Optimal allocation of defibrillator drones in mountainous regions

Responding to emergencies in Alpine terrain is quite challenging as air ambulances and mountain rescue services are often confronted with logistics challenges and adverse weather conditions that extend the response times required to provide life-saving support. Among other medical emergencies, sudden cardiac arrest (SCA) is the most time-sensitive event that requires the quick provision of medical treatment including cardiopulmonary resuscitation and electric shocks by automated external defibrillators (AED). An emerging technology called unmanned aerial vehicles (or drones) is regarded to support mountain rescuers in overcoming the time criticality of these emergencies by reducing the time span between SCA and early defibrillation. A drone that is equipped with a portable AED can fly from a base station to the patient's site where a bystander receives it and starts treatment. This paper considers such a response system and proposes an integer linear program to determine the optimal allocation of drone base stations in a given geographical region. In detail, the developed model follows the objectives to minimize the number of used drones and to minimize the average travel times of defibrillator drones responding to SCA patients. In an example of application, under consideration of historical helicopter response times, the authors test the developed model and demonstrate the capability of drones to speed up the delivery of AEDs to SCA patients. Results indicate that time spans between SCA and early defibrillation can be reduced by the optimal allocation of drone base stations in a given geographical region, thus increasing the survival rate of SCA patients

Computational Approaches for Grocery Home Delivery Services

Grocery home delivery services require customers to be present when their deliveries arrive. Hence, the grocery retailer and the customer must mutually agree on a time window during which the delivery can be guaranteed. This concept is referred to as the Attended Home Delivery (AHD) problem. The phase during which customers place orders, usually through a web service, constitutes the computationally most challenging part of the logistical processes behind such services. The system must determine potential delivery time windows that can be offered to incoming customers and incrementally build the delivery schedule as new orders are placed. Typically, a Vehicle Routing Problem with Time Windows forms the underlying optimization problem. This work is concerned with the use case given by an international grocery retailer's online shopping service. We present an analysis of efficient solution methods that can be employed to AHD services. We provide several heuristic approaches for tackling the steps mentioned above. However, the basic framework can be easily be adapted to be used for many similar Vehicle Routing applications. We provide a comprehensive computational study comparing several algorithmic strategies, combining heuristics utilizing Local Search operations and Mixed-Integer Linear Programs, tackling the booking process. Finally, we analyze the scalability and suitability of the approaches

Lower bounds for the bandwidth problem

The Bandwidth Problem seeks for a simultaneous permutation of the rows and columns of the adjacency matrix of a graph such that all nonzero entries are as close as possible to the main diagonal. This work focuses on investigating novel approaches to obtain lower bounds for the bandwidth problem. In particular, we use vertex partitions to bound the bandwidth of a graph. Our approach contains prior approaches for bounding the bandwidth as special cases. By varying sizes of partitions, we achieve a trade-off between quality of bounds and efficiency of computing them. To compute lower bounds, we derive a Semidefinite Programming relaxation. We evaluate the performance of our approach on several data sets, including real-world instances.Comment: 4 figure

Exact and heuristic approaches for a new circular layout problem

Abstract We discuss a new facility layout problem, the so-called Directed Circular Facility Layout Problem (DCFLP). The DCFLP aims to find an optimal arrangement of machines on a circular material handling system such that the total weighted sum of the center-to-center distances between all pairs of machines measured in clockwise direction is minimized. Several real-world applications, like for example the optimal arrangement of a set of cutting tools on a tool turret, can be modeled as a DCFLP. Further, the DCFLP generalizes a couple of layout problems that are well-discussed in literature. We show that the DCFLP can be modeled as a Linear Ordering Problem (LOP). Hence, it can be solved efficiently by using exact and heuristic approaches for the LOP. First, we apply a Semidefinite Programming as well as an Integer Linear Programming approach. Moreover, we use a Tabu Search and a Variable Neighborhood Search heuristic, for solving the DCFLP. Finally, we compare the practical performance of our approaches in a computational study