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

Security, privacy and safety evaluation of dynamic and static fleets of drones

Inter-connected objects, either via public or private networks are the near future of modern societies. Such inter-connected objects are referred to as Internet-of-Things (IoT) and/or Cyber-Physical Systems (CPS). One example of such a system is based on Unmanned Aerial Vehicles (UAVs). The fleet of such vehicles are prophesied to take on multiple roles involving mundane to high-sensitive, such as, prompt pizza or shopping deliveries to your homes to battlefield deployment for reconnaissance and combat missions. Drones, as we refer to UAVs in this paper, either can operate individually (solo missions) or part of a fleet (group missions), with and without constant connection with the base station. The base station acts as the command centre to manage the activities of the drones. However, an independent, localised and effective fleet control is required, potentially based on swarm intelligence, for the reasons: 1) increase in the number of drone fleets, 2) number of drones in a fleet might be multiple of tens, 3) time-criticality in making decisions by such fleets in the wild, 4) potential communication congestions/lag, and 5) in some cases working in challenging terrains that hinders or mandates-limited communication with control centre (i.e., operations spanning long period of times or military usage of such fleets in enemy territory). This self-ware, mission-focused and independent fleet of drones that potential utilises swarm intelligence for a) air-traffic and/or flight control management, b) obstacle avoidance, c) self-preservation while maintaining the mission criteria, d) collaboration with other fleets in the wild (autonomously) and e) assuring the security, privacy and safety of physical (drones itself) and virtual (data, software) assets. In this paper, we investigate the challenges faced by fleet of drones and propose a potential course of action on how to overcome them.Comment: 12 Pages, 7 Figures, Conference, The 36th IEEE/AIAA Digital Avionics Systems Conference (DASC'17

개미알고리즘을 이용한 드론의 제설 경로 최적화

학위논문(석사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2022.2. 김동규.Drones can overcome the limitation of ground vehicles by replacing the congestion time and allowing rapid service. For sudden snowfall with climate change, a quickly deployed drone can be a flexible alternative considering the deadhead route and the labor costs. The goal of this study is to optimize a drone arc routing problem (D-ARP), servicing the required roads for snow removal. A D-ARP creates computational burden especially in large network. The D-ARP has a large search space due to its exponentially increased candidate route, arc direction decision, and continuous arc space. To reduce the search space, we developed the auxiliary transformation method in ACO algorithm and adopted the random walk method. The contribution of the work is introducing a new problem and optimization approach of D-ARP in snow removal operation and reduce its search space. The optimization results confirmed that the drone travels shorter distance compared to the truck with a reduction of 5% to 22%. Furthermore, even under the length constraint model, the drone shows 4% reduction compared to the truck. The result of the test sets demonstrated that the adopted heuristic algorithm performs well in the large size networks in reasonable time. Based on the results, introducing a drone in snow removal is expected to save the operation cost in practical terms.드론은 혼잡시간대를 대체하고 빠른 서비스를 가능하게 함으로써 지상차량의 한계를 극복할 수 있다. 최근 기후변화에 따른 갑작스런 강설의 경우에, 드론과 같이 빠르게 투입할 수 있는 서비스는 운행 경로와 노동비용을 고려했을 때도 유연한 운영 옵션이 될 수 있다. 본 연구의 목적은 드론 아크 라우팅(D-ARP)을 최적화하는 것이며, 이는 제설에 필요한 도로를 서비스하는 경로를 탐색하는 것이다. 드론 아크 라우팅은 특히 큰 네트워크에서 컴퓨터 부하를 생성한다. 다시 말해D-ARP는 큰 검색공간을 필요로 하며, 이는 기하급수적으로 증가하는 후보 경로 및 호의 방향 결정 그리고 연속적인 호의 공간으로부터 기인한다. 검색공간을 줄이기 위해, 우리는 개미알고리즘에 보조변환방법을 적용하는 방안을 도입하였으며 또한 랜덤워크 기법을 채택하였다. 본 연구의 기여는 제설 운영에 있어 D-ARP라는 새로운 문제를 설정하고 최적화 접근법을 도입하였으며 검색공간을 최소화한 것이다. 최적화 결과, 드론은 지상트럭에 비해 약 5% ~ 22%의 경로 비용 감소를 보였다. 나아가 길이 제약 모델에서도 드론은 4%의 비용 감소를 보였다. 또한 실험결과는 적용한 휴리스틱 알고리즘이 큰 네트워크에서도 합리적 시간 내에 최적해를 찾음을 입증하였다. 이러한 결과를 바탕으로, 드론을 제설에 도입하는 것은 미래에 제설 운영 비용을 실질적으로 감소시킬 것으로 기대된다.Chapter 1. Introduction 4 1.1. Study Background 4 1.2. Purpose of Research 6 Chapter 2. Literature Review 7 2.1. Drone Arc Routing problem 7 2.2. Snow Removal Routing Problem 8 2.3. The Classic ARPs and Algorithms 9 2.4. Large Search Space and Arc direction 11 Chapter 3. Method 13 3.1. Problem Statement 13 3.2. Formulation 16 Chapter 4. Algorithm 17 4.1. Overview 17 4.2. Auxilary Transformation Method 18 4.3. Ant Colony Optimization (ACO) 20 4.4. Post Process for Arc Direction Decision 23 4.5. Length Constraint and Random Walk 24 Chapter 5. Results 27 5.1. Application in Toy Network 27 5.2. Application in Real-world Networks 29 5.3. Application of the Refill Constraint in Seoul 31 Chapter 6. Conclusion 34 References 35 Acknowledgment 40석

A Patient Risk Minimization Model for Post-Disaster Medical Delivery Using Unmanned Aircraft Systems

The purpose of this research was to develop a novel routing model for delivery of medical supplies using unmanned aircraft systems, improving existing vehicle routing models by using patient risk as the primary minimization variable. The vehicle routing problem is a subset of operational research that utilizes mathematical models to identify the most efficient route between sets of points. Routing studies using unmanned aircraft systems frequently minimize time, distance, or cost as the primary objective and are powerful decision-making tools for routine delivery operations. However, the fields of emergency triage and disaster response are focused on identifying patient injury severity and providing the necessary care. This study addresses the misalignment of priorities between existing routing models and the emergency response industry by developing an optimization model with injury severity to measure patient risk. Model inputs for this study include vehicle performance variables, environmental variables, and patient injury variables. These inputs are used to construct a multi-objective mixed-integer nonlinear programming (MOMINLP) optimization model with the primary objective of minimizing total risk for a set of patients. The model includes a secondary aim of route time minimization to ensure optimal fleet deployment but is constrained by the risk minimization value identified in the first objective. This multi-objective design ensures risk minimization will not be sacrificed for route efficiency while still ensuring routes are completed as expeditiously as possible. The theoretical foundation for quantifying patient risk is based on mass casualty triage decision-making systems, specifically the emergency severity index, which focuses on sorting patients into categories based on the type of injury and risk of deterioration if additional assistance is not provided. Each level of the Emergency Severity Index is assigned a numerical value, allowing the model to search for a route that prioritizes injury criticality, subject to the appropriate vehicle and environmental constraints. An initial solution was obtained using stochastic patient data and historical environmental data validated by a Monte Carlo simulation, followed by a sensitivity analysis to evaluate the generalizability and reliability of the model. Multiple what-if scenarios were built to conduct the sensitivity analysis. Each scenario contained a different set of variables to demonstrate model generalizability for various vehicle limitations, environmental conditions, and different scales of disaster response. The primary contribution of this study is a flexible and generalizable optimization model that disaster planning organizations can use to simulate potential response capabilities with unmanned aircraft. The model also improves upon existing optimization tools by including environmental variables and patient risk inputs, ensuring the optimal solution is useful as a real-time disaster response tool

Hybrid Vehicle-drone Routing Problem For Pick-up And Delivery Services Mathematical Formulation And Solution Methodology

The fast growth of online retail and associated increasing demand for same-day delivery have pushed online retail and delivery companies to develop new paradigms to provide faster, cheaper, and greener delivery services. Considering drones’ recent technological advancements over the past decade, they are increasingly ready to replace conventional truck-based delivery services, especially for the last mile of the trip. Drones have significantly improved in terms of their travel ranges, load-carrying capacity, positioning accuracy, durability, and battery charging rates. Substituting delivery vehicles with drones could result in $50M of annual cost savings for major U.S. service providers. The first objective of this research is to develop a mathematical formulation and efficient solution methodology for the hybrid vehicle-drone routing problem (HVDRP) for pick-up and delivery services. The problem is formulated as a mixed-integer program, which minimizes the vehicle and drone routing cost to serve all customers. The formulation captures the vehicle-drone routing interactions during the drone dispatching and collection processes and accounts for drone operation constraints related to flight range and load carrying capacity limitations. A novel solution methodology is developed which extends the classic Clarke and Wright algorithm to solve the HVDRP. The performance of the developed heuristic is benchmarked against two other heuristics, namely, the vehicle-driven routing heuristic and the drone-driven routing heuristic. Anticipating the potential risk of using drones for delivery services, aviation authorities in the U.S. and abroad have mandated necessary regulatory rules to ensure safe operations. The U.S. Federal Aviation Administration (FAA) is examining the feasibility of drone flights in restricted airspace for product delivery, requiring drones to fly at or below 400-feet and to stay within the pilot’s line of sight (LS). Therefore, a second objective of this research is considered to develop a modeling framework for the integrated vehicle-drone routing problem for pick-up and delivery services considering the regulatory rule requiring all drone flights to stay within the pilot’s line of sight (LS). A mixed integer program (MIP) and an efficient solution methodology were developed for the problem. The solution determines the optimal vehicle and drone routes to serve all customers without violating the LS rule such that the total routing cost of the integrated system is minimized. Two different heuristics are developed to solve the problem, which extends the Clarke and Wright Algorithm to cover the multimodality aspects of the problem and to satisfy the LS rule. The first heuristic implements a comprehensive multimodal cost saving search to construct the most efficient integrated vehicle-drone routes. The second heuristic is a light version of the first heuristic as it adopts a vehicle-driven cost saving search. Several experiments are conducted to examine the performance of the developed methodologies using hypothetical grid networks of different sizes. The capability of the developed model in answering a wide variety of questions related to the planning of the vehicle-drone delivery system is illustrated. In addition, a case study is presented in which the developed methodology is applied to provide pick-up and delivery services in the downtown area of the City of Dallas. The results show that mandating the LS rule could double the overall system operation cost especially in dense urban areas with LS obstructions

Life-cycle analysis of last-mile parcel delivery using autonomous delivery robots

The acceleration of global e-commerce brings an increasing environmental burden to urban last-mile logistics. Autonomous delivery robots (ADRs) have often been considered as an attractive solution to this challenge but, to date, their environmental impact had not been fully assessed. To fill this gap, a life-cycle analysis of two-echelon and business-as-usual distribution strategies is proposed in this paper. To model ADR production, primary data from an actual prototype is used. The mathematical formulation of the use stage is done using the continuous approximation methodology. Finally, some managerial insights are obtained. Two-echelon operations would generate between 60 and 130 gCO2-eq per parcel delivery depending on the considered operation scenario. The ADR fleet production and renewal are the biggest contributors to this total global warming potential (GWP). As a consequence, the three main leverages to decrease the GWP of an ADR-based two-echelon delivery scheme are an improvement of the ADR production processes, the maximization of the robot lifespan (both for mechanical parts and battery), and the optimization of delivery operations to minimize the robot fleet size.The first author would like to personally acknowledge CARNET for the funding of this research article, developed in the framework of his PhD thesis. The second author also thanks the funding by the DFG, German Research Foundation, under Germany's Excellence Strategy - EXC 2163/1 – SE2A. The participation of the last author of this paper was made under the project PID2020-118641RB-I00, funded by the Spanish Ministry of Science and Innovation, MCIN/AEI/10.13039/501100011033. The authors also acknowledge the comments of anonymous reviewers that greatly helped in improving and clarifying the paper.Peer ReviewedObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraObjectius de Desenvolupament Sostenible::12 - Producció i Consum ResponsablesObjectius de Desenvolupament Sostenible::13 - Acció per al ClimaObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

Cost and Performance Optimisation in the Technological Phase of Parcel Delivery – A Literature Review

The present review paper provides a systematic insight into the studies published so far when it comes to the research on the cost and performance optimisation in the parcel delivery phase. Globalisation, as well as the new trends, such as selling online, directly influences the demands for the delivery of goods. Demand for the delivery of goods proportionally affects the transport prices. A great majority of deliveries is carried out in densely populated urban areas. In terms of costs, the greatest part in the courier organisations costs is observed in the technological phase of parcel delivery, which is at the same time the least efficient. For that reason, significant improvement of performance and cost optimisation in the very delivery phase is a rather challenging field for the researchers. New algorithm-based technologies, innovations in the logistics and outsourcing of individual technological phases are ways by means of which one strives to enhance the delivery efficiency, to improve performance and quality, but also - to optimise the costs in the last phase of delivery. The aim of the present paper is to offer a systematic review into the most recent research in the field of technology, innovations and outsourcing models with the aim of reducing the cost and enhancing the productivity and quality in parcel delivery