ORCID

https://orcid.org/0000-0002-4411-8700

Date of Award

Summer 8-15-2018

Author's School

Graduate School of Arts and Sciences

Author's Department

Business Administration

Degree Name

Doctor of Philosophy (PhD)

Degree Type

Dissertation

Abstract

This dissertation studies operational issues in project supply chains and other settings. All research problems investigated in this dissertation originate from real world problems, and our results provide immediate guidance to help address these issues.

In the first chapter, "Managing Inventory and Time Buffers in a Two-Stage Project Supply Chain", we study the material inventory management problem in a two-stage project supply chain. In our model, a manufacturer has to deliver a designated project to a client at an agreed upon time. The project is subject to delay cost where project delay is caused by unpredictable level of work which consumes materials. We analyze material planning decisions within the supply chain and characterize deployment of inventory and time buffers in a two-stage supply chain under a flexible wholesale price contract setting.

In the second chapter, "Value of Operational Flexibility in Co-Production Systems with Yield and Demand Uncertainty", we study the production decisions of a firm that operates a co-production system (single input but multiple simultaneous outputs) with random yield and demand. With the goal of maximizing expected profits, we formulate a three stage stochastic programming problem, to investigate the value of intermediate upgrading flexibility. Using a stylized model of two products and two markets with different quality requirements, we characterize the optimal decisions and show that the quality upgrading policy is of a single threshold type. Although upgrading is costly, it creates value for the firm through reducing total production cost and better managing the yield uncertainty. Positive correlation between demands of the end-markets decreases the value of upgrading. The more general model formulation offers the blueprint of a stochastic programming model that can be solved for realistic applications.

In the third chapter, "Trailer Selection and Dynamic Matching Problem", we study the Anheuser Busch lnbev trailer problem which consists of selecting trailer types, choosing trailer inventories and then matching available trailers to a sequence of randomly arriving trucks dynamically. We develop two approaches which simultaneously address all three problems under two different settings that are distinguished by whether or not trailer alternations are allowed during the matching process. We characterize the optimal matching policies for both settings and develop efficient approaches to the trailer type and inventory selection problems. Our solution approach includes piece-wise linear approximations and constraint generations methods borrowed from the revenue management literature, which are adapted to our model by exploiting special properties of the optimal matching policy. This approach ultimately gives rise to a linear program which encompasses all three problems simultaneously. We show that this linear program can be efficiently solved and it provides a tight upper bound on the optimal revenue. Using historical truck arrival data and cost parameters, our computational

experiments show that our proposed approaches produce solutions that are within 1 % of optimal.

Language

English (en)

Chair and Committee

Panos Kouvelis

Committee Members

Lingxiu Dong, Jacob Feldman, John Nachbar, Danko Turcic,

Comments

Permanent URL: 2020-07-19

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