Exploiting max-sum for the decentralized assembly of high-valued supply chains

Supply Chain Formation involves determining the participants and the exchange of goods within a production network. Today’s companies operate autonomously, making local decisions, and coordinating with other companies to buy and sell goods along their Supply Chains. Such temporal interactions need to be formed rapidly and in a decentralized manner. For sufficiently large problems, current state-of-the-art approaches for Decentralized Supply Chain Formation are only capable of either (i) producing Supply Chains of high value at the expense of high resource requirements; or (ii) require low resources at the expense of producing Supply Chains of low value. In this paper we describe an algorithm that is able to produce Supply Chains of high value while keeping a low resource usage profile. Moreover, our method is able to produce near optimal Supply Chains while using up to four orders of magnitude less resources that the state-of-the-art.

[1]  Meritxell Vinyals,et al.  A Scalable Message-Passing Algorithm for Supply Chain Formation , 2012, AAAI.

[2]  Meritxell Vinyals,et al.  Worst-case bounds on the quality of max-product fixed-points , 2010, NIPS.

[3]  Michael Winsper,et al.  Decentralised Supply Chain Formation: A Belief Propagation-based Approach , 2010, ECAI.

[4]  Richard S. Zemel,et al.  HOP-MAP: Efficient Message Passing with High Order Potentials , 2010, AISTATS.

[5]  Brendan J. Frey,et al.  A Binary Variable Model for Affinity Propagation , 2009, Neural Computation.

[6]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[7]  Nicholas R. Jennings,et al.  Decentralised coordination of low-power embedded devices using the max-sum algorithm , 2008, AAMAS.

[8]  J. Rodríguez-Aguilar,et al.  Bidding Languages and Winner Determination for Mixed Multi-unit Combinatorial Auctions , 2007, EUMAS.

[9]  H. Keselman,et al.  Modern robust data analysis methods: measures of central tendency. , 2003, Psychological methods.

[10]  Michael P. Wellman,et al.  Decentralized Supply Chain Formation: A Market Protocol and Competitive Equilibrium Analysis , 2003, J. Artif. Intell. Res..

[11]  Moshe Babaioff,et al.  Incentive-compatible, budget-balanced, yet highly efficient auctions for supply chain formation , 2003, EC '03.

[12]  Maria L. Gini,et al.  A Multi-Agent Negotiation Testbed for Contracting Tasks with Temporal and Precedence Constraints , 2002, Int. J. Electron. Commer..

[13]  Moshe Babaioff,et al.  Concurrent auctions across the supply chain , 2001, EC '01.

[14]  Makoto Yokoo,et al.  On Market-Inspired Approaches to Propositional Satisfiability , 2001, IJCAI.

[15]  William T. Freeman,et al.  On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs , 2001, IEEE Trans. Inf. Theory.

[16]  Michael P. Wellman,et al.  Combinatorial auctions for supply chain formation , 2000, EC '00.

[17]  Michael P. Wellman,et al.  Flexible double auctions for electronic commerce: theory and implementation , 1998, Decis. Support Syst..

[18]  Pedro Meseguer,et al.  A test suite for the evaluation of mixed multi-unit combinatorial auctions , 2008, J. Algorithms.

[19]  Michael P. Wellman,et al.  Market protocols for decentralized supply chain formation , 2001 .

[20]  Yair Weiss,et al.  Correctness of Local Probability Propagation in Graphical Models with Loops , 2000, Neural Computation.