An Alternative Fuel Refueling Station Location Model considering Detour Traffic Flows on a Highway Road System

With the development of alternative fuel (AF) vehicle technologies, studies on finding the potential location of AF refueling stations in transportation networks have received considerable attention. Due to the strong limited driving range, AF vehicles for long-distance intercity trips may require multiple refueling stops at different locations on the way to their destination, which makes the AF refueling station location problem more challenging. In this paper, we consider that AF vehicles requiring multiple refueling stops at different locations during their long-distance intercity trips are capable of making detours from their preplanned paths and selecting return paths that may be different from original paths for their round trips whenever AF refueling stations are not available along the preplanned paths. These options mostly need to be considered when an AF refueling infrastructure is not fully developed on a highway system. To this end, we first propose an algorithm to generate alternative paths that may provide the multiple AF refueling stops between all origin/destination (OD) vertices. Then, a new mixed-integer programming model is proposed to locate AF refueling stations within a preselected set of candidate sites on a directed transportation network by maximizing the coverage of traffic flows along multiple paths. We first test our mathematical model with the proposed algorithm on a classical 25-vertex network with 25 candidate sites through various scenarios that consider a different number of paths for each OD pair, deviation factors, and limited driving ranges of vehicles. Then, we apply our proposed model to locate liquefied natural gas refueling stations in the state of Pennsylvania considering the construction budget. Our results show that the number of alternative paths and deviation distance available significantly affect the coverage of traffic flows at the stations as well as computational time

Application of remote sensing to selected problems within the state of California

There are no author-identified significant results in this report

Multi-vehicle refill scheduling with queueing

© 2017 We consider the problem of refill scheduling for a team of vehicles or robots that must contend for access to a single physical location for refilling. The objective is to minimise time spent in travelling to/from the refill station, and also time lost to queuing (waiting for access). In this paper, we present principled results for this problem in the context of agricultural operations. We first establish that the problem is NP-hard and prove that the maximum number of vehicles that can usefully work together is bounded. We then focus on the design of practical algorithms and present two solutions. The first is an exact algorithm based on dynamic programming that is suitable for small problem instances. The second is an approximate anytime algorithm based on the branch and bound approach that is suitable for large problem instances with many robots. We present simulated results of our algorithms for three classes of agricultural work that cover a range of operations: spot spraying, broadcast spraying and slurry application. We show that the algorithm is reasonably robust to inaccurate prediction of resource utilisation rate, which is difficult to estimate in cases such as spot application of herbicide for weed control, and validate its performance in simulation using realistic scenarios with up to 30 robots

TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution

The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A ton-level liquid scintillator detector will be placed at about 30 m from a core of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be measured with sub-percent energy resolution, to provide a reference spectrum for future reactor neutrino experiments, and to provide a benchmark measurement to test nuclear databases. A spherical acrylic vessel containing 2.8 ton gadolinium-doped liquid scintillator will be viewed by 10 m^2 Silicon Photomultipliers (SiPMs) of >50% photon detection efficiency with almost full coverage. The photoelectron yield is about 4500 per MeV, an order higher than any existing large-scale liquid scintillator detectors. The detector operates at -50 degree C to lower the dark noise of SiPMs to an acceptable level. The detector will measure about 2000 reactor antineutrinos per day, and is designed to be well shielded from cosmogenic backgrounds and ambient radioactivities to have about 10% background-to-signal ratio. The experiment is expected to start operation in 2022

Spatio-temporal optimization of tree removal to efficiently minimize crown fire potential

High-intensity wildfires have resulted in large financial, social, and environmental costs in the western U.S. This trend is not expected to decline soon, as there are millions of overstocked hectares at medium to high risk of catastrophic wildfires. Thinning is being widely used to restore different types of overstocked forest stands. Typically, thinning prescriptions are derived from average stand attributes and applied to landscapes containing a large number of stands. Stand-level thinning prescriptions have thus limitations when applied for reducing the risk of high-intensity wildfires. They use indicators of crown fire potential (e.g., canopy base height and canopy bulk density) that ignore variability of fuels within stands, location of individual cut- and leave-trees after treatments, and the temporal effects of these prescriptions for reducing crown fire potential over time. To address the limitations of current stand-level thinning prescriptions, a computerized approach to optimize individual tree removal and produce site-specific thinning prescriptions was designed. Based on stem maps and tree attributes derived from light detection and technology (LiDAR), the approach predicts individual tree growth over time, quantifies tree-level fuel connectivity, and estimates skidding costs for individual trees. The approach then selects the spatial combination of cut-trees that most efficiently reduces crown fire potential over time while ensuring cost efficiency of the thinning treatment

An emerging paradigm or just another trajectory? Understanding the nature of technological changes using engineering heuristics in the telecommunications switching industry

The theoretical literature on technological changes distinguishes between paradigmatic changes and changes in trajectories. Recently several scholars have performed empirical studies on the way technological trajectories evolve in specific industries, often by predominantly looking at the artifacts. Much less - if any - empirical work has been done on paradigmatic changes, even though these have a much more profound impact on today's industry. It follows from the theory that such studies would need to focus more on the knowledge level than on the artifact level, raising questions on how to operationalize such phenomena. This study aims to fill this gap by applying network-based methodologies to knowledge networks, represented here by patents and patent citations. The rich technological history of telecommunications switches shows how engineers in the post-war period were confronted with huge challenges to meet drastically changing demands. This historical background is a starting point for an in-depth analysis of patents, in search of information about technological direction, technical bottlenecks, and engineering heuristics. We aim to identify when such changes took place over the seven different generations of technological advances this industry has seen. In this way we can easily recognize genuine paradigmatic changes compared to more regular changes in trajectory.technological trajectories; patents; network analysis; telecommunication manufacturing industry

Optimization of route choice, speeds and stops in time-varying networks for fuel-efficient truck journeys

A method is presented for the real-time optimal control of the journey of a truck, travelling between a pair of pick-up/drop-off locations in a time-varying traffic network, in order to reduce fuel consumption. The method, when applied during the journey, encapsulates the choice of route, choice of speeds on the links, and choice of stop locations/durations; when applied pre-trip, it additionally incorporates choice of departure time. The problem is formulated by using a modified form of space-time extended network, in such a way that a shortest path in this network corresponds to an optimal choice of not only route, stops and (when relevant) departure time, but also of speeds. A series of simple illustrative examples are presented to illustrate the formulation. Finally, the method is applied to a realistic-size case study

Distribution planning in a weather-dependent scenario with stochastic travel times: a simheuristics approach

In real-life logistics, distribution plans might be affected by weather conditions (rain, snow, and fog), since they might have a significant effect on traveling times and, therefore, on total distribution costs. In this paper, the distribution problem is modeled as a multi-depot vehicle routing problem with stochastic traveling times. These traveling times are not only stochastic in nature but the specific probability distribution used to model them depends on the particular weather conditions on the delivery day. In order to solve the aforementioned problem, a simheuristic approach combining simulation within a biased-randomized heuristic framework is proposed. As the computational experiments will show, our simulation-optimization algorithm is able to provide high-quality solutions to this NP-hard problem in short computing times even for large-scale instances. From a managerial perspective, such a tool can be very useful in practical applications since it helps to increase the efficiency of the logistics and transportation operations.Peer ReviewedPostprint (published version