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Chapter 9 - Model-Based Decision Making: Optimization and Multi-Criteria Systems 393 9.3 Structure of Mathematical Models for Decision Support 9.4 Certainty, Uncertainty, and Risk 9.5 Decision Modeling with Spreadsheets 9.6 Mathematical Programming Optimization 9.7 Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking 9.8 Decision Analysis with Decision Tables and Decision Trees 9.9 Multi-Criteria Decision Making with Pairwise Comparisons 9.1 OPENING VIGNETTE: Midwest ISO Saves Billions by Better Planning of Power Plant Operations and Capacity Planning INTRODUCTION Midwest ISO (MISO) operates in 13 U.S. states as well as the province of Manitoba in Canada. It manages 35 transmission owners and 100 non-transmission owners, ensuring that all members of the organization have equal access to high-voltage power lines. Together, the United States and the province of Manitoba constitute one of the largest energy markets in the world, with yearly energy transactions amounting to about $23 billion. Before Midwest ISO existed, each transmission company operated independently. Now, after a company joins MISO, it still maintains control of its power plants and transmission lines, and shares in the responsibility of supplying and buying energy in a wholesale electricity market to meet demand. MISO, however, has the responsibility of deciding when and how much energy to produce and administer to the market in such a way as to increase benefit to society. PRESENTATION OF PROBLEM Individually, the companies had to make extra investments to manage risk. Their mode of operation resulted in inefficient use of transmission lines. Deregulation policies were introduced by Congress and were implemented by the Federal Energy Regulatory Commission (FERC) for the wholesale electricity industry. When MISO was formed, it first started an energy-only market in 2005 that ensured unbiased access to transmission lines. In 2009 , it added ancillary services (regulation and contingency reserves) to its operations. Regulation was supposed to ensure that the frequency did not deviate from 60 hertz. Contingency reserves were supposed to help ensure that in the event of unexpected power loss, demand was met within 10 minutes of the power loss. Operations research methods were considered as means to provide the level of performance demanded by the ancillary services. METHODOLOGY/SOLUTION Sequentially, two optimization algorithms were used. These were the commitment algorithm and the dispatch algorithm. The commitment algorithm committed power plants to be either on or off. The dispatch algorithm determined the level of a power plant's output and price. With these two algorithms, facilities were given constraints on how much electricity to carry within their physical limits in order to avoid overload and damage to expensive equipment. The commitment problem for the energy-only market made use of the Lagrangian relaxation method. As mentioned earlier, it determined when each plant should turn on or off. The dispatch problem was solved with a linear also be used for irrigation planning in such a way as to avoid the impact of droughts and surplus water. Even companies under financial stress need to invest in such solutions to Chapter 9 - Model-Based Decision Making: Optimization and Multi-Criteria Systems 395 squeeze more efficiency out of their limited resources-maybe even more so. Pillowtex, a $2 billion company that manufactures pillows, mattress pads, and comforters, had filed for bankruptcy and needed to reorganize its plants to maximize net profits from the company's operations. It employed a simulation model to develop a new lean manufacturing environment that would reduce the costs and increase throughput. The company estimated that the use of this model resulted in over $12 million savings immediately. (See promodel.com.) We will study simulation in the next chapter. Modeling is a key element in most DSS and a necessity in a model-based DSS. There are many classes of models, and there are often many specialized techniques for solving each one. Simulation is a common modeling approach, but there are several others. Applying models to real-world situations can save millions of dollars or generate millions of dollars in revenue. Christiansen et al. (2009) describe the applications of such models in shipping company operations. They describe applications of TurboRouter, a DSS for ship routing and scheduling. They claim that over the course of just a 3-week period, a company used this model to better utilize its fleet, generating additional profit of \$1-2 million in just a short time. We provide another example of a model application in Application Case 9.1. Application Case 9.1 Optimal Transport for ExxonMobil Downstream Through a DSS ExxonMobil, a petroleum and natural gas company, operates in several countries worldwide. It provides several ranges of petroleum products including clean fuels, lubricants, and high-value products and feedstock to several customers. This is completed through a complex supply chain between its refineries and customers. One of the main products ExxonMobil transports is vacuum gas oil (VGO). ExxonMobil transports several shiploads of vacuum gas oil from Europe to the United States. In a year, it is estimated that ExxonMobil transports about 60-70 ships of VGO across the Atlantic Ocean. Hitherto, both ExxonMobil-managed vessels and third-party vessels were scheduled to transport VGO across the Atlantic through a cumbersome manual process. The whole process required the collaboration of several individuals across the supply chain organization. Several customized spreadsheets with special constraints, requirements, and economic trade-offs were used to determine the transportation schedule of the vessels. Some of the constraints included: 1. Constantly varying production and demand projections 2. Maximum and minimum inventory constraints 3. A pool of heterogeneous vessels (e.g., ships with varying speed, cargo size) 4. Vessels that load and discharge at multiple ports 5. Both ExxonMobil-managed and third-party supplies and ports 6. Complex transportation cost that includes variable overage and demurrage costs 7. Vessel size and draft limits for different ports The manual process could not determine the actual routes of vessels, the timing of each vessel, and the quantity of VGO loaded and discharged. Additionally, consideration of the production and consumption data at several locations rendered the manual process burdensome and inefficient. Methodology/Solution A decision support tool that supported schedulers in planning an optimal schedule for ships to load, transport, and discharge VGO to and from multiple locations was developed. The problem was formulated as a mixed-integer linear programming problem. The solution had to satisfy requirements for routing, transportation, scheduling, and inventory management vis--vis varying production and demand profiles. A mathematical programming language, GAMS, was used for the problem formulation and Microsoft Excel was used as the (Continued) 396 Part IV - Prescriptive Analytics Application Case 9.1 (Continued) RESULTS/BENEFITS Based on the improvements made, reliability of the transmission grid improved. Also, a dynamic transparent pricing structure was created. Value proposition studies show that Midwest ISO achieved about $2.1 billion and $3 billion dollars in net cumulative savings between 2007 and 2010. Future savings are expected to accrue to about $6.1 billion. QUESTIONS FOR THE OPENING VIGNETTE 1. In what ways were the individual companies in Midwest ISO better off being part of MISO as opposed to operating independently? 2. The dispatch problem was solved with a linear programming method. Explain the need of such method in light of the problem discussed in the case. 3. What were the two main optimization algorithms used? Briefly explain the use of each algorithm. LESSONS WE CAN LEARN FROM THIS VIGNETTE Operations research (OR) methods were used by Midwest ISO to provide efficient and cheaper sources of energy for states in the midwestern region of the United States. A combination of linear programming and the Lagrangian relaxation methods was used to determine an optimized approach to generate and supply power. By extension, this methodology could be used by both government agencies and the private sector to optimize the cost and provision of services such as healthcare and education. Source: Brian Carlson, Yonghong Chen, Mingguo Hong, Roy Jones, Kevin Larson, Xingwang Ma, Peter Nieuwesteeg, et al., "MISO Unlocks Billions in Savings Through the Application of Operations Research for Energy and Ancillary Services Markets," Interfaces, Vol. 42, No. 1, 2012, pp. 58-73. 9.2 DECISION SUPPORT SYSTEMS MODELING Many readily accessible applications describe how the models incorporated in DSS contribute to organizational success. These include Pillowtex (see ProModel, 2013), Fiat (see ProModel, 2006), Procter \& Gamble (see Camm et al., 1997), and others. INFORMS publications such as Interfaces, ORMS Today, and Analytics magazine all include stories that illustrate successful applications of decision models in real settings. This chapter includes many examples of such applications, as does the next chapter. Simulation models can enhance an organization's decision-making process and enable it to see the impact of its future choices. Fiat (see ProModel, 2006) saves $1 million annually in manufacturing costs through simulation. IBM has predicted the behavior of the 230-mile-long Guadalupe River and its many tributaries. The prediction can be made several days before the imminent flood of the river. This is important as it would allow for enough time for disaster management and preparation. IBM used a combination of weather and sensor data to build a river system simulation application that could simulate thousands of river branches at a time. Besides flood prediction, the application could also be used for irrigation planning in such a way as to avoid the impact of droughts and surplus water. Even companies under financial stress need to invest in such solutions to