Learn More
The Traveling Salesman Problem involves nding the shortest route between a number of cities. This route must visit each of the cities exactly once and end in the same city as it started. As easy as it is to describe, this problem is notoriously diicult to solve. It is widely believed that there is no eecient algorithm that can solve it accurately. On the(More)
Dynamic programming, branch-and-bound, and constraint programming are the standard solution principles for finding optimal solutions to machine scheduling problems. We propose a new hybrid optimization framework that integrates all three methodologies. The hybrid framework leads to powerful solution procedures. We demonstrate our approach through the(More)
While the process of IMRT planning involves optimization of the dose distribution, the procedure for selecting the beam inputs for this process continues to be largely trial-and-error. We have developed an integer programming (IP) optimization method to optimize beam orientation using mean organ-at-risk (MOD) data from single-beam plans. Two test cases were(More)
On the equivalence of the max-min transportation lower bound and the time-indexed lower bound for single-machine scheduling problems Abstract New observations are made about two lower bound schemes for single-machine min-sum scheduling problems. We find that the strongest bound of those provided by transportation problem relaxations can be computed by(More)
Partition-based random search (PRS) provides a class of effective algorithms for global optimization. In each iteration of a PRS algorithm, the solution space is partitioned into subsets which are randomly sampled and evaluated. One subset is then determined to be the promising subset for further partitioning. In this paper, we propose the problem of(More)
We develop a financial model for a manufacturing process where quality can be affected by an assignable cause. We evaluate the options associated with applying a statistical process control chart using pentanomial lattice and Monte Carlo simulation methods. By connecting the aspects of market dynamics with the manufacturing operational aspects, we now have(More)
Large-scale discrete optimization problems are difficult to solve, especially when different kinds of real constraints are considered. Conventionally, standard mathematical programming is a general approach for discrete optimization, but may suffer from the unacceptable long solution time in applications. On the other hand, some heuristics/metaheuristics(More)