Opportunistic Scheduling and Pricing Strategies for Automated Contracting in Supply Chains

Supply chains form an integral cornerstone of the daily operations of today’s business enterprises. The effectiveness of the processes underlying the supply chain determine the steady-flow of raw material and finished products between entities in the marketplace. Electronic institutions facilitate free and fair competition between providers and suppliers vying for contracts from customers and manufacturers. We assume that contracts are awarded via competitive auction-based protocols. We believe that the efficiency of the scheduling and pricing strategies of the suppliers play an important role in their profitability in such a competitive supply chain. The suppliers can decide their scheduling strategy for completing the contracted tasks depending on their capacity, the nature of the contracts, the profit margins and other commitments and expectations about future contracts. Such decision mechanisms can incorporate task features including length of the task, priority of the task, scheduling windows, estimated future load, and profit margins. Robust, opportunistic task scheduling strategies can significantly improve the competitiveness of suppliers by identifying market niches and strategically positioning available resources to exploit such opportunities. Effective price adjustment mechanisms are also required to maximally exploit such market opportunities.

[1]  Jean Carletta,et al.  Communication in Virtual Supply Chain Teams , 1998, PROLAMAT.

[2]  Benoît Montreuil,et al.  A Heterogeneous Multi-agent Modelling for Distributed Simulation of Supply Chains , 2003, HoloMAS.

[3]  David W. Hildum,et al.  MASCOT: An Agent-based Architecture for Coordinated Mixed-Initiative Supply Chain Planning and Scheduling , 1999 .

[4]  Morten Lind,et al.  Holonic and Multi-Agent Systems for Manufacturing , 2005 .

[5]  Victor R. Lesser,et al.  Leveled-Commitment Contracting: A Backtracking Instrument for Multiagent Systems , 2002, AI Mag..

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

[7]  Stephen F. Smith,et al.  Modeling the Dynamics of Supply Chains , 1994 .

[8]  Sandip Sen,et al.  Opportunistic Scheduling and Pricing in Supply Chains , 2004, Künstliche Intell..

[9]  Jayashankar M. Swaminathan,et al.  Modeling Supply Chain Dynamics: A Multiagent Approach , 1998 .

[10]  Mark S. Fox,et al.  Coordinating multiple agents in the supply chain , 1996, Proceedings of WET ICE '96. IEEE 5th Workshop on Enabling Technologies; Infrastucture for Collaborative Enterprises.

[11]  Nesime Tatbul,et al.  An open electronic marketplace through agent-based workflows: MOPPET , 2000, International Journal on Digital Libraries.

[12]  Timothy W. Finin,et al.  A negotiation-based Multi-agent System for Supply Chain Management , 1999 .

[13]  Sandip Sen,et al.  Aggressive pricing to exploit market niches in supply chains , 2005, IEEE Intelligent Systems.

[14]  Weiming Shen,et al.  An Agent-Based Approach for Manufacturing Enterprise Integration and Supply Chain Management , 1998, PROLAMAT.

[15]  Nicholas R. Jennings,et al.  Applied Artificial Intelligence: An International Journal , 2022 .