IE Decisions System Engineering Fall ‘16 Seminar Series
October 28 at 12PM in BYENG 210
Production Planning with Price-Dependent Supply Capacity
Joint work with Z. Melis Teksan, Ozyegin University Industrial Engineering Department
Abstract
Joseph Geunes
Professor
University of Arkansas in
Fayetteville, Arkansas
Biography
Joseph Geunes is a Professor and John
and Mary Lib White Systems Integration
Chair in Industrial Engineering at the
University of Arkansas in Fayetteville,
Arkansas. Prior to joining the University
of Arkansas in 2016, he was a faculty
member in Industrial and Systems
Engineering for 18 years at the University
of Florida. Professor Geunes's research
focuses on production and logistics
planning, supply chain management, and
operations research. He received his PhD
in 1999 from Penn State, and was
designated as a Fellow of the Institute of
Industrial Engineers in 2015. He has coauthored more than 60 scholarly articles
published in peer-reviewed journals
including Operations Research,
Mathematical Programming, M&SOM,
Naval Research Logistics, and IIE
Transactions. In addition, he has coedited four books, authored two books,
and is currently a Department Editor for
IISE Transactions and Area Editor for
Omega.
We consider a production planning problem
in which a producer procures an input
component for production by offering a
price to suppliers. The available supply
quantity for the production input depends
on the price the producer offers, and this
supply level constrains production output.
The producer seeks to meet a set of
demands over a finite horizon at a minimum
cost, including component procurement
costs. We model the problem as a discretetime production and component supplypricing
planning
problem
with
nonstationary costs, demands, and
component supply levels. This leads to a
two-level lot-sizing problem with an
objective function that is neither concave
nor convex. Although the most general
version of the problem is NP-hard, we
provide polynomial-time algorithms for two
special cases of the model under particular
assumptions on the cost structure. We then
apply the resulting algorithms heuristically
to the more general problem version and
provide computational results that
demonstrate the high performance quality
of the resulting heuristic solution methods.
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