorithm choices are provided relatively to average cluster size.Comment: Working Paper, MIT -- 22 page

Disaster preparedness using risk-assessment methods from earthquake engineering

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Analyzing the uncertainties associated with disaster occurrences is critical to make effective disaster preparedness plans. In this study, we focus on pre-positioning emergency supplies for earthquake preparedness. We present a new method to compute earthquake likelihood and the number of the affected people. Our approach utilizes forecasting methods from the earthquake engineering literature, and avoids using probabilistic scenarios to represent the uncertainties related to earthquake occurrences. We validate the proposed technique by using historical earthquake data from Turkey, a country under significant earthquake risk. We also present a case study that illustrates the implementation of our method to solve the inventory allocation problem of the Turkish Red Crescen

Glider Routing and Trajectory Optimisation in disaster assessment

In this paper, we introduce the Glider Routing and Trajectory Optimisation Problem (GRTOP), the problem of finding optimal routes and trajectories for a fleet of gliders with the mission of surveying a set of locations. We propose a novel MINLP formulation for the GRTOP. In our approach, we consider the gliders' flight dynamics during the definition of the routes. In order to achieve better convergence, we linearise the gliders' dynamics and relax the dynamic constraints of our model, converting the proposed MINLP into a MISOCP. Several different discretisation techniques and solvers are compared. The formulation is tested on 180 randomly generated instances. In addition, we solve instances inspired by risk maps of flooding-prone cities across the UK

The Unmanned Aerial Vehicle Routing and Trajectory Optimisation Problem, a Taxonomic Review

Over the past few years, Unmanned Aerial Vehicles (UAVs) have become more and more popular. The complexity of routing UAVs has not been fully investigated in the literature. In this paper, we provide a formal definition of the UAV Routing and Trajectory Optimisation Problem (UAVRTOP). Next, we introduce a taxonomy and review recent contributions in UAV trajectory optimisation, UAV routing and articles addressing these problems, and their variants, simultaneously. We conclude with the identification of future research opportunities.<br/

Smart Mobility and Elderly People. Can ICT Make City More Accessible for Everybody?

The ageing population is a phenomenon whose relevance grows over time and quickly spreads in different territorial contexts. Therefore, cities will have to take into account the ageing population and define policies and strategies to improve the quality of life. For this purpose is particularly remarkable the transport sector because it allows to use the urban services and to promote an active ageing. Within the field of urban studies aimed at facing the new challenges related to social developments, including that of the ageing population, the Smart City paradigm has been spread to make cities safe, accessible and sustainable. The strategies to improve accessibility and safety of the mobility system using ICTs can have positive impacts in terms of ensuring elderly people the ability to lead an autonomous life and participate actively in society according to one’s individual needs. In this framework, the aim of the paper is to analyse how Italian cities are declining the topic of Smart Mobility, with particular attention to the use of new technologies to improve the elderly trips. The paper attempt to show that in the sample of Italian cities analysed the ICTs applied to the transport sector do not fully realize their potential; this is not due to the limited fields of application, but rather to the lack of a “system-orientated” perspective when applying innovations. The adoption of a smart approach cannot be limited to a market-induced uncritical introduction of devices or sensors, instead, it will be necessary to refine the tools for understanding the needs of specific categories of users, such as the elderly, to define integrated strategies able to operate on many aspects simultaneously

An exact algorithm for the static rebalancing problem arising in bicycle sharing systems

Bicycle sharing systems can significantly reduce traffic, pollution, and the need for parking spaces in city centers. One of the keys to success for a bicycle sharing system is the efficiency of rebalancing operations, where the number of bicycles in each station has to be restored to its target value by a truck through pickup and delivery operations. The Static Bicycle Rebalancing Problem aims to determine a minimum cost sequence of stations to be visited by a single vehicle as well as the amount of bicycles to be collected or delivered at each station. Multiple visits to a station are allowed, as well as using stations as temporary storage. This paper presents an exact algorithm for the problem and results of computational tests on benchmark instances from the literature. The computational experiments show that instances with up to 60 stations can be solved to optimality within 2 hours of computing time