7,217 research outputs found
Robustness and Adaptiveness Analysis of Future Fleets
Making decisions about the structure of a future military fleet is a
challenging task. Several issues need to be considered such as the existence of
multiple competing objectives and the complexity of the operating environment.
A particular challenge is posed by the various types of uncertainty that the
future might hold. It is uncertain what future events might be encountered; how
fleet design decisions will influence and shape the future; and how present and
future decision makers will act based on available information, their personal
biases regarding the importance of different objectives, and their economic
preferences. In order to assist strategic decision-making, an analysis of
future fleet options needs to account for conditions in which these different
classes of uncertainty are exposed. It is important to understand what
assumptions a particular fleet is robust to, what the fleet can readily adapt
to, and what conditions present clear risks to the fleet. We call this the
analysis of a fleet's strategic positioning. This paper introduces how
strategic positioning can be evaluated using computer simulations. Our main aim
is to introduce a framework for capturing information that can be useful to a
decision maker and for defining the concepts of robustness and adaptiveness in
the context of future fleet design. We demonstrate our conceptual framework
using simulation studies of an air transportation fleet. We capture uncertainty
by employing an explorative scenario-based approach. Each scenario represents a
sampling of different future conditions, different model assumptions, and
different economic preferences. Proposed changes to a fleet are then analysed
based on their influence on the fleet's robustness, adaptiveness, and risk to
different scenarios
Robustness and Adaptability Analysis of Future Military Air Transportation Fleets
Making decisions about the structure of a future military fleet is challenging. Several issues need to be considered, including multiple competing objectives and the complexity of the operating environment. A particular challenge is posed by the various types of uncertainty that the future holds. It is uncertain what future events might be encountered and how fleet design decisions will influence these events. In order to assist strategic decision-making, an analysis of future fleet options needs to account for conditions in which these different uncertainties are exposed. It is important to understand what assumptions a particular fleet is robust to, what the fleet can readily adapt to, and what conditions present risks to the fleet. We call this the analysis of a fleet’s strategic positioning. Our main aim is to introduce a framework that captures information useful to a decision maker and defines the concepts of robustness and adaptability in the context of future fleet design. We demonstrate our conceptual framework by simulating an air transportation fleet problem. We account for uncertainty by employing an explorative scenario-based approach. Each scenario represents a sampling of different future conditions and different model assumptions. Proposed changes to a fleet are then analysed based on their influence on the fleet’s robustness, adaptability, and risk to different scenarios
A Spatial Model of Dolphin Avoidance in the Eastern Tropical Pacific Ocean
This paper examines the impact of dolphin-safe eco-labeling and how it fundamentally altered the spatial distribution of fishing effort and fishermen's willingness to pay to avoid dolphins. To do this, a dynamic discrete choice econometric model is applied to the Eastern Tropical Pacific tuna fishery. This econometric approach combines a dynamic programming component with the static discrete site choice model. This estimator couples the current period projected profits associated with fishing a specific site with the value of all future location choices on the cruise, assuming choices are made optimally. The key feature of this model is that it recovers behavioral parameters and solves the dynamic programming problem recursively. The dynamic site choice model reveals a markedly higher impact on producers as compared to the commonly used static model following the labeling regime. Further, in all but a few cases the common practice in dynamic choice models of setting discount factors equal to one is rejected.Environmental Economics and Policy,
A Distributed and Privacy-Aware Speed Advisory System for Optimising Conventional and Electric Vehicles Networks
One of the key ideas to make Intelligent Transportation Systems (ITS) work
effectively is to deploy advanced communication and cooperative control
technologies among the vehicles and road infrastructures. In this spirit, we
propose a consensus-based distributed speed advisory system that optimally
determines a recommended common speed for a given area in order that the group
emissions, or group battery consumptions, are minimised. Our algorithms achieve
this in a privacy-aware manner; namely, individual vehicles do not reveal
in-vehicle information to other vehicles or to infrastructure. A mobility
simulator is used to illustrate the efficacy of the algorithm, and
hardware-in-the-loop tests involving a real vehicle are given to illustrate
user acceptability and ease of the deployment.Comment: This is a journal paper based on the conference paper "Highway speed
limits, optimised consensus, and intelligent speed advisory systems"
presented at the 3rd International Conference on Connected Vehicles and Expo
(ICCVE 2014) in November 2014. This is the revised version of the paper
recently submitted to the IEEE Transactions on Intelligent Transportation
Systems for publicatio
The Merits of Sharing a Ride
The culture of sharing instead of ownership is sharply increasing in
individuals behaviors. Particularly in transportation, concepts of sharing a
ride in either carpooling or ridesharing have been recently adopted. An
efficient optimization approach to match passengers in real-time is the core of
any ridesharing system. In this paper, we model ridesharing as an online
matching problem on general graphs such that passengers do not drive private
cars and use shared taxis. We propose an optimization algorithm to solve it.
The outlined algorithm calculates the optimal waiting time when a passenger
arrives. This leads to a matching with minimal overall overheads while
maximizing the number of partnerships. To evaluate the behavior of our
algorithm, we used NYC taxi real-life data set. Results represent a substantial
reduction in overall overheads
Shipping markets and freight rates: an analysis of the Baltic Dry Index
Shipping, although a crucial component of the transportation of commodities worldwide, is hardly present in the finance literature at this point. The first and foremost goal of this paper is to describe and explain from an economic perspective the key features of shipping markets; the second one is to analyze the behavior of freight rates, which define the final cost of an imported commodity. We focus on the major index, the BDI (Baltic Dry Index) and propose some diffusion models able to capture the unique features of its trajectories, namely large swings and continuity. Their performance is exhibited on a database covering the period 1988-2010. Such spot models should facilitate the growth of the market of freight rates options, a safe hedging instrument for farmers and cooperatives that ship their grains to distant destinations
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