in the laboratory focuses on a wide range of logistics problems
in manufacturing, supply chains, transportation and service systems.
A common thread to research in these areas is the use of operations
research methods including stochastic modeling and optimization.
The following is a sample of current research projects.
Manufacturing in the Electronic Industry
- Saif Benjaafar, University of Minnesota
- Karen Donohue, University of Minnesota
- David Wu, Lehigh University
- N. Vishwanadham, National University of Singapore
- Fikri Karaesmen, Ecole Centrale de Paris
Sponsors: The Logistics Institute-Asia Pacific, Polarfab
Corporation, Lucent Technology
Contract Manufacturing has emerged in recent years as the preferred
mode of production in the high-tech industry. Contract manufacturing
has grown from a few billion dollars in the early 1990s to over
a $140 billion industry in 2000. By 2005, over 50% of all electronics
manufacturing is expected to be carried out on a contract-basis.
Contract manufacturing is different from traditional supplier-manufacturer
settings in that few large supercontractors dominate the marketplace.
These supercontractors are able to support a large number of original
equipment manufacturers (OEMs) with products ranging from cellular
phones to laser printers. In this sense, supercontractors are
engaged less in the selling of specific products and more in the
selling of manufacturing capacity. Contract manufacturing raises
several important research questions. How should contractual agreements
between contractors and OEMs be structured so that they are mutually
beneficial? How should contractors ration their available capacity
and inventory among the competing needs of different customers?
Should contract manufacturers pursue a few large accounts or several
smaller ones? What are the true costs and benefits of customer
diversification? When there is excess capacity, how should it
be channeled? How should contractors take advantage of the emerging
e-markets for capacity? What are the costs and benefits of the
dynamic pricing of capacity that results from participating in
In this research, we propose to address these important questions.
More specifically, we will develop analytical models, computational
algorithms, and decision support tools that push the current frontier
of supply chain modeling and analysis in this area. These tools
will assist contract manufacturers in managing their capacity,
customer and product portfolios, and inventories and provide them
with vital insights as they face the new challenges of supercontracting.
Our research will focus on three main areas:
- Contract Design, where we will develop a game-theoretic
framework for the modeling and analysis of option-based contracts
in multi-buyer settings. Various forms of contracts will be examined
and their implications to supercontracting will be analyzed.
- Variety Management, where we will develop analytical
models that draw from both inventory and queueing theory to assist
contractors in making decisions regarding product and customer
portfolio selection, dynamic capacity allocation, and inventory
- Capacity Trading, where we will examine game-theoretic
models for the emerging electronic marketplaces for capacity.
In particular, we will examine how participation in online markets
such as auctions affects the way contract manufacturers price
and manage their capacity.
Our overarching objective is to develop a science base for the
emerging field of contract manufacturing. We will introduce new
research issues brought forward by this fast growing sector of
the economy, and the role various analytical models play in providing
vital insights. Our research will benefit industry by providing
decision-makers with analytical tools that can guide both their
contractual and operational decisions. Our research will also
provide a roadmap to contract manufacturers regarding when and
how to participate in the new e-markets for manufacturing capacity.
In all phases of this research, we will be guided by continuous
interaction with our industry partners.
Design and Inventory Deployment for Improving Delivery Time Performance
in the Steel Industry
- Diwakar Gupta and Saifallah Benjaafar, University of Minnesota
This grant provides funding for the development of mathematical
models and numerical tools which can be used to determine the
optimal design (dimensions, grade, and weight) of steel slabs,
and optimal replenishment policies for slab inventories. A two-step
approach will be used. In the first step, optimal design configurations
will be determined using a combination of heuristics to generate
both a good initial solution and refinements to the initial solution.
A stochastic integer programming formulation involving binary
variables will be used to provide bounds on the performance of
these heuristic solutions. For a given number of slab designs,
these methods will determine those configurations that maximize
coverage, measured in terms of total finished tons that can be
manufactured from the chosen configurations. In the second step,
a multi period stochastic linear program will be developed to
determine replenishment batch sizes for each slab design configuration
in order to minimize the sum of inventory holding and production
If successful, the results of this research will provide a science-based
solution to a chronic problem faced by integrated steel mills
(ISMs). By focusing on reducing the number of slab designs, ISMs
can pursue specialty steel markets while simultaneously reducing
their production process complexity and inventory costs. Solution
techniques developed to solve the underlying stochastic optimization
problems, will have wider applications to industries with similar
process architecture, e.g., paper and pulp, and to other stochastic
optimization problems involving a large number of scenarios. The
latter occur in a host of applications ranging from energy models,
capacity planning to financial asset management. Models proposed
for determining optimal replenishment policies, especially when
coupled with the possibility of delaying product differentiation,
will be useful for manufacturing firms trying to cope with increased
of Production-Inventory Systems
- Saif Benjaafar and Bill Cooper, University of Minnesota
- Mohsen Elhafsi, University of California, Riverside
Sponsor: NSF, St Jude Medical
The focus of this research is on modeling, design and analysis
of integrated production-inventory systems. Several projects are
- Analysis of inventory pooling in production-inventory systems
- Demand allocation in multi-product/multi-facility make-to-stock
- Analysis of demand variability in production-inventory systems
- Inventory rationing in make-to-stock systems
- Advanced order information in production-inventory systems.
- Saif Benjaafar, University of Minnesota
- Gurumurthi Suryanarayan, Aloca, Corporations
This research deals with the modeling and analysis of flexible
queueing systems. Flexible queueing systems to refer to systems
with multiple classes of arrivals and multiple servers, where
customers have the flexibility of being routed to more than one
server and servers possess the capability of processing more than
one customer class. Flexible queueing systems arise in a variety
of contexts, including manufacturing, telecommunication networks,
computer systems, vehicle and crew dispatching, and service systems,
among others. We provide a modeling framework for the analysis
of general system with an arbitrary number of heterogeneous resources
and customer types and arbitrary routing and resource flexibility.
We consider a rich set of control policies that include strict
priority schemes for customer routing and queue selection. We
use our models to generate several insights into the effect of
system parameters. In particular, we examine the relationship
between flexibility, control policies, and system throughput.