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Panel II: State of the Art/Practice in DELS: Successes & Failures. Scott J. Mason March 8, 2010. CELDi. Center for Engineering Logistics and Distribution (CELDi) National Science Foundation Industry/University Cooperative Research Center (10 yrs) with $1.3M in annual memberships.

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celdi
CELDi
  • Center for Engineering Logistics and Distribution (CELDi)
  • National Science Foundation Industry/University Cooperative Research Center (10 yrs) with $1.3M in annual memberships.
  • CELDi is focused on applied research projects in engineering logistics and distribution for commercial, military, and government applications.
    • http://celdi.ineg.uark.edu
  • Academic Partners (10)
    • University of Arkansas (lead), Oklahoma State University, University of Oklahoma, University of Louisville, Lehigh University, Texas Tech University, Clemson University, Virginia Tech, University of Missouri, Arizona State University
previous celdi research a large company s logistics network

Suppliers Consolidation Distribution Centers

Mode: LTL

Modes:

Intermodal and TL

Modes: LTL or TL

i suppliers

j facilities

k distribution centers

Previous CELDi Research:A Large Company’s Logistics Network
strategic network optimization
Strategic Network Optimization
  • Based on forecasted demand for the next five years, determine which consolidation facilities should be
    • Closed, expanded, opened, or built
  • Objective: minimize transportation costs
    • Truck
    • Intermodal (i.e., truck and rail)
  • Constraints:
    • Demand satisfaction
    • Transportation vehicle weight/volume capacity
    • Consolidation facility capacity
success
Success!
  • Dealing with real data from large company
    • 40% optimality gap in 1 hour
  • Due to computational (and model) limitations, needed to group data from 2000+ suppliers into 3-digit ZIP codes
    • 412 individual 3-digit ZIP codes (i.e., 727xx)
    • 0.5% optimality gap in 30 minutes
  • Using 3-digit ZIPs, future # of suppliers not a concern as model size should remain somewhat stable (consistent) within any given time period
failure
Failure!
  • Solving the strategic problem in 6-month time buckets may not equate to operational feasibility
  • Project did not secure any high level company “champion” such that
    • No internal visibility or data support was evident early on
    • No chance of implementation was possible
    • Our research efforts did not stay aligned with company priorities and we became irrelevant
  • Early efforts to solve large model with real, practical constraints proved detrimental to our success
wafer fab starts optimization
Wafer Fab Starts Optimization
  • Semiconductor manufacturing characterizing by hundreds of process steps taking weeks to produce a patterned silicon wafer containing integrated circuits
    • Photolithography steppers often bottlenecks
  • “Lots” of 25 wafers are released into the factory every day of various product or technology types
    • Each technology can be of a certain sub-technology type
      • Different technologies are qualified on different subsets of steppers (i.e., only certain tools can run certain types)
      • Timing, quantity, and sub-technology specification of lot releases can dramatically impact production cycle times
optimal decision making
“Optimal”  Decision Making
  • Model integrated with fab MES to extract route, tool, qualification, and E[Step Cycle Time] data (based on actuals)
  • Client asked for decision support tool to set technology sub-type specifications and schedule lot release into fab for a one week time period (by day) such that
    • Average utilization of all 19 steppers is balanced
    • Maximum daily utilization of any stepper is minimized
    • Maximum number of lot starts on any day is minimized
    • Desired minimum number of lot starts on any day by sub-technology type are observed
      • “Even if 1 is optimal, minimum rule says 3, so release 3.”
grand challenge 1
Grand Challenge #1
  • Lack of computational resources can limit ability to analyze full scale problem when practical constraints are necessary
  • Today, significant availability of parallel computing resources ease computational burden of solving large DELS problems
    • To develop modeling and analysis methods that leverage growing computational resources to promote implementation of large scale, real world DELS problem solutions containing practical (i.e., reality) constraints
      • Solve the “right” problem
modeling risk reliability in supply chains
Modeling Risk & Reliability in Supply Chains
  • Motivation – Supply Chain Disruptions
    • Anthrax spores were discovered in October 2001 at USPS processing facility in (Washington D. C)
    • West Coast port lockout in 2002
    • In 2005, hurricane Katrina damaged plants and warehouses
supply chain risk
Supply Chain Risk
  • Modern supply chains have evolved into complex systems due to globalization and decentralization
  • Of primary concern are risks associated with large-scale disruptions
    • Natural disasters, terrorist attacks, political instability, and transportation/network failures
      • Impact supply chain continuity and effectiveness
      • Can result in significant economic loss and loss of life
recent research efforts
Recent Research Efforts
  • Dept of Homeland Security (DHS) project
  • Investigating importance of facility locations in the face of infrastructure disruptions
  • As part of DHS-funded research effort, we are compiling a reference database
    • http://comp.uark.edu/~mason/Bib_Test.html
    • 350+ articles, we welcome your contribution and participation
grand challenge 2
Grand Challenge #2
  • To develop models for resilient, reliable, and sustainable supply chain network design using both reliability- and optimization-based tools and techniques