Industry Research Projects – C4iSN

A major challenge for the industry involves the booking of passengers while factoring in the booking of cargo. In passenger booking airlines try to balance expected need, seating capacity and the actual number of passengers who show for a flight. Booking cargo is more complicated because it must adhere to weight guidelines while also considering volume. Too many heavy boxes create issues, as do too many big boxes not fit in the cargo hold.

“Most of what had been done in this area focused on these weight and volume variable separately and were too simplistic,” says Dr. Kasilingam. “My goals going in were to advance the state of the art and adapt our findings into a commercialized product. After studying data, Dr. Cakanyildirm developed estimates about cargo volumes and weight, deduced density from those estimates and started developing an optimization procedure to find a way to make good decisions in the process.”

“It is an important problem, and no one had examined it as we did,” he says. “In very simple terms, we have told the industry some things about their cargo problem that they didn’t know before.”

Blockbuster Inc., (was) a major player in the retail, rental DVD and game media industry, has developed a highly specialized distribution network to perform packaging and distribution services for a large number of products that have short life cycles. In this environment, the number of products and the volume per product has significant week-to-week volatility and short lead times due to manufacturing delays from the suppliers and strict in-store due-date requirements. The processing and packing operations are scheduled at the distribution center through multiple processing departments, which compete for subsequent merge conveyors and sortation systems.

In this research, a Mixed Integer Programming model was developed and implemented to schedule Blockbuster’s short-range order processing operations. “As a result we have seen that the model is effective in not only reducing production and transportation costs but is also very effective in in creasing capacity by eliminating many of the activity “spikes” in key production areas which were prevalent in previous scheduling processes. Year to date for the current year we are on track to realize labor savings of over $1.5 million, transportation savings of $ 322,000 with an increase in overall system capacity of 18 %”, William Wissing, Senior Vice President Operations. The application of this model in other industries which compete for subsequent shared resources such as merge conveyors and sortation systems is also discussed. This research was selected as a Finalist, Daniel H. Wagner Prize for Excellence in Operations Research Practice Award (INFORMS), 2010.

The Federal Reserve System of the United States was considering making changes to its cash recirculation policy to reduce depository institutions’ (banks’) overuse of its cash processing services. These changes will affect operating policies and costs at many institutions having large cash businesses and, in turn, impact cash transportation and logistics providers. This study, performed jointly with The Brink’s Company, discusses the cash supply chain structure and analyzes it as a closed-loop supply chain. Additionally, it describes the cash flow management system used by banks in the U.S. We propose strategies to manage the cash supply chain and develop a modeling framework to evaluate the costs of these and other strategies that banks may use in response to upcoming changes in the Federal Reserve System’s policy.

We optimize a large country’s currency supply network for its central bank. The central bank provides currency to all branches (who in turn serve consumers and commerce) through its network of big vaults, regional vaults, and retail vaults. The central bank intends to reduce its total transportation cost by enlarging a few retail vaults to regional vaults. It seeks further reductions by optimizing the sourcing in the updated currency network.

We develop an optimization model to select the retail vaults to upgrade so that the total cost is minimized. Optimally choosing which retail vaults to upgrade is strongly NP-hard, so we develop an efficient heuristic that provides solutions whose costs average less than 3% above the optimum for realistic problem instances. For one of the test states alone, which was used for validation, upgrading select vaults provided potential annual savings of 1.1 million..

This project was motived from a capstone project sponsored by Dell. When a make-to-order manufacturing company adopts a commit-to-delivery business mode, it commits a delivery due date for an order and is responsible for the shipping cost. Without loss of generality, we consider that transportation is done by a third party logistics company such as FedEx or UPS, which provides multiple shipping modes such as overnight, one-day, two-day delivery, and more. When the transportation time has to be short, clearly shipping cost is more expensive than it could have been. How should a company schedule production for accepted orders so that the company can leave enough transportation time for orders to take slow shipping modes to reduce the shipping cost? We study this problem of integrating the production and transportation functions for a manufacturing company producing a variety of customized products in a make-to-order environment with a commit-to-delivery mode of business.

Various realistic scenarios are investigated, in increasing order of complexity. When customers allow partial delivery, we provide both an MIP model and a minimum cost flow model. We show that non preemptive EDD production schedules are optimal when partial delivery is allowed and shipping cost is a decreasing convex function with transportation time. When partial delivery is not allowed, we develop an MIP model and prove that the problem is NP-hard. An efficient heuristic algorithm with polynomial computation time is provided for the NP-hard problem. It gives near- optimal production schedules, as shown via thousands of numerical experiments. We also provide models and analysis for other scenarios where shipping cost accounts for customer locations and quantity discounts.

Furnaces, Air conditioners, Coil blowers and Heat pumps are major heating, ventilation and air conditioning (HVAC) products. The HVAC manufacturers whose total U.S. revenues are about 18 billion dollars search for better ways of forecasting the sales for HVAC products. One of the ways to improve forecasts is to use easily observable, independent variables that drive the demand. In this research, we investigate the effect of some of these variables which include monthly average cooling degree days and housing starts. By using real-life data, we test the accuracy improvements achieved with these and as well as additional variables such as prices. We eventually suggest a forecasting model that provides a good compromise between complexity and accuracy. The model is implemented in open-source R software which should facilitate its adaptation.