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Publication Date



UM campus only

Degree Type


Degree Name

Doctor of Philosophy (PHD)


Industrial Engineering (Engineering)

Date of Defense


First Committee Member

Sohyung Cho - Committee Chair

Second Committee Member

Murat Erkoc - Committee Member

Third Committee Member

Shihab Asfour - Committee Member

Fourth Committee Member

Moiez Tapia - Outside Committee Member


The first primary objective of this dissertation is to develop a framework that can quantitatively measure complexity of manufacturing systems in various configurations, including conjoined and disjoined systems. In this dissertation, an analytical model for manufacturing systems complexity that employs information entropy theory is proposed and verified. The model uses probability distribution of information regarding resource allocations that are described in terms of interactions among resources for part processing and part processing requirements. In the proposed framework, both direct and indirect interactions among resources are modeled using a matrix, called interaction matrix, which accounts for part processing and waiting times. The proposed complexity model identifies a manufacturing system that has evenly distributed interactions among resources as being more complex, because under disruption situation more information is required to identify source of the disruption. In addition, implicit relationships between the system complexity and performance in terms of resource utilizations, waiting time, cycle time and throughput of the system are studied in this dissertation by developing a computer program for simulating general job shop environment. The second primary objective of this dissertation is to develop a mathematical model for measuring the vulnerability of the supply chain systems. Global supply chains are exposed to different kinds of disruptions. This has promoted the issue of supply chain resilience higher than ever before in business as well as supporting agendas. In this dissertation, an extension of the proposed measure for manufacturing system complexity is used to measure the vulnerability of the supply chain systems using information entropy theory and influence matrix. We define the vulnerability of supply chain systems based on required information that describes the system in terms of topology and interrelationship among components. The proposed framework for vulnerability modeling in this dissertation focus on disruptive events such as natural disasters, terrorist attacks, or industrial disputes, rather than deviations such as variations in demand, procurement and transportation.


Supply Chain Resilience; Vulnerability; Part Mix Ratio; Resource Interactions; Information Entropy; Complexity