Víctor Muñoz

Schedule coordination through egalitarian recurrent multi-unit combinatorial auctions

When selfish industries are competing for limited shared resources, they need to coordinate their activities to handle possible conflicting situations. Moreover, this coordination should not affect the activities already planned by the industries, since this could have negative effects on their performance. Although agents may have buffers that allow them to delay the use of resources, these are of a finite capacity, and therefore cannot be used indiscriminately. Thus, we are faced with the problem of coordinating schedules that have already been generated by the agents. To address this task, we propose to use a recurrent auction mechanism to mediate between the agents. Through this auction mechanism, the agents can express their interest in using the resources, thus helping the scheduler to find the best distribution. We also introduce a priority mechanism to add fairness to the coordination process. The proposed coordination mechanism has been applied to a waste water treatment system scenario, where different industries need to discharge their waste. We have simulated the behavior of the system, and the results show that using our coordination mechanism the waste water treatment plant can successfully treat most of the discharges, while the production activity of the industries is almost not affected by it.

Fairness In Recurrent Auctions With Competing Markets And Supply Fluctuations

Auctions have been used to deal with resource allocation in multiagent environments, especially in service‐oriented electronic markets. In this type of market, resources are perishable and auctions are repeated over time with the same or a very similar set of agents. In this scenario it is advisable to use recurrent auctions: a sequence of auctions of any kind where the result of one auction may influence the following one. Some problems do appear in these situations, as for instance, the bidder drop problem, the asymmetric balance of negotiation power or resource waste, which could cause the market to collapse. Fair mechanisms can be useful to minimize the effects of these problems. With this aim, we have analyzed four previous fair mechanisms under dynamic scenarios and we have proposed a new one that takes into account changes in the supply as well as the presence of alternative marketplaces. We experimentally show how the new mechanism presents a higher average performance under all simulated conditions, resulting in a higher profit for the auctioneer than with the previous ones, and in most cases avoiding the waste of resources.

Review: Improving urban wastewater management through an auction-based management of discharges

This article proposes the use of an auction process in which the capacity of a WasteWater Treatment Plant (WWTP) is sold to coordinate different industrial discharges within the Urban Wastewater System (UWS). The main goal of coordination is to manage the wastewater inflow rate and pollutants to improve the WWTP operation. The system is modeled as a multi-agent system where each industry is represented by an agent, another agent represents the influent coming from the domestic use and one agent represents the WWTP. When the maximum level of the flow or the maximum concentrations of some components exceed the plants capacity, an auction starts. In the auction, the WWTP agent is the auctioneer that sells its resources and the industry agents are the bidders that want to buy the resources. The winners of the auction will discharge to the sewage system and the losers will have to wait for the next opportunity. The resulting wastewater discharge schedules of the industries have been analyzed using the IWA/COST simulation benchmark as a case study. The results obtained through this simulation protocol show that the auction-based coordination mechanism using both pollution and hydraulic capacity constraints accomplishes the goal of improving the effluent quality, achieving a reduction in the impact of industrial discharges up to 20.99%.

Strategies for exploiting trust models in competitive multi-agent systems

Multi-agent systems where agents compete against one another in a specific environment pose challenges in relation to the trust modeling of an agent aimed at ensuring the right decisions are taken. A lot of literature has focused on describing trust models, but less in developing strategies to use them optimally. In this paper we propose a decision-making strategy that uses the information provided by the trust model to take the best decisions to achieve the most benefits for the agent. This decision making tackles the exploration versus exploitation problem since the agent has to decide when to interact with the known agents and when to look for new ones. The experiments were performed using the ART Testbed, a simulator created with the goal of objectively evaluate different trust strategies. The agent competed in and won the Third International ART Testbed Competition held in Estoril (Portugal) in March 2008.

A Fair Mechanism for Recurrent Multi-unit Auctions

Auctions are a good tool for dealing with resource allocation in multi-agent environments. When the resources are either renewable or perishable, a repeated auction mechanism is needed, in what is known as recurrent auctions. However, several problems arise with this kind of auction, namely, the resource waste problem, the bidder drop problem, and the asymmetric balance of negotiation power. In this paper we present different mechanisms to deal with these issues. We have evaluated the mechanisms in a network bandwidth allocation scenario, and the results show that the proposed mechanisms achieve higher benefits for the auctioneer, while also providing a fairer behavior.

Solving large immobile location-allocation by affinity propagation and simulated annealing. Application to select which sporting event to watch

Immobile Location-Allocation (ILA) is a combinatorial problem which consists in, given a set of facilities and a set of demand points, determining the optimal service each facility has to offer and allocating the demand to such facilities. The applicability of optimization methods is tied up to the dimensionality of the problem, but since the distance between data points is a key factor, clustering techniques to partition the data space can be applied, converting the large initial problem into several simpler ILA problems that can be solved separately. This paper presents a novel method that combines clustering and heuristic methods to solve an ILA problem, which reduces the elapsed time keeping the quality of the solution found compared with other heuristics methods.

Developing strategies for the ART domain

In this paper we propose the design of an agent for the ART Testbed, a tool created with the goal of objectively evaluate different trust strategies. The agent design includes a trust model and a strategy for decision making. The trust model is based on the three components of trust considered in ART, namely direct, indirect (reputation) and self trust (certainty). It also incorporates a variable time window size based on the available information that allows the agent to easily adapt to possible changes in the environment. The decision-making strategy uses the information provided by the trust model to take the best decisions to achieve the most benefits for the agent. This decision making tackles the exploration versus exploitation problem since the agent has to decide when to interact with the known agents and when to look for new ones. The agent, called Uno2008, competed in and won the Third International ART Testbed Competition held at AAMAS in March 2008.

Dynamic Configurable Auctions for Coordinating Industrial Waste Discharges

The use of auctions for distributing resources in competing environments has produced a large variety of auctions types and algorithms to treat them. However, auctions have some problems when faced with some real-world applications containing renewable and perishable resources. In this paper we present a mechanism to deal with such issues by dynamically configuring some of the auction parameters taking into account the past experience. The mechanism has been used to coordinate industrial discharges and a Waste Water Treatment Plant, so that the treatment thresholds of the plant are never exceeded. We have performed some simulations to evaluate the system, and the results show that with this mechanism the coordination between the industries improves the treatment of the water.

Experimental analysis of optimization techniques on the road passenger transportation problem

Analyzing the state of the art in a given field in order to tackle a new problem is always a mandatory task. Literature provides surveys based on summaries of previous studies, which are often based on theoretical descriptions of the methods. An engineer, however, requires some evidence from experimental evaluations in order to make the appropriate decision when selecting a technique for a problem. This is what we have done in this paper: experimentally analyzed a set of representative state-of-the-art techniques in the problem we are dealing with, namely, the road passenger transportation problem. This is an optimization problem in which drivers should be assigned to transport services, fulfilling some constraints and minimizing some function cost. The experimental results have provided us with good knowledge of the properties of several methods, such as modeling expressiveness, anytime behavior, computational time, memory requirements, parameters, and free downloadable tools. Based on our experience, we are able to choose a technique to solve our problem. We hope that this analysis is also helpful for other engineers facing a similar problem.

Reformulation based MaxSAT robustness

The presence of uncertainty in the real world makes robustness a desirable property of solutions to constraint satisfaction problems (CSP). A solution is said to be robust if it can be easily repaired when unexpected events happen. This issue has already been addressed in the frameworks of Boolean satisfiability (SAT) and Constraint Programming (CP). Most existing works on robustness implement search algorithms to look for robust solutions instead of taking the declarative approach of reformulation, since reformulation tends to generate prohibitively large formulas, especially in the CP setting. In this paper we consider the unaddressed problem of robustness in weighted MaxSAT, by showing how robust solutions to weighted MaxSAT instances can be effectively obtained via reformulation into pseudo-Boolean formulae. Our encoding provides a reasonable balance between increase in size and performance, as shown by our experiments in the robust resource allocation framework. We also address the problem of flexible robustness, where some of the breakages may be left unrepaired if a totally robust solution does not exist. In a sense, since the use of SAT and MaxSAT encodings for solving CSP has been gaining wide acceptance in recent years, we provide an easy-to-implement new method for achieving robustness in combinatorial optimization problems.