2016 INFORMS Annual Meeting Program

236 / 561

INFORMS Nashville – 2016
236
TA11
104A-MCC
Novel Applications of Network Optimization
Sponsored: Optimization, Network Optimization
Sponsored Session
Chair: Anna Nagurney, John F. Smith Memorial Professor, Isenberg
School of Management, University of Massachuseets, Amherst, MA,
01003, United States,
1 - A General Multitiered Supply Chain Network Model Of Quality
Competition With Suppliers
Dong Li, Arkansas State University, ,
Anna B Nagurney
In this paper, we develop a general multitiered supply chain network equilibrium
model consisting of competing suppliers and competing firms who purchase
components from suppliers. The competitive behavior of each tier of decision-
makers is described along with their strategic variables, which include quality of
the components and, in the case of the firms, the quality of the assembly process
itself. The governing equilibrium conditions of the supply chain network are
formulated as a variational inequality and qualitative properties are presented.
The algorithm, accompanied with convergence results, is then applied to
numerical supply chain network examples along with sensitivity analysis.
2 - Supply Chain Network Equilibrium With Strategic Financial
Hedging Using Futures Contracts
Zugang Liu, Pennsylvania State University - Hazleton,
Hazleton, PA, United States, , Jia Wang
We develop a modeling and computational framework for supply chain networks
with strategic financial hedging. We consider multiple competing firms that
purchase multiple materials to manufacture their products. The supply chain
firms’ procurement activities are exposed to several risk factors such as
commodity price uncertainty and exchange rate volatility. The firms can use
futures contracts to hedge the risks. Our research studies the equilibrium of the
entire network where each firm optimizes its own operation and hedging
decisions.
3 - A Time-space Network Model For Medical Resources Allocation
In An Epidemic Outbreak
Ding Zhang, SUNY Oswego, New York, NY, United States,
This paper presents a dynamic logistics model for medical resources allocation that
can be used in the control of an epidemic diffusion. It couples a forecasting
mechanism, constructed for the demand of a medicine in the course of such
epidemic diffusion, and a logistic planning system to satisfy the forecasted demand
and minimize the total cost. The model is built as a closed-loop cycle, comprises of
forecast phase, planning phase, execution phase, and adjustment phase. The
parameters of the forecast mechanism are adjusted in reflection of the real data
collected in the execution phase by solving a quadratic programming problem. A
numerical example is presented to illustrate the model.
4 - A Generalized Nash Equilibrium Network Model For Post-disaster
Humanitarian Relief
Anna Nagurney, John F. Smith Memorial Professor, University of
Massachusetts Amherst, Amherst, MA, 01003, United States,
, Emilio Alvarez Flores, Ceren Soylu
We develop a Generalized Nash Equilibrium network model for post-disaster
humanitarian relief by nongovernmental organizations (NGOs). NGOs derive
utility from providing relief supplies to the victims of the disasters at multiple
demand points in a supply chain context while competing with each other for
financial funds provided by donations. The shared constraints consist of the lower
and upper bounds for demand for relief items at the demand points and can be
imposed by the regulatory body or higher level coordinating NGO to reduce
materiel convergence and congestion. We provide an effective computational
scheme and numerical examples plus solutions under the Nash Equilibrium
counterpart.
TA12
104B-MCC
Mixed-Integer Programming with Applications
Sponsored: Optimization, Integer and Discrete Optimization
Sponsored Session
Chair: Minjiao Zhang, Kennesaw State University, 560 Parliament
Garden Way, Kennesaw, GA, 30144, United States,
1 - Coordinated Capacitated Lot-sizing For Multiple Product Families
With Setup Times
Tiffany Bayley, University of Waterloo, Waterloo, ON, Canada,
Haldun Sural, James H Bookbinder
We examine a coordinated capacitated lot-sizing problem for multiple product
families, where demand is deterministic and time-varying. The problem considers
only holding costs, where capacity constraints limit the number of item and
family setups and the amount of production in each period. Using a strong
reformulation and relaxing the demand constraint, we improve both the upper
and lower bounds using Benders decomposition and a cutting plane procedure.
Through computational experiments, we show that our method consistently
achieves better bounds, reducing the duality gap compared to other single-family
methods studied in the literature.
2 - A Polyhedral Study On Lot-sizing Problem With
Capacity Acquisition
Jia Guo, The University of Alabama, Tuscaloosa, AL, 35487,
United States, , Minjiao Zhang
Determining the optimal capacity level to invest is one of the fundamental
problems for an enterprise’s operation. In this study, we consider a single-echelon
lot-sizing problem with capacity acquisition (CALS). Two families of the so-called
capacity definition and demand satisfying inequalities are proposed to describe
the convex hull of CALS. Our computational results show that the proposed
inequalities are very effective in solving CALS.
3 - The Slim Branch And Price Method With An Application To The
Hamiltonian P-median Problem
Ahmed Marzouk, Texas A&M University, College Station, TX,
77840, United States,
Erick Moreno-Centeno, Halit Uster
We present the Slim Branch and Price (SBP) method which is an improvement
over traditional Branch and Price in the case of binary master problems. The main
advantage in SBP is that the branching tree has only one main branch with
several leaves. We illustrate the computational advantage of SBP over Branch and
Price on the Hamiltonian p-median problem. In particular, under one hour limit,
SBP can solve to optimality instances with up to 200 nodes; whereas Branch and
Price can solve to optimality instances with up to 127 nodes.
4 - Multiechelon Lot Sizing With Intermediate Demands
Ming Zhao, Assistant Professor, University of Houston, Houston,
TX, 77204, United States, , Minjiao Zhang
We prove that multiechelon lot sizing with intermediate demands is NP-hard.
However, in the case of fixed number of echelons, we are able to derive
polynomial time algorithms for both capacitated and uncapacitated models.
TA13
104C-MCC
Computational Linear Optimization
General Session
Chair: Nikolaos Ploskas, Carnegie Mellon University, 5000 Forbes
Avenue, Pittsburgh, PA, 15213, United States,
1 - Vector Space Decomposition For Solving Linear Programs
Marco Lübbecke, RWTH Aachen University,
Jean Bertrand Gauthier,
Jacques Desrosiers
We propose a general algorithmic framework for solving linear programs. An
iteration moves from one solution to the next in a direction with a certain step
size. Part of the direction is determined by a pricing problem that is interesting in
its primal and dual form: the dual fixes part of the dual variables and optimizes
the rest; the primal selects a convex combinations of variables. Several known
algorithms are unified by our view, in particular the primal simplex method, the
minimum mean cycle canceling algorithm, and the improved primal simplex
method. The framework allows us to precisely characterize when degenerate
iterations can occur, and how to avoid them (of course, at a computational cost).
TA11