Supply Chain Engineering and Optimization Laboratory (SCEOL)
SCEOL is a knowledge center where theoretical and applied research is conducted as it relates to problems in the processes that convert raw materials into finished goods and then deliver these goods to customers. Research activities frequently involve mathematical modeling and computational optimization.
Brochure (PDF File)
The following project descriptions are provided to illustrate the types of activities in SCEOL. Please note that these do not represent in any way the scope of activities undertaken within the lab; rather, they are intended to provide a flavor of some past work supported by industry and governmental sponsors.
Design of the Decision Support Tool for Tactical Scheduling (industry funded)
This research project was aimed at providing decision support for shop schedulers faced with assigning tasks to employees. In this situation, a number of tasks were always waiting to be performed and each had the potential to impact corporate profit; however, there was no universal agreement on which tasks should be performed first so the shop scheduler was faced with making assignments based on intuition and the persuasive powers of the task owners. Further, not all employees were qualified for all tasks, so the scheduling task was quite complex for someone armed with one common sense and intuition. The SCEOL team used optimization techniques and a spreadsheet interface to create a user friendly solution to the problem.
Next Generation Production Logistics Systems (government funded)
As supply chain strategies are becoming more common in industry, a number of theoretical issues are being uncovered. Two that we began to initially investigate in this project areas are multiobjective scheduling and integrated optimization models that include more than one element of the supply chain. Results suggest that both of these research areas have the potential to providing tremendous competitive advantages by reducing costs and cycle time; however, much work is left to be done in both the theoretical underpinnings and implementation issues.
Total Quality Methods and Technology Partnerships (government funded)
In an effort to become more efficient, one of the national labs decided to implement total quality methods in nontraditional areas. Faculty and students in SCEOL developed statistically valid techniques for a variety of processes, provided implementation via spreadsheets, and conducted training to insure the systems were correctly used and updated. The results were stable and measurable processes that were then capable of being improved to achieve stated goals.
Design of Improved Logistics Methodologies (industry funded)
Companies that try to implement supply chain ideas in complex assembly processes invariably encounter the same problem. Materials requirements planning (MRP) is necessary to organize production but experience with MRP dictates long cycle times and high inventory levels - two results are in direct conflict with the supply chain ideas. This industrial sponsor requested that we address this problem in their setting. The scope of activities involved engineering analysis and modeling, the latter including mining data to support the modeling efforts. The results were implemented in a spreadsheet format so that the employees tasked with executing the orders from the MPR system simply used the MRP output as input to the model that, in turn, provided a recommended course of action.
Ferrell, Jr., W. G., J. Sale, J. Sams and M. Yellamraju (2000). Evaluation of Simple Scheduling Rules in Realistic Shops. Computers and Industrial Engineering
D’Souza, A. and W. G. Ferrell, Jr. (2001). Quality Control of Continuous Flow Processes. International Journal of Production Research
Sengupta, S., R. P. Davis and W. G. Ferrell, Jr. (1993). Production Planning and Control in a JIT Environment. Journal of Applied Mathematical Modeling
Erol, I. and W. Ferrell, Jr. (2003). A Supply Chain Approach to the Industrial Distributor’s Problem. International Journal of Production Economics.
Rangsaritratsamee, R. and W. G. Ferrell Jr., and M. E. Kurz, Dynamic Rescheduling using a Bicriteria Objective with Genetic Local Search. Computers and Industrial Engineering.
Fink, M. and W. G. Ferrell Jr. (2002). Inventory Policy for Products with Short Life Cycles. Proceeding of the 11th Industrial Engineering Research Conference, Orlando.