Supply chain management under uncertainty

The focus of this thesis is to quantify and analyze supply chain planning decisions under market and/or technical uncertainty. The two key questions that are answered in this work are: (i) how to quantify the impact of uncertainty on supply chain operations and (ii) how to manage these operations to limit the downside risk posed by uncertainty.; To set the stage for including a description of uncertainty in supply chain planning, the first part of the thesis addresses the computational challenge posed by deterministic planning models. In particular, an efficient decomposition procedure for solving midterm planning problems is developed based on Lagrangean relaxation. The basic idea of the proposed solution technique is the successive partitioning of the original problem into smaller, more computationally tractable subproblems by hierarchical relaxation of key complicating constraints.; Subsequently, a two-stage, stochastic programming approach is proposed for incorporating demand uncertainty in multisite midterm supply chain planning problems.; Next, the management of key supply chain issues such as customer service levels and inventory control within the proposed modeling framework is addressed. To safeguard against inventory depletion at the production sites and excessive shortage at the customer, a chance constraint programming approach is embedded within the two-stage framework. The developed methodology is shown to uncover strategic capacity integration options that can help limit the variability in the supply chain and thus lead to robust operation under uncertainty.; The analysis is then extended to account for a multiperiod planning horizon. A multistage stochastic programming approach is used to model the sequence of planning decisions as they react to demand outcomes arising over time. The challenge presented by the computational intractability of the multistage problem, due to the presence of multiple (one for each period) nested optimization problems, is resolved by incorporating partial information about future uncertain events.; Finally, a real options-based valuation (ROV) framework for hedging under uncertainty is developed. The basic idea of the proposed ROV methodology, is the recognition and utilization of external market opportunities for guiding internal planning decisions of a company. This is achieved by applying key financial planning principles such as arbitrage-free pricing and risk-neutral valuation to real, non-financial resource allocation decisions under uncertainty. (Abstract shortened by UMI.)...