Improved opportunity cost algorithm for carrier selection in combinatorial auctions

Transportation costs constitute up to thirty percent of the total costs involved in a supply chain. Outsourcing the transportation service requirements to third party logistics providers have been widely adopted, as they are economically more rational than owning and operating a service. Transportation service procurement has been traditionally done through an auctioning process where the auctioneer (shipper) auctions lanes (distinct delivery routes) to bidders (carriers). Individual lanes were being auctioned separately disallowing the carriers to express complements and substitutes. Using combinatorial auctions mechanism to auction all available lanes together would allow the carriers to take advantage of the lane bundles, their existing service schedule, probability of securing other lanes and available capacity to offer services at lower rates and be more competitive. The winners of the auction are the set of non-overlapping bids that minimize the cost for the shippers. The winner determination problem to be solved in determining the optimal allocation of the services in such kind of combinatorial auctions is a NP-hard problem. Many heuristics like approximate linear programming, stochastic local search have proposed to find an approximate solution to the problem in a reasonable amount of time. Akcoglu et al [22] developed the opportunity cost algorithm using the “local ratio technique” to compute a greedy solution to the problem. A recalculation modification to the opportunity cost algorithm has been formulated where opportunity costs are recalculated every time for the set of remaining bids after eliminating the bid chosen to be a part of the winning solution and its conflicts have eliminated. Another method that formulates the winning solution based on the maximum total revenue values calculated for each bid using the opportunity cost algorithm has also been researched

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I would like to express my sincere gratitude to the members of my thesis committee for their constant guidance and support. I am particularly indebted to my major professor, Dr. Gerald Knapp, for his guidance, support and constant encouragement during the course of this research. I am grateful to Dr. Thomas Ray and Dr. Baba Sarker for serving on my committee and for their suggestions and encouragement. Finally, I am grateful to my parents and my sister for their constant support and encouragement without which this research would not have been possible. I would also like to thank all my friends, especially all the students working in the Systems Integration Lab for their help when needed. i