Despite the large amounts of runtime needed to adequately solve a combinatorial auction (CA), existing iterative CA auction protocols require winner determination during every round of bid submissions. Using existing algorithms for winner determination will cause a timing bottleneck during the winner determination phase. Furthermore, there has recently been work which models the formation of su...

This paper considers the problem of generating k cheapest solutions to a class of procurement auction winner determination problems. A computationally efficient solution to this problem would enable a fundamentally new approach to decision support for procurement, based on “mining” the k cheapest solutions. However, previous methods do not scale in crucial problem-size parameters, e.g., the num...

We consider the problem of winner determination under Chamberlin–Courant’s multiwinner voting rule with approval utilities. This problem is equivalent to the wellknown NP-complete MaxCover problem (i.e., a version of the SetCover problem where we aim to cover as many elements as possible) and, so, the best polynomial-time approximation algorithm for it has approximation ratio 1 − 1 e . We show ...

Since combinational auctions allow bidders to bid on any combination of goods, bid data can be of exponential size. To combat this problem, restrictions have been proposed limiting the number of bundles a bidder is allowed to bid on (as in FCC Auction #31). We describe a new combinatorial auction format that restricts bidders to submit a fixed number of “matrix bids with order”. The advantage o...

In this paper we introduce a new graph based bidding language for combinatorial auctions. In our language, each bidder submits to the arbitrator a generalized flow network (netbid) representing her bids. The interpretation of the winner determination problem as an aggregation of individual preferences represented as flowbids allows building an aggregate netbid for its representation. Labelling ...

Negotiation events in industrial procurement involving multiple, highly customisable goods pose serious challenges to buying agents when trying to determine the best set of providing agents’ offers. Typically, a buying agent’s decision involves a large variety of constraints that may involve attributes of a very same item as well as attributes of multiple items. In this paper we describe iBundl...

There are many different ways to allocate resources in cloud computing. The economic methods are some of common ways to resource allocation. The auction-based method has some advantages compared with Fixed-Price method. The double combinatorial auction is one of the proper ways of resource allocation in cloud computing. Resource allocation in double combinatorial auction includes two phases. Th...

We extend the IP models proposed by Nisan and Andersson for winner determination in combinatorial auctions, to the problem of evaluating bids for coordinated task sets. This requires relaxing the free disposal assumption, and encoding temporal constraints in the model. We present a basic model, along with an improved model that dramatically reduces the number of rows by preprocessing the tempor...

We study a framework for multiagent resource allocation where autonomous software agents negotiate over the allocation of bundles of indivisible resources. Connections to well-known combinatorial optimisation problems, including the winner determination problem in combinatorial auctions, shed light on the computational complexity of the framework. We give particular consideration to scenarios w...

Shoham In this chapter we consider the empirical hardness of the winner determination problem. We identify distribution-nonspecific features of data instances and then use statistical regression techniques to learn, evaluate and interpret a function from these features to the predicted hardness of an instance, focusing mostly on ILOG's CPLEX solver. We also describe two applications of these mo...