Professor Rummel joined the Goizueta Business School in the Spring of 2009. He received his PhD from the University of Rochester and then served on the faculties of the Fuqua School of Business at Duke University and in the School of Business at the University of Connecticut. Jeff has taught courses in operations management, operations strategy, management science and statistics. He was the founding director of the Management & Engineering for Manufacturing program at UConn (a joint degree program with the School of Engineering) and also served as Associate Dean for Undergraduate Programs and Administration at UConn from 2001 through 2006, and then directed the Honors and Study Abroad program for the School. As part of that effort, Jeff created and taught a summer study abroad program in Florence, Italy where students studied operations strategy by visiting Italian manufacturing companies in and around Tuscany.
Jeff is interested in the application of economics and quantitative methods to problems of production and project management. Within this area of interest, he has worked on projects involving the management of advanced manufacturing technology, scheduling and planning systems, project design and control, manufacturing strategy, the interaction between operations and managerial accounting, and process control and improvement (quality, six sigma, lean manufacturing).
Areas of Expertise (3)
Manufacturing planning and control
Service operations and strategy
University of Rochester: PhD, Operations Management 1989
University of Rochester: MS, Operations Research 1985
Capital University: BA, Business Administration 1981
Media Appearances (1)
Faculty, staff honored with year-end awards
Each year numerous awards are bestowed on faculty members at Goizueta Business School with emphasis on their roles in the classroom. For the 2013-2014 academic year, professors from multiple academic areas and programs were honored. Allison Burdette, Assistant Professor in the Practice of Business Law, received The Marc F. Adler Prize for Excellence in Teaching. This award honors outstanding teaching quality, course innovation and relevance to real-world problem solving in all Goizueta Business School programs. This spring, J.B. Kurish, Associate Professor in the Practice of Finance, received the Emory Williams Teaching Award. This is the oldest teaching award at the university. Nominations are made by a committee, reviewed by the Dean’s Office and submitted to the Provost for approval. Crystal Apple Teaching Awards honor Emory faculty members for outstanding achievements in teaching. Faculty members are nominated and selected by students.
Order picking in conventional warehouse environments involves determining a sequence in which to visit the unique locations where each part in the order is stored, and thus is often modeled as a traveling salesman problem. With computer tracking of inventories, parts may now be stored in multiple locations, simplifying replenishment of inventory and eliminating the need to reserve space for each item. In this environment, order picking requires choosing a subset of the locations that store an item to collect the required quantity. Thus, both the assignment of inventory to an order and the associated sequence in which the selected locations are visited affect the cost of satisfying an order. We formulate a model for simultaneously determining the assignment and sequencing decisions, and compare it to previous models for order picking. The complexity of the order picking problem is discussed, and an upper bound on the number of feasible assignments is established. Several extensions of TSP heuristics to the new problem setting and a tabu search algorithm are presented and experimentally tested.
Traditional scheduling models have emphasized sequencing of tasks at machines. At multiple-machine work-centers mean flow times are affected by the allocation of work to machines, as well as the batch-sizes used for processing. An efficient algorithm for computing the optimal solution for the single product case is given. An approximate closed-form solution is available that could be used as a heuristic loading-rule in a dynamic environment. For the special case of equal setup times at machines, the optimal solution is given in closed form. Further, it is demonstrated that the optimal flow time for the work center depends only on the total processing rate at that center.
Machine sequencing formulations typically assume that the characteristics of the job set to be processed are given. However, in many applications, jobs can be batched in different ways. Two types of minimum flow-time sequencing problems with lot sizing are formulated. The “item-flow” formulation is solved giving an index rule for ordering items. The “batch-flow” formulation is solved for the single product case showing that processing is done in unequal batches ordered in decreasing size. Heuristics and bounds are provided for the multiproduct case.