47,545 research outputs found
Train schedule coordination at an interchange station through agent negotiation
In open railway markets, coordinating train schedules at an interchange station requires negotiation between two independent train operating companies to resolve their operational conflicts. This paper models the stakeholders as software agents and proposes an agent negotiation model to study their interaction. Three negotiation strategies have been devised to represent the possible objectives of the stakeholders, and they determine the behavior in proposing offers to the proponent. Empirical simulation results confirm that the use of the proposed negotiation strategies lead to outcomes that are consistent with the objectives of the stakeholders
Solving Hard Control Problems in Voting Systems via Integer Programming
Voting problems are central in the area of social choice. In this article, we
investigate various voting systems and types of control of elections. We
present integer linear programming (ILP) formulations for a wide range of
NP-hard control problems. Our ILP formulations are flexible in the sense that
they can work with an arbitrary number of candidates and voters. Using the
off-the-shelf solver Cplex, we show that our approaches can manipulate
elections with a large number of voters and candidates efficiently
Learning optimization models in the presence of unknown relations
In a sequential auction with multiple bidding agents, it is highly
challenging to determine the ordering of the items to sell in order to maximize
the revenue due to the fact that the autonomy and private information of the
agents heavily influence the outcome of the auction.
The main contribution of this paper is two-fold. First, we demonstrate how to
apply machine learning techniques to solve the optimal ordering problem in
sequential auctions. We learn regression models from historical auctions, which
are subsequently used to predict the expected value of orderings for new
auctions. Given the learned models, we propose two types of optimization
methods: a black-box best-first search approach, and a novel white-box approach
that maps learned models to integer linear programs (ILP) which can then be
solved by any ILP-solver. Although the studied auction design problem is hard,
our proposed optimization methods obtain good orderings with high revenues.
Our second main contribution is the insight that the internal structure of
regression models can be efficiently evaluated inside an ILP solver for
optimization purposes. To this end, we provide efficient encodings of
regression trees and linear regression models as ILP constraints. This new way
of using learned models for optimization is promising. As the experimental
results show, it significantly outperforms the black-box best-first search in
nearly all settings.Comment: 37 pages. Working pape
Designing and Evaluating Sustainable Logistics Networks
The objective in this paper is to shed light into the design of logistic networks balancing profit and the environment. More specifically we intend to i) determine the main factors influencing environmental performance and costs in logistic networks ii) present a comprehensive framework and mathematical formulation, based on multiobjective programming, integrating all relevant variables in order to explore efficient logistic network configurations iii) present the expected computational results of such formulation and iv) introduce a technique to evaluate the efficiency of existing logistic networks.The European Pulp and Paper Industry will be used to illustrate our findings.Eco-efficiency;Data Envelopment Analysis (DEA);Multi-Objective Programming (MOP);Supply Chain Design;Sustainable Supply Chain
On the Essential Multidimensionality of an Economic Problem: Towards Tradeoffs-Free Economics
The foundation of welfare economics is the assumption of Pareto-efficiency and its concept of tradeoffs. Also the production possibility frontier, efficiency frontier, nondominated set, etc., belong to the plethora of tools derived from the Pareto principle. The assumption of tradeoffs does not address the issue of system design or redesign in order to reduce or eliminate tradeoffs as a sure characteristic of suboptimal, inefficient system configuration. In this paper we establish that tradeoffs are not attributes of objectives, criteria or dimensions, as it is habitually assumed, but are the properties of the very sets of possibilities, alternatives or options they purport to value and measure. We use De novo programming, through which the so called feasible set of opportunities can be redefined towards optimal, tradeoffs-free configuration. The implications of tradeoff-free economics are too vast to foresee and elaborate in a single paper; they do touch the very foundations of economic thought. So me numerical examples are given in order to illustrate system-design calculations in linear systems.Tradeoffs, multiple criteria, decision making, tradeoffs-free, optimization, De novo programming, Pareto-efficiency, added value
Slow Adaptive OFDMA Systems Through Chance Constrained Programming
Adaptive OFDMA has recently been recognized as a promising technique for
providing high spectral efficiency in future broadband wireless systems. The
research over the last decade on adaptive OFDMA systems has focused on adapting
the allocation of radio resources, such as subcarriers and power, to the
instantaneous channel conditions of all users. However, such "fast" adaptation
requires high computational complexity and excessive signaling overhead. This
hinders the deployment of adaptive OFDMA systems worldwide. This paper proposes
a slow adaptive OFDMA scheme, in which the subcarrier allocation is updated on
a much slower timescale than that of the fluctuation of instantaneous channel
conditions. Meanwhile, the data rate requirements of individual users are
accommodated on the fast timescale with high probability, thereby meeting the
requirements except occasional outage. Such an objective has a natural chance
constrained programming formulation, which is known to be intractable. To
circumvent this difficulty, we formulate safe tractable constraints for the
problem based on recent advances in chance constrained programming. We then
develop a polynomial-time algorithm for computing an optimal solution to the
reformulated problem. Our results show that the proposed slow adaptation scheme
drastically reduces both computational cost and control signaling overhead when
compared with the conventional fast adaptive OFDMA. Our work can be viewed as
an initial attempt to apply the chance constrained programming methodology to
wireless system designs. Given that most wireless systems can tolerate an
occasional dip in the quality of service, we hope that the proposed methodology
will find further applications in wireless communications
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