A repeated-negotiation game approach to distributed (re)scheduling of multiple projects using decoupled learning

Abstract

With peer-to-peer software technologies based on Blockchain and Smart Contracts, automated negotiation of client-server relationships for enterprise networking and project-oriented fractal organizations can now be readily implemented. To this aim, the distributed (emergent) schedule of client-server contracts must be a Nash equilibrium from which any agent finds no incentive to deviate. Also, to respond effectively to unplanned events and disturbances, the renegotiating process of all concerned client-server contracts must pursue a new Nash equilibrium solution in the face of incomplete information by each individual agent. In this work, distributed multi-project (re)scheduling is formulated as a repeated-negotiation game in a multi-agent setting where each agent resorts to decoupled learning rules for deciding the terms and conditions in each contract settlement. After a finite number of stages of the negotiation game, a new emergent schedule close to a Nash equilibrium is found. An agent-based simulation framework is proposed to implement the repeated negotiation game based on bilateral client-server contracts. The effectiveness of the proposed approach is demonstrated using a case study of a project-oriented fractal company in the pharmaceutical industry. Simulation results obtained highlight that repeated negotiations and decoupled learning are key for approaching a Nash equilibrium and welfare solutions to a (re)scheduling problem.