153 research outputs found

    Multiattribute electronic procurement using goal programming

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    One of the key challenges of current day electronic procurement systems is to enable procurement decisions transcend beyond a single attribute such as cost. Consequently, multiattribute procurement have emerged as an important research direction. In this paper, we develop a multiattribute e-procurement system for procuring large volume of a single item. Our system is motivated by an industrial procurement scenario for procuring raw material. The procurement scenario demands multiattribute bids, volume discount cost functions, inclusion of business constraints, and consideration of multiple criteria in bid evaluation. We develop a generic framework for an e-procurement system that meets the above requirements. The bid evaluation problem is formulated as a mixed linear integer multiple criteria optimization problem and goal programming is used as the solution technique. We present a case study for which we illustrate the proposed approach and a heuristic is proposed to handle the computational complexity arising out of the cost functions used in the bids

    Design and Evaluation of Feedback Schemes for Multiattribute Procurement Auctions

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    Multiattribute auctions, which allow bids on multiple dimensions of the product, are IT-enabled sourcing mechanisms that increase the efficiency of procurement for configurable goods and services compared to price-only auctions. Given the strategic nature of procurement auctions, the amount of information concerning the buyer’s preferences that is disclosed to the suppliers has implications on the profits of the buyer and suppliers and, consequently, on the long-term relationship between them. This study develops novel feedback schemes for multiattribute auctions that protect buyer’s preference information from the supplier and suppliers’ cost schedule from the buyer. We conduct a laboratory experiment to study bidder behavior and profit implications under three different feedback regimes. Our results indicate that bidders are able to extract more profit with more information regarding the state of the auction in terms of provisional allocation and prices. Furthermore, bidding behavior is substantially influenced by the nature and type of feedback

    Coordinating service composition

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    The fundamental paradigm shift from traditional value chains to agile service value networks implies new economic and organizational challenges. As coordination mechanisms, auctions have proven to perform quite well in situations where intangible and heterogeneous goods are traded. Nevertheless traditional approaches in the area of multiattribute combinatorial auctions are not quite suitable to enable the trade of composite services. A flawless service execution and therefore the requester\u27s valuation highly depends on the accurate sequence of the functional parts of the composition, meaning that in contrary to service bundles, composite services only generate value through a valid order of their components. We present an abstract model as a formalization of a service value network. The model comprehends a graph-based mechanism design to allocate multiattribute service offers within the network, to impose penalties for non-performance and to determine prices for complex services. The mechanism and the bidding language support various types of QoS attributes and their (semantic) aggregation. We analytically show that this variant is incentive compatible with respect to all dimensions of the service offer (quality and price)

    Information Disclosure in Open Non-Binding Procurement Auctions: an Empirical Study

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    The outcome of non-binding reverse auctions critically depends on how information is distributed during the bidding process. We use data from a large European procurement platform to study the impact of different information structures, specifically the availability of quality information to the bidders, on buyers' welfare and turnover of the platform. First we show that on the procurement platform considered bidders indeed are aware of their rivals' characteristics and the buyers preferences over those non-price characteristics. In a counterfactual analysis we then analyze the reduction of non-price information available to the bidders. As we find, platform turnovers in the period considered would decrease by around 30%, and the buyers' welfare would increase by the monetary equivalent of around 45% of turnover of the platform

    Crowd-sourcing with uncertain quality - an auction approach

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    This article addresses two important issues in crowd-sourcing: ex ante uncertainty about the quality and cost of different workers and strategic behaviour. We present a novel multi-dimensional auction that incentivises the workers to make partial enquiry into the task and to honestly report quality-cost estimates based on which the crowd-sourcer can choose the worker that offers the best value for money. The mechanism extends second score auction design to settings where the quality is uncertain and it provides incentives to both collect information and deliver desired qualities

    Structured Preference Representation and Multiattribute Auctions

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    Handling preferences over multiple objectives (or attributes) poses serious challenges to the development of automated solutions to complex decision problems. The number of decision outcomes grows exponentially with the number of attributes, and that makes elicitation, maintenance, and reasoning with preferences particularly complex. This problem can potentially be alleviated by using a factored representation of preferences based on independencies among the attributes. This work has two main components. The first component focuses on development of graphical models for multiattribute preferences and utility functions. Graphical models take advantage of factored utility, and yield a compact representation for preferences. Specifically, I introduce CUI networks, a compact graphical representation of utility functions over multiple attributes. CUI networks model multiattribute utility functions using the well studied utility independence concept. I show how conditional utility independence leads to an effective functional decomposition that can be exhibited graphically, and how local conditional utility functions, depending on each node and its parents, can be used to calculate joint utility. The second main component deals with the integration of preference structures and graphical models in trading mechanisms, and in particular in multiattribute auctions. I first develop multiattribute auctions that accommodate generalized additive independent (GAI) preferences. Previous multiattribute mechanisms generally either remain agnostic about traders’ preference structures, or presume highly restrictive forms, such as full additivity. I present an approximately efficient iterative auction mechanism that maintains prices on potentially overlapping GAI clusters of attributes, thus decreasing elicitation and computation burden while allowing for expressive preference representation. Further, I apply preference structures and preference-based constraints to simplify the particularly complex, but practically useful domain of multi-unit multiattribute auctions and exchanges. I generalize the iterative multiattribute mechanism to a subset of this domain, and investigate the problem of finding an optimal set of trades in multiattribute call markets, given restrictions on preference expression. Finally, I apply preference structures to simplify the modeling of user utility in sponsored-search auctions, in order to facilitate ranking mechanisms that account for the user experience from advertisements. I provide short-term and long-term simulations showing the effect on search-engine revenues.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61670/1/yagil_1.pd

    Optimization Based e-Sourcing

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    Efficient multi-unit procurement mechanism with supply disruption risk

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    In this paper, we study the multi-attribute multi-unit procurement mechanism design problem facing a set of potential suppliers who suffer from disruption risks. Each supplier's production cost depends on its disruption probability, and both are private information. We propose a Vickery-Clark-Groves auction with disruption risk (VCG-DR) for this problem and show that the mechanism is incentive-compatible, individual-rational and social efficient. Moreover, we compare the performance of the proposed mechanism and the popular single-attribute multi-unit forward auction (SA-MFV) with reserved attribute by numerical experiments. The results show that VCG-DR outperforms SA-MFV in both social efficiency and optimality.Peer ReviewedPostprint (author's final draft
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