Date of Award

Spring 3-2018

Embargo Period


Degree Type

Dissertation (CMU Access Only)

Degree Name

Doctor of Philosophy (PhD)


Chemical Engineering


Nikolaos Sahinidis


In this dissertation, we consider two specific types of problems over networks. In the first problem, we explore systematic methods to address some of the challenges in largescale maintenance planning in integrated chemical sites. In the second problem, we investigate different optimization model formulations for the design of flow distribution networks where the flow is potential-driven and nonlinearly related to the potential loss. Maintenance turnaround in the processing industry is a complex asset renewal project that includes huge capital expenditures and downtime losses. The option of deferring or rescheduling a turnaround project typically provides immediate financial relief from capital expenditure. However, the risk of running into site-wide disruptions in the form of unplanned events, yield, and reliability losses is not straightforward to assess. We propose mathematical optimization models to evaluate the risk of loss from turnaround deferrals in integrated sites and provide alternatives to reliably operate the site in a medium-term horizon. In the first chapter, we introduce the turnaround planning problem and the challenges it poses in integrated sites. We also introduce the background for the network design problem. In the second chapter, we study the financial impact of rescheduling turnarounds and the associated risk under unplanned outages. We compare the risk profiles presented by different production planning strategies. We propose a stochastic programming model for production planning that optimally builds up inventory ahead of time to hedge against production losses during unplanned outages. In the third chapter, we extend the stochastic optimization to handle a large set of scenarios and propose a Lagrangean decomposition method that improves a myopic production plan. The fourth chapter proposes a mixed-integer linear programming model that prescribes turnaround schedules when the underlying assets undergo yield loses and selectivity degradation. Here, we also study the impact of deferrals over a long-term horizon. The penultimate chapter addresses the nonlinear network design problem.The closing chapter summarizes the work and provides a few future directions. In the spirit of advancing manufacturing paradigms, the thesis supports investment in modeling efforts that address enterprise-wide planning problems.