An agent-based production control framework for collaboration in supply chain management
Abstract
This research focuses on constructing an agent-based collaborative production control framework capable of conducting real-time scheduling and dispatching functions among production entities as well as within them in a supply chain. This control framework utilizes autonomous agent and weighted functions for distributed decision-making while all control entities work in an active and collaborative way to help each other in making decisions. The control framework is highly flexible because all the agents are constructed with an object-oriented perspective so that they can join or depart from the control scheme without affecting the rest of the control framework. This collaborative control framework is capable of realizing and seeking a balance among heterogeneous objectives of the supply chain system and the production entities within the system. Simple index values, instead of detailed data, are used for information exchange among agents. This research emphasizes collaboration among production entities to synchronize the production of all sub-assemblies of a make-to-order product when the due date needs to be met. This was achieved through coordinating the frontloading time of sub-assemblies and dynamically assigning priorities to the sub-assemblies at each stage of the production process. Simulation models of a real-world multi-line elevator manufacturing supply chain system were developed as a test bed. Two scenarios, a vertically integrated supply chain and multiple company supply chain, were examined. Control strategies within three and two different levels of collaboration were applied to these scenarios, respectively, to compare and evaluate the performance of the proposed control strategy. Sensitivity analyses were performed to demonstrate the ability of the proposed control strategy to improve system performances under different levels of machine downtime, which is one of the major system uncertainty factors.
Degree
Ph.D.
Advisors
Yih, Purdue University.
Subject Area
Industrial engineering
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