A Multiechelon Inventory Problem with Secondary Market Sales

Published version made available in SMU repository with permission of INFORMS, 2014, February 28</p

Radio Astronomy

Contains table of contents for Section 4 and reports on twelve research projects.National Science Foundation Grant AST 88-19848Jet Propulsion Laboratory Contract 957687National Aeronautics and Space Administration Grant NAGW 1386National Science Foundation Grant AST 88-19848Annie Jump Cannon AwardSM Systems and Research, Inc.U.S. Navy Office of Naval Research Contract N00014-88-K-2016NASA/Goddard Space Flight Center Grant NAG 5-537NASA/Goddard Space Flight Center Grant NAG 5-10Woods Hole Oceanographic Institution Contract SC-28860Leaders for Manufacturing Progra

Managing Capacitated Multiechelon Systems with Domain-Optimal Policies

under the 2nd review at Operations Research</p

Simultaneous Capacity and Production Management of Short-Life-Cycle, Produce-to-Stock Goods Under Stochastic Demand

This paper derives the optimal simultaneous capacity and production plan for a shortlife-cycle, produce-to-stock good under stochastic demand. Capacity can be reduced as well as added, at exogenously set unit prices. In both cases studied, with and without carryover of unsold units, a target interval policy is optimal: There is a (usually different) target interval for each period such that capacity should be changed as little as possible to bring the level available into that interval. Our contribution in the case of no carry-over, is a detailed characterization of the target intervals, assuming demands increase stochastically at the beginning of the life cycle and decrease thereafter. In the case of carry-over, we establish the general result and show that capacity and inventory are economic substitutes: The target intervals decrease in the initial stock level and the optimal unconstrained base stock level decreases in the capacity level. In both cases, optimal service rates are not necessarily constant over time. A numerical example illustrates the results.Capacity management, Production management, Capacity expansion, Capacity contraction, Finite lifetime, Stochastic demand, Nonstationary

Opportunities for Improved Statistical Process Control

Our Bayesian dynamic programming model builds on existing models to account for inspection delay, choice of keeping production going during inspection and/or restoration, and lot sizing. We focus on describing how dynamic statistical process control (DSPC) rules can improve on traditional, static ones. We explore numerical examples and identify nine opportunities for improvement. Some of these ideas are well known and strongly supported in the literature. Other ideas may be less well understood. Our list includes the following: Cancel some of the inspections called for by an (economically) optimal static rule when starting in control (such as at the beginning of a production run and following a restoration). Inspect more frequently than called for by an optimal static rule once inspections begin, and inspect even more frequently than that when negative evidence is accumulated. Utilize evidence from previous inspections to justify either restoration or another inspection. Cancel inspections and hesitate to restore the process at the end of a production run. Consider using scheduled restoration, in which restoration is carried out regardless of the results of any inspections. Implementation, limitations, and extensions are addressed.dynamic statistical process control, Bayesian dynamic programming, inspection delay, quality control, sequential sampling, lot sizing, scheduled maintenance and restoration, inspection deferral, inspection cancellation

Optimal Shipping, Collaboration, and Outsourcing Decisions in a Hybrid Cross-docking Supply Chain

This repository contains code associated with our accepted paper in IISE Transactions titled "Optimal Shipping, Collaboration, and Outsourcing Decisions in a Hybrid Cross-docking Supply Chain." To briefly summarize, 1. value of collaboration.zip - includes code to evaluate the value of upstream collaboration and downstream collaboration in a hybrid cross-docking supply chain. The "upstream" folder includes code to evaluate the upstream collaboration benefits to the cross-dock in a varying number of products (q=2, 4, 6). The "downstream" folder includes code to evaluate the downstream collaboration benefits to the oil-well facilities (OWFs) in a varying number of products (q=2, 4, 6). 2. outsourcing.zip - includes code to obtain the optimal outsourcing decision for oil well facilities and pricing decisions of the logistics provider using the sample approximation method. The "best response (OWF-outsourcing-decision)" folder includes code to obtain the threshold for each individual OWF's optimal outsourcing decision, such that for any outsourcing price lower than the threshold, the OWF outsources to the logistics provider. Given the OWFs' best responses, the "outsourcing price" folder includes code to evaluate the optimal outsourcing price for the logistics provider. The code evaluates the optimal pricing strategy under a varying number of OWFs (M=1,2,3,4) in supplemental materials. To conduct the numerical experiment in the paper, the author team used the ILOG AMPL CPLEX system (ILOG AMPL CPLEX 2021) on Intel Core i7-8650 CPU with a 2.11 GHz processor and 16 GB of RAM