Control Engineering Practice 11 (2003) 733–764 A survey of industrial model predictive control technology S. Joe Qina‚*‚ Thomas A. Badgwellb‚1 a Department of Chemical Engineering‚ The University of Texas at Austin‚ 1 Texas Lonhorns‚ C0400‚ Austin‚ TX 78712‚ USA b Aspen Technology‚ Inc.‚ 1293 Eldridge Parkway‚ Houston‚ TX 77077‚ USA Received 8 November 2001; accepted 31 August 2002 Abstract This paper provides an overview of commercially available model predictive control (MPC) technology
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Chapter 13 Cost Planning for The Product Life Cycle: Target Costing‚ Theory of Constraints‚ And Strategic Pricing Cases |13-1 |California-Illini Manufacturing (The Theory of Constraints) | |13-2 |Blue Ridge Manufacturing (B) | |13-3 |Nebraska Toaster Company (Target Costing)
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policy portfolio but higher expected return. iv) Repeat question ii using the constraints in Exhibit 13. v) Repeat question iii using the constraints in Exhibit 13. vi) Consider the following seven asset classes: Domestic Equity‚ Foreign Equity‚ Emerging Markets‚ Private Equity‚ Commodities‚ Inflation-Indexed Bonds‚ and Cash. Using HMC’s input assumptions (see Exhibit 11‚ and also using the constraints shown on Page 22‚ Exhibit 12)‚ what would be the allocation across these seven security
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OR PRACTICE SCHEDULING A MAJOR COLLEGE BASKETBALL CONFERENCE GEORGE L. NEMHAUSER Georgia Institute of Technology‚ Atlanta‚ Georgia MICHAEL A. TRICK Carnegie Mellon University‚ Pittsburgh‚ Pennsylvania (Received February 1997; revision received May 1997; accepted July 1997) The nine universities in the Atlantic Coast Conference (ACC) have a basketball competition in which each school plays home and away games against each other over a nine-week period. The creation of a suitable schedule
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expansion strategies require determining: Amount‚ time‚ Form of capacity changes. The Theory of Constraints (TOC) is a set of principles that focuses on increasing total process throughput by maximizing the utilization of all bottleneck work activities and workstations. Throughput: Production Rate – in the end: Amount of money generated per time period through actual output that becomes actual sales. Constraint: Anything that limits an organization from
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European Molecular Biology Laboratory. Retrieved June 30‚ 2010. Mount DM. (2004). Bioinformatics: Sequence and Genome Analysis 2nd ed. Cold Spring Harbor Laboratory Press: Cold Spring Harbor‚ NY. Papadopolous‚ J. S. and Agarwala‚ R. (2007) COBALT: a constraint-based alignment tool for multiple protein sequences. Bioinformatics 23(9): 1073-1079. Zhang‚ X and Kahveci‚ T(2006).ANewApproach forAlignment of multiple proteins. Pac. Symp. Biocomput.‚ 11: 339350. Ogden‚T.H. and Rosenberg‚ M.S. (2006) Multiple
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2 to produce. Objective function Maximize 4.0x1 + 3.6x2 Constraints Constraint 1: 11x1 + 5x2 > 55 Constraint 2: 3x1 + 4x2 < 36 Constraint 3: 4x1 – 9x2 < 0 Nonnegativity: x1‚ x2 >= 0 Solve this linear programming model by using the graphical approach (Graph paper is provided on the next page). For your graphical solution‚ Label the axes. Draw and label each constraint. Show your procedure of drawing Constraint 3 only. For each constraint line‚ determine and label which side is feasible. Briefly
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PORTFOLIO MANAGEMENT OUTLINE (PART ONE): I. The Rationale for Portfolio Management; II. Investor Objectives and Constraints; III. Risk and Return Profile of Philippine Financial Assets; IV. Traditional Portfolio Management; V. Modern Portfolio Theory; VI. Implications of Diversifications on Portfolio Management; and VII. Investing in Managed Portfolios. I. The Rationale for Portfolio Management: a.) To balance investor objectives and available investment opportunities; b.) b)
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ARTICLE IN PRESS Technovation 28 (2008) 633–643 www.elsevier.com/locate/technovation Post-project reviews as a key project management competence Frank T. Anbaria‚c‚Â Elias G. Carayannisb‚c‚1‚ Robert James Voetschd‚2 a Department of Decision Sciences‚ School of Business‚ Funger Hall 415‚ 2201 G Street‚ NW Washington‚ DC‚ USA Department of Information Systems and Technology Management‚ School of Business‚ Funger Hall 415‚ 2201 G Street‚ NW Washington‚ DC‚ USA c The George Washington
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description of each constraint Allowed Calories Allowed Carbohydrates Allowed Protein Allowed Vitamins 5. Write the right-hand side of each constraint 2700 300 250 60 6. Write <‚ =‚ or > for each constraint ≤ 2700 ≥ 300 ≥ 250 ≥ 60 7. Write all the decision variables on the left-hand side of each constraint x1 x2 x3 x4 ≤ 2700 x1 x2 x3 x4 ≥ 300 x1 x2 x3 x4 ≥ 250 x1 x2 x3 x4 ≥ 60 8. Write the coefficient for each decision in each constraint 160 x1 + 110 x2 + 90
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