A Lotting Method for Electronic Reverse Auctions

An increasing number of commercial companies are using online reverse auctions for their sourcing activities. In reverse auctions, multiple suppliers bid for a contract from a buyer for selling goods and/or services. Usually, the buyer has to procure multiple items, which are typically divided into lots for auctioning purposes. By steering the composition of the lots, a buyer can increase the attractiveness of its lots for thesuppliers, which can then make more competitive offers, leading to larger savings for the procuring party. In this paper, a clustering-based heuristic lotting method is proposed for reverse auctions. Agglomerative clustering is used for determining the items that will be put in the same lot. A suitable metric is defined, which allows the procurer to incorporate various approaches to lotting. The proposed lotting method has been tested for the procurement activities of a consumer packaged goods company. The results indicate that the proposed strategy leads to 2-3% savings, while the procurement experts confirm that the lots determined by the proposed method are acceptable given the procurement goals.e-commerce;reverse auctions;hierarchical clustering;lotting;e-procurement

Fuzzy clustering with volume prototypes and adaptive cluster merging

Two extensions to the objective function-based fuzzy clustering are proposed. First, the (point) prototypes are extended to hypervolumes, whose size can be fixed or can be determined automatically from the data being clustered. It is shown that clustering with hypervolume prototypes can be formulated as the minimization of an objective function. Second, a heuristic cluster merging step is introduced where the similarity among the clusters is assessed during optimization. Starting with an overestimation of the number of clusters in the data, similar clusters are merged in order to obtain a suitable partitioning. An adaptive threshold for merging is proposed. The extensions proposed are applied to Gustafson–Kessel and fuzzy c-means algorithms, and the resulting extended algorithm is given. The properties of the new algorithm are illustrated by various examples

A Theoretical Analysis of Cooperative Behavior in Multi-Agent Q-learning

A number of experimental studies have investigated whether cooperative behavior may emerge in multi-agent Q-learning. In some studies cooperative behavior did emerge, in others it did not. This report provides a theoretical analysis of this issue. The analysis focuses on multi-agent Q-learning in iterated prisoner’s dilemmas. It is shown that under certain assumptions cooperative behavior may emerge when multi-agent Q-learning is applied in an iterated prisoner’s dilemma. An important consequence of the analysis is that multi-agent Q-learning may result in non-Nash behavior. It is found experimentally that the theoretical results derived in this report are quite robust to violations of the underlying assumptions.Cooperation;Multi-Agent Q-Learning;Multi-Agent Reinforcement Learning;Nash Equilibrium;Prisoner’s Dilemma

Weighted Constraints in Fuzzy Optimization

Many practical optimization problems are characterized by someflexibility in the problem constraints, where this flexibility canbe exploited for additional trade-off between improving theobjective function and satisfying the constraints. Especially indecision making, this type of flexibility could lead to workablesolutions, where the goals and the constraints specified bydifferent parties involved in the decision making are traded offagainst one another and satisfied to various degrees. Fuzzy setshave proven to be a suitable representation for modeling this typeof soft constraints. Conventionally, the fuzzy optimizationproblem in such a setting is defined as the simultaneoussatisfaction of the constraints and the goals. No additionaldistinction is assumed to exist amongst the constraints and thegoals. This report proposes an extension of this model forsatisfying the problem constraints and the goals, where preferencefor different constraints and goals can be specified by thedecision-maker. The difference in the preference for theconstraints is represented by a set of associated weight factors,which influence the nature of trade-off between improving theoptimization objectives and satisfying various constraints.Simultaneous weighted satisfaction of various criteria is modeledby using the recently proposed weighted extensions of(Archimedean) fuzzy t-norms. The weighted satisfaction of theproblem constraints and goals are demonstrated by using a simplefuzzy linear programming problem. The framework, however, is moregeneral, and it can also be applied to fuzzy mathematicalprogramming problems and multi-objective fuzzy optimization.wiskundige programmering;fuzzy sets;optimalisatie

Fuzzy Modeling of Client Preference in Data-Rich Marketing Environments

Advances in computational methods have led, in the world of financial services, to huge databases of client and market information. In the past decade, various computational intelligence (CI) techniques have been applied in mining this data for obtaining knowledge and in-depth information about the clients and the markets. This paper discusses the application of fuzzy clustering in target selection from large databases for direct marketing (DM) purposes. Actual data from the campaigns of a large financial services provider are used as a test case. The results obtained with the fuzzy clustering approach are compared with those resulting from the current practice of using statistical tools for target selection.fuzzy clustering;direct marketing;client segmentation;fuzzy systems

Extended Fuzzy Clustering Algorithms

Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. Ithas been applied successfully in various fields including finance and marketing. Despitethe successful applications, there are a number of issues that must be dealt with in practicalapplications of fuzzy clustering algorithms. This technical report proposes two extensionsto the objective function based fuzzy clustering for dealing with these issues. First, the(point) prototypes are extended to hypervolumes whose size is determined automaticallyfrom the data being clustered. These prototypes are shown to be less sensitive to a biasin the distribution of the data. Second, cluster merging by assessing the similarity amongthe clusters during optimization is introduced. Starting with an over-estimated number ofclusters in the data, similar clusters are merged during clustering in order to obtain a suitablepartitioning of the data. An adaptive threshold for merging is introduced. The proposedextensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resultingextended algorithms are given. The properties of the new algorithms are illustrated invarious examples.fuzzy clustering;cluster merging;similarity;volume prototypes

Cognitive and smart adaptation in computer-communication networks

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A new fuzzy set merging technique using inclusion-based fuzzy clustering

This paper proposes a new method of merging parameterized fuzzy sets based on clustering in the parameters space, taking into account the degree of inclusion of each fuzzy set in the cluster prototypes. The merger method is applied to fuzzy rule base simplification by automatically replacing the fuzzy sets corresponding to a given cluster with that pertaining to cluster prototype. The feasibility and the performance of the proposed method are studied using an application in mobile robot navigation. The results indicate that the proposed merging and rule base simplification approach leads to good navigation performance in the application considered and to fuzzy models that are interpretable by experts. In this paper, we concentrate mainly on fuzzy systems with Gaussian membership functions, but the general approach can also be applied to other parameterized fuzzy sets

Neural Networks for Target Selection in Direct Marketing

Partly due to a growing interest in direct marketing, it has become an important application field for data mining. Many techniques have been applied to select the targets in commercial applications, such as statistical regression, regression trees, neural computing, fuzzy clustering and association rules. Modeling of charity donations has also recently been considered. The availability of a large number of techniques for analyzing the data may look overwhelming and ultimately unnecessary at first. However, the amount of data used in direct marketing is tremendous. Further, there are different types of data and likely strong nonlinear relations amongst different groups within the data. Therefore, it is unlikely that there will be a single method that can be used under all circumstances. For that reason, it is important to have access to a range of different target selection methods that can be used in a complementary fashion. In this respect, learning systems such as neural networks have the advantage that they can adapt to the nonlinearity in the data to capture the complex relations. This is an important motivation for applying neural networks for target selection. In this report, neural networks are applied to target selection in modeling of charity donations. Various stages of model building are described by using data from a large Dutch charity organization as a case. The results are compared with the results of more traditional methods for target selection such as logistic regression and CHAID.neural networks;data mining;direct mail;direct marketing;target selection