Capita Selecta Penelitian Decision Support Systems atau Design Science/Modelling
By: Ir. Togar A. Napitupulu, MS., MSc., Ph.D
- Mixed-integer programming (MIP) model
Interfaces, Vol 46. Issue 6.
Permalink: http://dx.doi.org/10.1287/inte.2016.0870
Published Online: October 31, 2016
Page Range: 503 – 521
A Decision Support System for Fuel Supply Chain Design at Tampa Electric Company
Abstract
Tampa Electric Company (TECO), which serves 687,000 customers in Florida, generates 60 percent of its electricity using coal-fired generators. To meet environmental regulations on the emission of coal combustion, it must carefully mix several fuels of different qualities to make safe, environmentally friendly, and affordable blends that are generator specific. We worked with the management and engineering teams at TECO to develop a decision support platform, which centers around a mixed-integer programming (MIP) model to comprehensively capture system specifications, requirements, and operations in all key aspects of TECO’s fuel supply chain. This platform enables TECO to make optimal procurement, transportation, blending, and burn decisions and satisfy all environmental regulations. We estimate that the implementation of this model can provide TECO with annual fuel-cost savings of 2–3 percent, which translate to millions of dollars of savings in total fuel costs.
Keywords: fuel supply chain; power plant optimization; transportation and logistics; coal blending; decision support system; mixed-integer programming
Stochastic dynamic programming model
Interfaces, Vol 46. Issue 6.
Permalink: http://dx.doi.org/10.1287/inte.2016.0865
Published Online: October 20, 2016
Page Range: 482 – 492
Noninvasive Test Scheduling in Live Electricity Markets at Transpower New Zealand
Abstract
In 2013, Transpower New Zealand commissioned a new high-voltage, direct current link to transfer electrical power between the North and South Islands of New Zealand. This was a substantial and prolonged undertaking, requiring approximately 400 in-situ capability tests. Transpower elected to perform these tests “live,” without suspending the normal operation of the wholesale electricity market. Instead, Transpower’s trading team attempted to create suitable flow conditions for each test by entering into innovative financial derivative contracts with power generation firms. We created a stochastic dynamic programming model to handle the contingent scheduling of the tests; its most important random variable was the state of water storage available to hydropower plants.
Keywords: stochastic dynamic programming; scheduling; electricity; financial derivatives
- General-purpose machine-learning framework
Interfaces, Vol 46. Issue 5.
Permalink: http://dx.doi.org/10.1287/inte.2016.0862
Published Online: October 12, 2016
Page Range: 368 – 390
Machine Learning for Predicting Vaccine Immunogenicity
Abstract
The ability to predict how different individuals will respond to vaccination and to understand what best protects individuals from infection greatly facilitates developing next-generation vaccines. It facilitates both the rapid design and evaluation of new and emerging vaccines and identifies individuals unlikely to be protected by vaccine. We describe a general-purpose machine-learning framework, DAMIP, for discovering gene signatures that can predict vaccine immunity and efficacy. DAMIP is a multiple-group, concurrent classifier that offers unique features not present in other models: a nonlinear data transformation to manage the curse of dimensionality and noise; a reserved-judgment region that handles fuzzy entities; and constraints on the allowed percentage of misclassifications.
Using DAMIP, implemented results for yellow fever demonstrated that, for the first time, a vaccine’s ability to immunize a patient could be successfully predicted (with accuracy of greater than 90 percent) within one week after vaccination. A gene identified by DAMIP, EIF2AK4, decrypted a seven-decade-old mystery of vaccination. Results for flu vaccine demonstrated DAMIP’s applicability to both live-attenuated and inactivated vaccines. Results in a malaria study enabled targeted delivery to individual patients.
Our project’s methods and findings permit highlighting and probabilistically prioritizing hypothesis design to enhance biological discovery. Moreover, they guide the rapid development of better vaccines to fight emerging infections, and improve monitoring for poor responses in the elderly, infants, or others with weakened immune systems. In addition, the project’s work should help with universal flu-vaccine design.
- Stochastic Inventory Model
Incorporating Stochastic Lead Times Into the Guaranteed Service Model of Safety Stock Optimization
Interfaces, Vol 43. Issue 5.
Permalink: http://dx.doi.org/10.1287/inte.2013.0699
Published Online: November 1, 2013
Page Range: 421 – 434
Abstract
Effective end-to-end supply chain management and network inventory optimization must account for service levels, demand volatility, lead times, and lead-time variability. Most inventory models incorporate demand variability, but far fewer rigorously account for lead-time variability, particularly in multiechelon supply chain networks. Our research extends the guaranteed service model of safety stock placement to allow random lead times. The main methodological contribution is the creation of closed-form equations for the expected safety stock in the system; this includes a derivation for the early-arrival stock in the system. The main applied contributions are the demonstration of real stochastic lead times in practice and a discussion of how our approach outperforms more traditional heuristics that either ignore lead-time variability or consider the maximum lead time at each stage.
Key words: base-stock policy; multiechelon inventory optimization; stochastic lead times; supply chain application
- Global Supply-Chain Optimization
Supply Chain–Wide Optimization at TNT Express
Interfaces, Vol 43. Issue 1.
Permalink: http://dx.doi.org/10.1287/inte.1120.0655
Published Online: February 1, 2013
Page Range: 5 – 20
Abstract
The application of operations research (OR) at TNT Express during the past seven years has significantly improved decision-making quality and resulted in cost savings of 207 million euros. The global optimization (GO) program initiative has led to the development of a suite of optimization solutions to assist the operating units of TNT Express to improve their package delivery in road and air networks. To create and deploy these solutions, we established communities of practice (CoPs), at which internal and external subject matter experts meet three times annually at an internal conference. We also created a unique two-year learning environment, the GO academy, where employees of TNT Express are taught the principles, use, and deployment of optimization techniques. As a result of these combined initiatives, OR is now an effective part of the core values at TNT Express.
Keywords: express service providers ; transportation ; supply chain optimization ; network design problem ; pickup and delivery problem ; change management ; OR deploymen
- Monte Carlo Simulation, Forecasting, Stochastic Programming
Optimizing Capital Investment Decisions at Intel Corporation
Interfaces, Vol 43. Issue 1.
Permalink: http://dx.doi.org/10.1287/inte.1120.0659
Published Online: February 1, 2013
Page Range: 62 – 78
Abstract
Intel Corporation spends over $5 billion annually on manufacturing equipment. With increasing lead times from equipment suppliers and increasing complexity in forecasting market demand, optimizing capital investment decisions is a significant managerial challenge. In response to this challenge, we developed a capital supply chain velocity program for ordering, shipping, and installing production equipment. At the core of this velocity program is a new and additional procurement framework that enables Intel to purchase options from its equipment suppliers for a faster delivery of some equipment. The framework seamlessly combines statistical forecasting with Monte Carlo simulation and stochastic programming to determine the number of options Intel should procure and exercise, and it includes built-in scenario and sensitivity analysis capabilities to support Intel’s contract selection, options reservation, and equipment procurement decisions. The velocity program and the framework provided Intel with hundreds of millions of dollars in cost savings and at least $2 billion in revenue upside during a recent period of global economic crisis.
Keywords: capital-intensive industries ; capacity expansion ; dual sourcing ; expedited equipment lead times ; option contracts ; forecast revision ; stochastic programming ; Monte Carlo simulation ; isoprofit analysis
SYSTEM DYNAMICS SYMULATION
- Pharmaceutical market dynamics and strategic
planning: a system dynamics perspective
Mark Paich, Corey Peck and Jason Valant
Abstract
Competitive pressures are forcing pharmaceutical companies to develop even more effective productbased strategic plans, which have traditionally been derived from unarticulated mental models about the individual diseases an the role of various decision‐makers within them. The industry is blessed with a wealth of patient and physician level data, but often this information is not leveraged to its full extent. System dynamics provides an operational framework for understanding and analyzing how the interaction of patient flow dynamics, physician prescribing/ product adoption behavior, and the evaluation of therapy options drive marketplace behavior. By evaluating the response of such an integrated model to possible marketing initiatives, pharmaceutical fi rms can develop and ultimately execute more cost-effective strategic plans for their products. Copyright © 2011 System Dynamics Society
The feedback method of teaching macroeconomics: is it effective?
Abstract
The conventional method of teaching macroeconomics to undergraduates relies on static graphs, an approach with documented pedagogical problems. In contrast, the feedback method uses causal loop diagrams and interactive computer simulation models. This paper describes the feedback method and four experiments that tested its effectiveness. Two experiments examined student preferences for methods of learning macroeconomics (e.g., using static graphs or a causal loop diagram), and a significant majority preferred the feedback method. In the third experiment, students showed more understanding of GDP when they had access to a stock-and-flow feedback diagram of the economy. In the final experiment, students using causal loop diagrams displayed more understanding of business cycle dynamics than those with access to a conventional aggregate supply-and-demand graph. Searching for feedback structure in the economy and using computer simulation to connect structure with behavior appears to be a promising method for learning macroeconomics. Copyright © 2007 John Wiley & Sons, Ltd.
System dynamics and strategy
Volume 24, Issue 4
Winter 2008
Pages 407–429
Abstract
System dynamics research has made numerous contributions to a range of management subfields, including operations, organization behavior, marketing, behavioral decision making, and strategy. In this paper, we focus on the role for system dynamics research in making important progress on the defining issue in the field of strategy: why are some firms more profitable than others? Strategy researchers are eager for dynamic theories that explain the evolution of performance differences among firms and are increasingly looking to managerial decision making as the source of dynamics. This interest in dynamics and decision making creates an enormous opportunity for system dynamics researchers as carefully grounded behavioral theories of dynamics flow naturally from the research methods of system dynamics. Building and testing theories that explain longitudinal patterns of performance differences among firms would be an enormous step forward where mainstream strategy approaches have struggled. We identify four promising research paths along these lines for system dynamics research in the field of strategy. Copyright © 2009 John Wiley & Sons, Ltd.
Jay Forrester’s operational approach to economics
Khalid Saeed
Volume 30, Issue 4
October/December 2014
Pages 233–261
Abstract
Jay Forrester’s models evoke an operational approach to understanding economic behavior and managing economic systems. They build on managerial roles, not rational agency embedded in mainstream models of economic theory. Forrester’s models can therefore be easily tied to policies that relate to everyday decisions. His writings provide deep insights that can be effectively applied to managing firms, regions, nations and the global economic system. This paper discusses Forrester’s approach to addressing economic problems, and how it calls for rethinking the practice of economics. Copyright © 2015 System Dynamics Society