Mixed Integer Linear Programming Models for Combinatorial Optimization Problems

TL;DR: In this article, the authors present an introduction to Sequencing and Scheduling in the context of the Operational Research Society (ORS) and the International Journal of Distributed Sensor Networks (ILS).

TL;DR: The RAND Corporation in the early 1950s contained “what may have been the most remarkable group of mathematicians working on optimization ever assembled”: Arrow, Bellman, Dantzig, Flood, Ford, Fulkerson, Gale, Johnson, Nash, Orchard-Hays, Robinson, Shapley, Simon, Wagner, and other household names.

TL;DR: This paper discusses how Dantzig-Wolfe decomposition techniques can be applied to alleviate, at least partly, the difficulties associated with the size of time-indexed formulations, and shows that the application of these techniques still allows the use of cut generation techniques.

TL;DR: A new integer linear programming formulation for this min-max problem with a polynomial number of variables and constraints is presented, and it is shown that its LP relaxation provides a lower bound tighter than the classical one.