Optimal Combinatorial Electricity Markets

The deregulation of the electricity industry in many countries has created a number of marketplaces in which producers and consumers can operate in order to more effectively manage and meet their energy needs. To this end, this paper develops a new model for electricity retail where end-use customers choose their supplier from competing electricity retailers. The model is based on simultaneous reverse combinatorial auctions, designed as a second-price sealed-bid multi-item auction with supply function bidding. This model prevents strategic bidding and allows the auctioneer to maximise its payoff. Furthermore, we develop optimal single-item and multi-item algorithms for winner determination in such auctions that are significantly less complex than those currently available in the literature

PENANGANAN HIPOKALSEMIA PADA SAPI PERAH DI UNIT PELAKSANA TEKNIS DAERAH (UPTD) PUSKESWAN PADANG PANJANG

Banda Ace

La nissaga catalana del món clàssic

La nissaga catalana del món clàssic és un volum que conté un recull de 165 semblances de les persones que, al llarg de la història del nostre poble, han tingut una pàtria comuna: el món clàssic. És una obra col·lectiva plantejada com un diàleg a través del temps entre els qui ja no hi són i els qui en som hereus i encara habitem en aquesta pàtria comuna a les terres de llengua catalana. És un intent de trobar les interaccions i les aportacions de les persones des de diverses dimensions i perspectives del món clàssic, i establir les relacions de mestratge i col·laboració que hi ha entre els protagonistes

Does the Sun Shine by pp or CNO Fusion Reactions?

We show that solar neutrino experiments set an upper limit of 7.8% (7.3% including the recent KamLAND measurements) to the fraction of energy that the Sun produces via the CNO fusion cycle, which is an order of magnitude improvement upon the previous limit. New experiments are required to detect CNO neutrinos corresponding to the 1.5% of the solar luminosity that the standard solar model predicts is generated by the CNO cycle.Comment: Background information at http://www.sns.ias.edu/~jn

Convocatoria

Convocatòria a l'assemblea general ordinària

Aproximació a Verdaguer

Abstract not availabl

Approximate algorithms for decentralized Supply Chain Formation

[eng] Supply chain formation involves determining the participants and the exchange of goods within a production network. Today’s companies operate autonomously, making local decisions, and coordinating with other companies to buy and sell goods along their supply chains. Decentralized decision making is well suited to this scenario since it better preserves the privacy of the participants, offers better scalability on large-scale scenarios, and is more resilient to failure. Moreover, decentralized supply chain formation can be tackled either by means of peer-to-peer communication between supply chain participants or by introducing local markets that mediate the trading of goods. Unfortunately, current approaches to decentralized supply chain formation, both in the peer- to-peer and the mediated scenario, are unable to provide computationally and economically efficient solutions to the supply chain formation problem. The main goal of this dissertation is to provide computationally and economically efficient methods for decentralized supply chain formation both in the peer-to-peer and the mediated scenario. This is achieved by means of two optimized max-sum based methods for supply chain formation. On the one hand, we contribute to peer-to-peer supply chain formation via the so-called Reduced Binarized Loopy Belief Propagation (rb-lbp) algorithm. The rb-lbp algorithm is run by a multi-agent system in which each of the participants in the supply chain is represented by a computational agent. Moreover, rb-lbp’s message computation mechanisms allow the efficient computation of max-sum messages. This results in an algorithm that is able to find solutions to the supply chain formation problem of higher value than the state of the art while reducing the memory, bandwidth and computational resources required by several orders of magnitude. On the other hand, we contribute to mediated supply chain formation via the so-called CHaining Agents IN Mediated Environments (chainme) algorithm. The chainme algorithm is run by a multi-agent system in which each of the participants and each of the goods in the supply chain is represented by a computational agent. In chainme participant agents communicate exclusively with the agents representing the goods who act as mediators. Likewise rb-lbp, chainme is also endowed with a message computation mechanism for the efficient computation of max-sum messages. This results in an algorithm that is able to find economically efficient solutions while requiring a fraction of the computa- tional resources needed by the state-of-the-art methods for both peer-to-peer and mediated supply chain formation. Finally, the design and implementation of both of our contributions to decentralized supply chain formation follow the same methodology. That is, we first map the problem at hand into a local term graph over which max-sum can operate. Then, we assign each max-sum local term to a computational agent. Last, we derive computationally efficient expressions to assess the max-sum messages exchanged between these agents. Although our methodology proved to be valid for the design of SCF algorithms, its generality makes it appear as a promising candidate for other multi-agent coordination problems