This dissertation analyzes three scheduling problems motivated by real life situations. In many manufacturing and service industries, scheduling is an important decision-making process. Unfortunately, scheduling problems are often computationally challenging to solve, and even modeling a scheduling problem can be difficult. Chapter 2 considers single-facility non-preemptive scheduling problems ...

We study project scheduling at a large IT services delivery center in which there are unpredictable delays. We apply robust optimization to minimize tardiness while informing the customer of a reasonable worst-case completion time, based on empirically determined uncertainty sets. We introduce a new solution method based on logic-based Benders decomposition. We show that when the uncertainty se...

A facility with front room and back room operations has the option of hiring specialized or, more expensive, cross-trained workers. Assuming stochastic customer arrival and service times, we seek a smallest-cost combination of cross-trained and specialized workers satisfying constraints on the expected customer waiting time and expected number of workers in the back room. A constraint programmi...

We study an unrelated parallel machines scheduling problem with sequence and machine dependent setup times. A logicbased Benders decomposition approach is proposed to minimize the makespan. The decomposition approach is a hybrid model that makes use of a mixed integer programming master problem and a travelling salesman problem subproblem. The master problem is a relaxation of the problem and i...

We study an unrelated parallel machines scheduling problem with sequence and machine dependent setup times. A logicbased Benders decomposition approach is proposed to minimize the makespan. The decomposition approach is a hybrid model that makes use of a mixed integer programming master problem and a travelling salesman problem subproblem. The master problem is a relaxation of the problem and i...

Schedule disruptions are commonplace in the airline industry with many flight-delaying events occurring each day. Recently there has been a focus on introducing robustness into airline planning stages to reduce the effect of these disruptions. We propose a recoverable robustness technique as an alternative to robust optimisation to reduce the effect of disruptions and the cost of recovery. We f...

Title of Document: OPTIMIZATION MODELS AND METHODOLOGIES TO SUPPORT EMERGENCY PREPAREDNESS AND POST-DISASTER RESPONSE Lichun Chen, Doctor of Philosophy, 2010 Directed By: Associate Professor, Elise Miller-Hooks, Department of Civil and Environmental Engineering This dissertation addresses three important optimization problems arising during the phases of pre-disaster emergency preparedness and ...

We study an adaptive partition-based approach for solving two-stage stochastic programs with fixed recourse. A partition-based formulation is a relaxation of the original stochastic program, and we study a finitely converging algorithm in which the partition is adaptively adjusted until it yields an optimal solution. A solution guided refinement strategy is developed to refine the partition by ...

In this thesis, a Benders decomposition algorithm is designed and implemented to solve both deterministic and stochastic pooling problems to global optimality. Convergence of the algorithm to a global optimum is proved and then it is implemented both in GAMS and C++ to get the best performance. A series of example problems are solved, both with the proposed Benders decomposition algorithm and c...

We apply logic-based Benders decomposition (LBBD) to two-stage stochastic planning and scheduling problems in which the second stage is a task. solve master problem with mixed integer/linear programming subproblem constraint programming. As cuts, we use simple no-good cuts as well analytic develop for this application. find that LBBD computationally superior integer L-shaped method. In particul...