Solving large, complex optimization problems can be a daunting task. Conquering them effectively, however, can be the difference between success and failure in
today’s highly competitive marketplace. As problems grow in size and scope, it becomes increasingly difficult to get answers in a timely manner. Recent improvements in optimization solver engine performance have not been sufficient to deal with the challenge of these increasingly complex problems. In many cases, optimization problems are too large to fit into memory, require too much time to compute or are simply too hard to solve.
For these reasons, simply boosting the computational horsepower is often not enough. As one of the ways to address the scalability issue, next-generation solver
engines must look for ways to decompose problems into smaller, more manageable components. Doing so will allow even the most complex problems to be solved so that key tactical, strategic and operational decisions can still be made with confidence. Organizations that solve problems efficiently at all levels of complexity have a unique competitive advantage.