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.

The steadily growing popularity of grocery home-delivery services is most likely based on the convenience experienced by its customers. However, the perishable nature of the products imposes certain requirements during the delivery process. The customer must be present when the delivery arrives so that the delivery process can be completed without interrupting the cold chain. Therefore, 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 in the scientific literature. 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, the underlying optimization problem is a vehicle routing problem with a time windows. This work is concerned with a case given by an international grocery retailer’s online shopping service. We present an analysis of several efficient solution methods that can be employed to AHD services. A framework for the operational planning tools required to tackle the order placement process is provided. However, the basic framework can 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.

Nowadays, industry and individuals alike are highly dependent on a reliable power supply. A large-scale power outage, commonly known as a “blackout” is caused by natural disasters, cyber attacks, technical failure, or human errors, and can lead to a variety of severe consequences. The far-reaching dynamics of blackouts can even result in the collapse of critical public service infrastructure reliant on electricity (e.g., communication, water supply, medical services, public safety). Particularly, the loss of information and communication infrastructure essential to reporting medical emergencies, and the collapse of the drinking water supply are two critical stressors for the population to cope with. One attempt to tackle this situation is to install temporary emergency contact points (ECPs) into existing infrastructure. These can be approached by the population to communicate with medical personnel and to receive drinking water. Different types of professional personnel, which is a limited resource, are required to run such ECPs. Our study introduces this tactical decision problem. We formulate it as an integer linear program for the optimal spatial allocation of ECPs, such that multiple types of human resources that are required for operating such locations can be efficiently assigned. A comprehensive numerical study, based on data of the City of Vienna, demonstrates how to reduce the walking distance of inhabitants while increasing the efficiency of resource allocation. Matrix pruning based on an enforced limit of the walking distances together with a decomposition approach is utilized to solve the considered instances.