36 research outputs found
An Optimisation Algorithm to Establish the Location of Stations of a Mixed Fleet Biking System: An Application to the City of Lisbon
AbstractThis paper presents the design and deployment of a bike-sharing system developed for Lisbon. The design of this new service is performed through an heuristic, encompassing a Mixed Integer Linear Program (MILP), that simultaneously optimise the location of shared biking stations, the fleet dimension and measuring the bicycle relocation activities required in a regular operation day. The results obtained for the several tested scenarios provided better insights into knowing how to improve the design and operation of these systems
On-line Non-stationary Inventory Control using Champion Competition
The commonly adopted assumption of stationary demands cannot actually reflect
fluctuating demands and will weaken solution effectiveness in real practice. We
consider an On-line Non-stationary Inventory Control Problem (ONICP), in which
no specific assumption is imposed on demands and their probability
distributions are allowed to vary over periods and correlate with each other.
The nature of non-stationary demands disables the optimality of static (s,S)
policies and the applicability of its corresponding algorithms. The ONICP
becomes computationally intractable by using general Simulation-based
Optimization (SO) methods, especially under an on-line decision-making
environment with no luxury of time and computing resources to afford the huge
computational burden. We develop a new SO method, termed "Champion Competition"
(CC), which provides a different framework and bypasses the time-consuming
sample average routine adopted in general SO methods. An alternate type of
optimal solution, termed "Champion Solution", is pursued in the CC framework,
which coincides the traditional optimality sense under certain conditions and
serves as a near-optimal solution for general cases. The CC can reduce the
complexity of general SO methods by orders of magnitude in solving a class of
SO problems, including the ONICP. A polynomial algorithm, termed "Renewal Cycle
Algorithm" (RCA), is further developed to fulfill an important procedure of the
CC framework in solving this ONICP. Numerical examples are included to
demonstrate the performance of the CC framework with the RCA embedded.Comment: I just identified a flaw in the paper. It may take me some time to
fix it. I would like to withdraw the article and update it once I finished.
Thank you for your kind suppor
Collision risk-capacity tradeoff analysis of an en-route corridor model
AbstractFlow corridors are a new class of trajectory-based airspace which derives from the next generation air transportation system concept of operations. Reducing the airspace complexity and increasing the capacity are the main purposes of the en-route corridor. This paper analyzes the collision risk-capacity tradeoff using a combined discrete–continuous simulation method. A basic two-dimensional en-route flow corridor with performance rules is designed as the operational environment. A second-order system is established by combining the point mass model and the proportional derivative controller together to simulate the self-separation operations of the aircrafts in the corridor and the operation performance parameters from the User Manual for the Base of Aircraft Data are used in this research in order to improve the reliability. Simulation results indicate that the aircrafts can self-separate from each other efficiently by adjusting their velocities, and rationally setting the values of some variables can improve the rate and stability of the corridor with low risks of loss of separation
A Knowledge Gradient Policy for Sequencing Experiments to Identify the Structure of RNA Molecules Using a Sparse Additive Belief Model
We present a sparse knowledge gradient (SpKG) algorithm for adaptively
selecting the targeted regions within a large RNA molecule to identify which
regions are most amenable to interactions with other molecules. Experimentally,
such regions can be inferred from fluorescence measurements obtained by binding
a complementary probe with fluorescence markers to the targeted regions. We use
a biophysical model which shows that the fluorescence ratio under the log scale
has a sparse linear relationship with the coefficients describing the
accessibility of each nucleotide, since not all sites are accessible (due to
the folding of the molecule). The SpKG algorithm uniquely combines the Bayesian
ranking and selection problem with the frequentist regularized
regression approach Lasso. We use this algorithm to identify the sparsity
pattern of the linear model as well as sequentially decide the best regions to
test before experimental budget is exhausted. Besides, we also develop two
other new algorithms: batch SpKG algorithm, which generates more suggestions
sequentially to run parallel experiments; and batch SpKG with a procedure which
we call length mutagenesis. It dynamically adds in new alternatives, in the
form of types of probes, are created by inserting, deleting or mutating
nucleotides within existing probes. In simulation, we demonstrate these
algorithms on the Group I intron (a mid-size RNA molecule), showing that they
efficiently learn the correct sparsity pattern, identify the most accessible
region, and outperform several other policies
Ranking and Selection under Input Uncertainty: Fixed Confidence and Fixed Budget
In stochastic simulation, input uncertainty (IU) is caused by the error in
estimating the input distributions using finite real-world data. When it comes
to simulation-based Ranking and Selection (R&S), ignoring IU could lead to the
failure of many existing selection procedures. In this paper, we study R&S
under IU by allowing the possibility of acquiring additional data. Two
classical R&S formulations are extended to account for IU: (i) for fixed
confidence, we consider when data arrive sequentially so that IU can be reduced
over time; (ii) for fixed budget, a joint budget is assumed to be available for
both collecting input data and running simulations. New procedures are proposed
for each formulation using the frameworks of Sequential Elimination and Optimal
Computing Budget Allocation, with theoretical guarantees provided accordingly
(e.g., upper bound on the expected running time and finite-sample bound on the
probability of false selection). Numerical results demonstrate the
effectiveness of our procedures through a multi-stage production-inventory
problem
Efficient computing budget allocation by using regression with sequential sampling constraint
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