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A three-stage procurement optimization problem under uncertainty

Authors

  • Mike Prince,

    1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida 32611-6595
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  • J. Cole Smith,

    Corresponding author
    1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida 32611-6595
    • Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida 32611-6595
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  • Joseph Geunes

    1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida 32611-6595
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Abstract

This article examines a problem faced by a firm procuring a material input or good from a set of suppliers. The cost to procure the material from any given supplier is concave in the amount ordered from the supplier, up to a supplier-specific capacity limit. This NP-hard problem is further complicated by the observation that capacities are often uncertain in practice, due for instance to production shortages at the suppliers, or competition from other firms. We accommodate this uncertainty in a worst-case (robust) fashion by modeling an adversarial entity (which we call the “follower”) with a limited procurement budget. The follower reduces supplier capacity to maximize the minimum cost required for our firm to procure its required goods. To guard against uncertainty, the firm can “protect” any supplier at a cost (e.g., by signing a contract with the supplier that guarantees supply availability, or investing in machine upgrades that guarantee the supplier's ability to produce goods at a desired level), ensuring that the anticipated capacity of that supplier will indeed be available. The problem we consider is thus a three-stage game in which the firm first chooses which suppliers' capacities to protect, the follower acts next to reduce capacity from unprotected suppliers, and the firm then satisfies its demand using the remaining capacity. We formulate a three-stage mixed-integer program that is well-suited to decomposition techniques and develop an effective cutting-plane algorithm for its solution. The corresponding algorithmic approach solves a sequence of scaled and relaxed problem instances, which enables solving problems having much larger data values when compared to standard techniques. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013

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