A wide variety of optimization and decision-support problem arise in manufacturing and transportation logistics. Factory floor scheduling requires consideration of a number of fundamental objectives including the minimization of production costs, maximizing on-time delivery, minimizing inventory carrying costs, and all the while ensuring that customer order specifications are met within specified limits. Much of my research in past years has focussed on paper mill scheduling, where all of the above problems arise on a daily basis. For example, in trim optimization (see figure below), also known as cutting-stock optimization, the goal is to fulfill customer demand for different sized rolls of paper (within specified tolerances) while maximizing utilization of the reel, minimizing setups (slitter-changes) that incur production delays. Other secondary considerations arise that can nevertheless have a substantial impact on factory-floor optimization. For example, it is generally desirable if any particular order is fulfilled over a relatively short range of trimming patterns as partial orders can cause bottlenecks at the loading docks where vehicles need to be retained while waiting for the completion of an order. Rachlin Research develops optimization engines for solving these types of manufacturing problems. Our current goal is to develop systems capable of generating upwards of billions of different solution alternatives, revealing critical tradeoffs among various competing objectives. We are particularly interested in partnering with ERP and supply-side optimization vendors seeking to develop turn-key software applications for manufacturing industries.