Drone location and scheduling problems in humanitarian logistics.

Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, are a promising technology for the last-mile delivery of medical and aid items in humanitarian logistics. In emergency scenarios, like disasters, where transportation networks are destroyed and people are stranded, drones can accelerate the delivery of urgently needed items, e.g., food and water, insulin shots and blood pressure pills, to those trapped in the disaster-affected areas. Drones can also provide logistics services in many non-emergency situations by delivering medical items, e.g., vaccine shots and lab specimens, to remote communities and hard-to-access locations. The contribution of using UAVs goes beyond merely having access to remote and disaster-affected areas. With inexpensive launching infrastructures and no need for on-board pilots, drones can offer an inexpensive, agile, and ready-to-use alternative to traditional last-mile delivery modes. Motivated by the challenges associated with the last-mile delivery of aid items to hard-to-access areas, this dissertation studies the problem of drone-based delivery of aid items, e.g., medical and relief packages, to hard-to-access areas in humanitarian logistics. The main goal of this dissertation is to design the logistics and orchestrate a fleet of drones to provide the timely delivery of items to hard-to-access areas in emergency and non-emergency scenarios. In this dissertation, we develop multiple extensions of a drone location and scheduling problem while taking into account the critical aspects of drone- based delivery systems in humanitarian logistics. These critical aspects include: i) limited coverage range, ii) limited payload capacity, iii) energy consumption, iv) timeliness, and v) uncertainty. Chapter II presents a general case of the drone location and scheduling (DLS) problem for the delivery of aid items in disaster-affected areas. In this chapter, we first develop a time-slot formulation to address the problem of optimally locating drone take- off platforms and concurrently scheduling and sequencing a set of trips for each drone to minimize total disutility for product delivery. We extend a two-period problem of DLS where the platforms can be relocated using useable road networks after the first period in order to provide a higher level of coverage. Chapter III proposes a multi-stop drone location and scheduling (MDLS) problem for the delivery of medical items in rural and suburban areas. In this chapter, we assume drones are allowed to stop at one or multiple charging stations, installed on existing platforms having access to electricity, e.g., streetlights, during each trip in order to improve the drones’ coverage range while considering the drones’ energy consumption. The problem is to find optimum locations for medical item providers and charging stations as well as optimally scheduling and sequencing drone trips over a long-term horizon. Chapter IV presents a stochastic extension for the drone location and scheduling (SDLS) problem. Due to the lack of information and instability of the situation, we assume the set of demand locations is not known. The main problem is to locate a set of drone take-off platforms so that with a given probability, the maximum total disutility (or cost) under all realizations of the demand locations is minimized. Finally, Chapter V presents a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas in humanitarian logistics. Our goal is to develop a simulation-based system to perform analytical/numerical studies, evaluate the performance of a drone delivery system in humanitarian logistics, and support the decision-making process in such a context while considering multiple sources of variabilities

Market size and attendance in English Premier League football

This paper models the impacts of market size and team competition for fan base on matchday attendance in the English Premier League over the period 1997-2004 using a large panel data set. We construct a comprehensive set of control variables and use tobit estimation to overcome the problems caused by sell-out crowds. We also account for unobserved influences on attendance by means of random effects attached to home teams. Our treatment of market size, with its use of Geographical Information System techniques, is more sophisticated than in previous attendance demand studies.

Auction-based Bandwidth Allocation Mechanisms for Wireless Future Internet

An important aspect of the Future Internet is the efficient utilization of (wireless) network resources. In order for the - demanding in terms of QoS - Future Internet services to be provided, the current trend is evolving towards an "integrated" wireless network access model that enables users to enjoy mobility, seamless access and high quality of service in an all-IP network on an "Anytime, Anywhere" basis. The term "integrated" is used to denote that the Future Internet wireless "last mile" is expected to comprise multiple heterogeneous geographically coexisting wireless networks, each having different capacity and coverage radius. The efficient management of the wireless access network resources is crucial due to their scarcity that renders wireless access a potential bottleneck for the provision of high quality services. In this paper we propose an auction mechanism for allocating the bandwidth of such a network so that efficiency is attained, i.e. social welfare is maximized. In particular, we propose an incentive-compatible, efficient auction-based mechanism of low computational complexity. We define a repeated game to address user utilities and incentives issues. Subsequently, we extend this mechanism so that it can also accommodate multicast sessions. We also analyze the computational complexity and message overhead of the proposed mechanism. We then show how user bids can be replaced from weights generated by the network and transform the auction to a cooperative mechanism capable of prioritizing certain classes of services and emulating DiffServ and time-of-day pricing schemes. The theoretical analysis is complemented by simulations that assess the proposed mechanisms properties and performance. We finally provide some concluding remarks and directions for future research

Clear skies : the fight for dominance in the airline industry

Related link(s): http://www.richmondfed.org/publications/research/region_focus/2009/spring/feature3_weblinks.cfmAirlines

Trends in life cycle greenhouse gas emissions of future light duty electric vehicles

The majority of previous studies examining life cycle greenhouse gas (LCGHG) emissions of battery electric vehicles (BEVs) have focused on efficiency-oriented vehicle designs with limited battery capacities. However, two dominant trends in the US BEV market make these studies increasingly obsolete: sales show significant increases in battery capacity and attendant range and are increasingly dominated by large luxury or high-performance vehicles. In addition, an era of new use and ownership models may mean significant changes to vehicle utilization, and the carbon intensity of electricity is expected to decrease. Thus, the question is whether these trends significantly alter our expectations of future BEV LCGHG emissions. To answer this question, three archetypal vehicle designs for the year 2025 along with scenarios for increased range and different use models are simulated in an LCGHG model: an efficiency-oriented compact vehicle; a high performance luxury sedan; and a luxury sport utility vehicle. While production emissions are less than 10% of LCGHG emissions for today's gasoline vehicles, they account for about 40% for a BEV, and as much as two-thirds of a future BEV operated on a primarily renewable grid. Larger battery systems and low utilization do not outweigh expected reductions in emissions from electricity used for vehicle charging. These trends could be exacerbated by increasing BEV market shares for larger vehicles. However, larger battery systems could reduce per-mile emissions of BEVs in high mileage applications, like on-demand ride sharing or shared vehicle fleets, meaning that trends in use patterns may countervail those in BEV design