Academic literature on the topic 'Mixed-integer model'

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Journal articles on the topic "Mixed-integer model"

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Westerlund, Joakim, Mattias Hästbacka, Sebastian Forssell, and Tapio Westerlund. "Mixed-Time Mixed-Integer Linear Programming Scheduling Model." Industrial & Engineering Chemistry Research 46, no. 9 (2007): 2781–96. http://dx.doi.org/10.1021/ie060991a.

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Wu, Jie, and Zhixiang Zhou. "A mixed-objective integer DEA model." Annals of Operations Research 228, no. 1 (2011): 81–95. http://dx.doi.org/10.1007/s10479-011-0938-8.

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Östermark, Ralf, Hans Skrifvars, and Tapio Westerlund. "A nonlinear mixed integer multiperiod firm model." International Journal of Production Economics 67, no. 2 (2000): 183–99. http://dx.doi.org/10.1016/s0925-5273(00)00019-0.

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Navarro, H., S. Nooshabadi, and J. A. Montiel-Nelson. "Adder model for mixed integer linear programming." Electronics Letters 45, no. 7 (2009): 348. http://dx.doi.org/10.1049/el.2009.3637.

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Teunissen, Peter J. G. "Ambiguity-Resolved Model Tests for Carrier-Phase GNSS." Applied Sciences 15, no. 7 (2025): 3531. https://doi.org/10.3390/app15073531.

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Although the theory of mixed-integer inference is well developed for GNSS parameter estimation, such is not yet the case for the validation and monitoring of mixed-integer GNSS carrier-phase models. It is the goal of this research to contribute to this field by introducing a class of mixed-integer model (MIM) tests for carrier-phase GNSS. Members from this class and their distributional properties are worked out for different model validation applications relevant to GNSS, such as detection, identification, significance testing, and integer testing. The power performance of the various tests i
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Walker, H. Douglas, and Stephen W. Preiss. "Operational Planning Using Mixed Integer Programming." Forestry Chronicle 64, no. 6 (1988): 485–88. http://dx.doi.org/10.5558/tfc64485-6.

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A mathematical model was constructed and used to help plan five-year timber harvesting and delivery activities from an industrially managed public forest in Ontario. Harvest systems, harvest levels, and wood flows from compartments within the forest to various mills and delivery points were scheduled to minimize costs. The mathematical structure of the model may suggest applications to related forest planning problems. The model was useful in addressing the planning problem, and model results were used within the company's planning process. Data accuracy problems precluded assessing definitive
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Williams, H. P., and R. G. Jeroslow. "Logic-Based Decision Support: Mixed Integer Model Formulation." Journal of the Operational Research Society 41, no. 4 (1990): 357. http://dx.doi.org/10.2307/2583807.

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Williams, H. P. "Logic-Based Decision Support: Mixed Integer Model Formulation." Journal of the Operational Research Society 41, no. 4 (1990): 357–58. http://dx.doi.org/10.1057/jors.1990.58.

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Sayeed, Q. A., and E. C. De Meter. "Mixed-Integer Programming Model for Fixture Layout Optimization." Journal of Manufacturing Science and Engineering 121, no. 4 (1999): 701–8. http://dx.doi.org/10.1115/1.2833111.

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Workpiece deformation during machining is a significant source of machined feature geometric error. This paper presents a linear, mixed integer programming model for determining the optimal locations of locator buttons, supports, and their opposing clamps for minimizing the affect of static workpiece deformation on machined feature geometric error. This model operates on discretized candidate regions as opposed to continuous candidate regions. In addition it utilizes a condensed FEA model of the workpiece in order to minimize model size and computation expense. This model has two advantages ov
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Xu, Gang, and Lazaros G. Papageorgiou. "A mixed integer optimisation model for data classification." Computers & Industrial Engineering 56, no. 4 (2009): 1205–15. http://dx.doi.org/10.1016/j.cie.2008.07.012.

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Dissertations / Theses on the topic "Mixed-integer model"

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Nordsveen, Espen T. "Mixed Integer Model Predictive Control of Multiple Shale Gas Wells." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18400.

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Horizontal wells with multistage hydraulic fracturing are today the most important drilling technology for shale gas extraction. Considered unprofitable before, the production has now become economically profitable due to advances in technology. Shales main characteristics is its low permeability, making the gas challenging and expensive to extract. Hydraulic fracturing stimulates the wells by creating additional conductivity, making the gas flows from storage pores to the well. This flow only possible in a short time scale, and states the need for multistage fracturing. Shale gas flow theref
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Ali, Md Kamar. "Stream water quality management a stochastic mixed-integer programming model /." Morgantown, W. Va. : [West Virginia University Libraries], 2002. http://etd.wvu.edu/templates/showETD.cfm?recnum=2450.

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Thesis (Ph. D.)--West Virginia University, 2002.<br>Title from document title page. Document formatted into pages; contains xi, 158 p. : ill. (some col.), col. map. Vita. Includes abstract. Includes bibliographical references (p. 126-132).
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Salehpour, Soheil. "Fault detection and model quality estimation using mixed integer linear programming /." Luleå : Luleå University of Technology, 2009. http://pure.ltu.se/ws/fbspretrieve/2740260.

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Brooks, J. Paul. "Solving a mixed-integer programming formulation of a classification model with misclassification limits." Diss., Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-08232005-133023/.

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Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2006.<br>Prausnitz, Mark, Committee Member ; Vidakovic, Brani, Committee Member ; Lee, Eva, Committee Chair ; Nemhauser, George, Committee Member ; Johnson, Ellis, Committee Member. Includes bibliographical references.
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Tang, Yuehao. "A mixed integer-linear programming model for solving the hydroelectric unit maintenance scheduling problem." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31269.

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The unit maintenance scheduling problem is traditionally a challenging topic for electric utilities. In general, the system operators schedule the generating units for maintenance periodically. Research on the maintenance scheduling problem started in 1960's in the last century and has continued until today. Although significant contributions were made as a results of this continuous investigations, but many of those approaches were made in specific areas as different systems have their own special characteristics, such as system reliability considerations. According to the literature ci
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Bajany, Mpyana Danny. "A mixed integer linear programming model for truck-shovel scheduling to minimize fuel consumption." Diss., University of Pretoria, 2005. http://hdl.handle.net/2263/66190.

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In the mining industry, methods to reduce the fuel consumed in the haulage operations are largely sought as a result of the growing energy demand, fuel cost increases and adverse environmental impacts due to the emission of greenhouse gases. Fuel consumption reduction in the open-pit mine operations can be achieved by improving the performance efficiency, technology efficiency, equipment efficiency and operation efficiency of trucks, shovels and the truck-shovel dispatching system. The study conducted in this work lies within the operational strategies that seek to improve the operational effi
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Bürger, Adrian [Verfasser], and Moritz [Akademischer Betreuer] Diehl. "Nonlinear mixed-integer model predictive control of renewable energy systems : : methods, software, and experiments." Freiburg : Universität, 2020. http://d-nb.info/1225682150/34.

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Monmousseau, Philippe. "Scheduling of a Constellation of Satellites: Improving a Simulated Annealing Model by Creating a Mixed-Integer Linear Model." Thesis, KTH, Optimeringslära och systemteori, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-179300.

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The purpose of this thesis is to provide a new scheduling model of a large constellation of imaging satellites that does not use a heuristic solving method. The objective is to create a mixed-integer linear model that would be competitive in speed and in its closeness to reality against a current model using simulated annealing, while trying to improve both models. Each satellite has the choice between a number of possible events, each event having a utility and a cost, and the chosen schedule must take into account numerous time-related constraints. The main difficulties appeared in modeling
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Lundmark, Olle. "Evaluation of mixed integer linear programming solvers when applied to a biogas reactor system model." Thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-155344.

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Optimization of a biogas reactor system, to maximize its profits, is a natural step to become a more competitive electricity source. This is done by first formulating mixed integer linear program (MILP), of an abstract biogas reactorsystem, which is then implemented with parameters to simulate a real biogas reactor system. Then to solve a MILP problem, a solver is used. Depending on what solver is used to solve the problem, the run-time and solution may differ a lot. That is the reason why FarmBioTech AB is interested in the evaluation of the solvers Cplex, Gurobi, Glpk, and Cbc when applied t
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Alqattan, Nael Abdulhameed. "A Multi-Period Mixed Integer Linear Programming Model for Desalination and Electricity Co-generation in Kuwait." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5167.

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Water is the root of life and the engine that drives agriculture, industry, economy and services. The demand for water often necessitates desalination, particularly in arid coastal environments where there are several desalination technologies in use today such as Multi-Effect Distillation (MED) and Reverse Osmosis (RO). The key utility requirement for technologies such as desalination and population in general include energy in one form or another. Therefore, desalination and co-generation are often integrated. Another key utility is electricity which is generated from either renewable or non
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Books on the topic "Mixed-integer model"

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Jeroslow, Robert G. Logic-based decision support: Mixed integer model formulation. North-Holland, 1989.

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Glen, John J. A mixed integer programming model for fisheries management. University of Edinburgh, Management School, 1994.

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Kirches, Christian. Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control. Vieweg+Teubner Verlag, 2011. http://dx.doi.org/10.1007/978-3-8348-8202-8.

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Glen, John J. A mixed integer programming model of a single species fishery. Department of Business Studies, University of Edinburgh, 1990.

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Iyengar, A. V. K. Optimal location of cement plants: Using mixed integer linear programming model. Institute for Financial Management and Research, 1992.

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Pochet, Yves. Production planning by mixed integer programming. Springer, 2011.

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Floudas, Christodoulos A. Nonlinear and mixed-integer optimization: Fundamentals and applications. Oxford University Press, 1995.

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McGarvey, David C. Mixed-strategy solutions to integer Prim-Read defense problems. Rand, 1987.

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McGarvey, David C. Mixed-strategy solutions to integer Prim-Read defense problems. Rand, 1987.

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Greg, Jones J., and Intermountain Research Station (Ogden, Utah), eds. A Heuristic process for solving mixed-integer land management and transportation planning models. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1991.

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Book chapters on the topic "Mixed-integer model"

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Kirches, Christian. "Mixed–Integer Optimal Control." In Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control. Vieweg+Teubner Verlag, 2011. http://dx.doi.org/10.1007/978-3-8348-8202-8_3.

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Kirches, Christian. "Mixed–Integer Real–Time Iterations." In Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control. Vieweg+Teubner Verlag, 2011. http://dx.doi.org/10.1007/978-3-8348-8202-8_5.

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Sadowski, Krzysztof L., Dirk Thierens, and Peter A. N. Bosman. "Combining Model-Based EAs for Mixed-Integer Problems." In Parallel Problem Solving from Nature – PPSN XIII. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10762-2_34.

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Bouhtou, Mustapha, Jean-Robin Medori, and Michel Minoux. "Mixed Integer Programming Model for Pricing in Telecommunication." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21527-8_70.

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Johnson, Ellis L. "Modeling and Strong Linear Programs for Mixed Integer Programming." In Algorithms and Model Formulations in Mathematical Programming. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-83724-1_1.

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Inui, Nobuo, and Yuuji Shinano. "Minimizing State Transition Model for Multiclassification by Mixed-Integer Programming." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11579427_48.

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Haugland, Dag, Kurt Jornsten, and Ebrahim Shayan. "A Mixed Integer Model of Petroleum Fields with Moving Platforms." In Recent Developments in Mathematical Programming. CRC Press, 2022. http://dx.doi.org/10.1201/9780429333439-19.

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Lopez-Perez, Jesus. "Elasticities on a Mixed Integer Programming Model for Revenue Optimization." In Digital Era and Fuzzy Applications in Management and Economy. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94485-8_12.

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Kirches, Christian. "Introduction." In Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control. Vieweg+Teubner Verlag, 2011. http://dx.doi.org/10.1007/978-3-8348-8202-8_1.

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Kirches, Christian. "Numerical Results." In Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control. Vieweg+Teubner Verlag, 2011. http://dx.doi.org/10.1007/978-3-8348-8202-8_10.

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Conference papers on the topic "Mixed-integer model"

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Wei, Zihong, Yike Lu, and Yu Zhang. "Crop Planting Optimization Based on Random Number Method and Mixed Integer Planning Model." In 2025 IEEE International Conference on Electronics, Energy Systems and Power Engineering (EESPE). IEEE, 2025. https://doi.org/10.1109/eespe63401.2025.10987070.

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Mitrai, Ilias, and Prodromos Daoutidis. "Machine Learning-Based Initialization of Generalized Benders Decomposition for Mixed Integer Model Predictive Control." In 2024 American Control Conference (ACC). IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644418.

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McAllister, Robert D., and James B. Rawlings. "Advances in Mixed-Integer Model Predictive Control." In 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867869.

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Lopez, Julio Cesar, J. R. S. Mantovani, and Javier Contreras Sanz. "Mixed-integer convex model for VAr expansion planning." In 2014 IEEE Power & Energy Society General Meeting. IEEE, 2014. http://dx.doi.org/10.1109/pesgm.2014.6939057.

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Sayeed, Quazi A., and Edward C. De Meter. "Mixed-Integer Programming Model for Fixture Layout Optimization." In ASME 1998 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/imece1998-1104.

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Abstract Workpiece deformation during machining is a significant source of machined feature geometric error. This paper presents a linear, mixed integer programming model for determining the optimal locations of locator buttons, supports, and their opposing clamps for minimizing the affect of static workpiece deformation on machined feature geometric error. This model operates on discretized candidate regions as opposed to continuous candidate regions. In addition it utilizes a condensed FEA model of the workpiece in order to minimize model size and computation expense. This model has two adva
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Saif, M. A., R. Kumar, and M. Shanyoor. "Mixed Integer, Linear Programming Model for Multireservoir Strategic Planning." In Middle East Oil Show. Society of Petroleum Engineers, 1987. http://dx.doi.org/10.2118/15759-ms.

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Karg, Benjamin, and Sergio Lucia. "Deep learning-based embedded mixed-integer model predictive control." In 2018 17th European Control Conference (ECC). IEEE, 2018. http://dx.doi.org/10.23919/ecc.2018.8550234.

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De Mauri, Massimo, Wim Van Roy, Joris Gillis, Jan Swevers, and Goele Pipeleers. "Real Time Iterations for Mixed-Integer Model Predictive Control." In 2020 European Control Conference (ECC). IEEE, 2020. http://dx.doi.org/10.23919/ecc51009.2020.9143646.

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Xu, Hailin, Maosheng Ding, and Duo Geng. "A mixed integer programming model for intra-day microgrid scheduling." In TENCON 2015 - 2015 IEEE Region 10 Conference. IEEE, 2015. http://dx.doi.org/10.1109/tencon.2015.7372844.

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Xue-Ming Yuan and Soumen Saha. "A mixed integer programming model for production inventory network design." In 2008 6th IEEE International Conference on Industrial Informatics (INDIN). IEEE, 2008. http://dx.doi.org/10.1109/indin.2008.4618218.

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Reports on the topic "Mixed-integer model"

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Malarín, Hector R., and William J. Vaughan. An Approach to the Economic Analysis of Solid Waste Disposal Alternatives. Inter-American Development Bank, 1997. http://dx.doi.org/10.18235/0008803.

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In this best practice paper a mixed integer optimization approach to the selection of sanitary landfill site sizes and locations in a regional context is explained and illustrated using stylized cost and location information adapted from a real case study. After reviewing the basics of the heuristic approach to solid waste disposal site selection, the optimization model is laid out and solved in the case study context, initially in terms of financial costs. In a subsequent section, a way of incorporating the relative environmental damages of alternative locations into the model is suggested, a
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Liebchen, Christian. An Integer Programming Model to Assign Train Drivers to Good Positions in Basic Turni. Technische Hochschule Wildau, 2024. http://dx.doi.org/10.15771/1935.

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We are reporting on a project on duty rostering of a train operating company in Germany. There, in the past, rostering took place on a purely individual basis. As the goal of the project, the train drivers should work according to a set of 20 essentially fixed basic turni. These plans took into effect from January 2024 on. In this extended abstract, we are focusing on a specific task that had to be resolved within the transition process. In particular, the fixed turni are intended to repeat periodically, most of them after 17 weeks. In particular, for that one turnus is equipped with train dri
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Alt, Jonathan, Willie Brown, George Gallarno, John Richards, Jennifer Olszewski, and Titus Rice. Risk-based prioritization of operational condition assessments : methodology and case study results. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/46123.

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USACE operates, maintains, and manages more than $232 billion of the Nation’s water resource infrastructure. USACE uses the Operational Condition Assessment (OCA) to allocate limited resources to assess condition of this infrastructure in efforts to minimize risks associated with performance degradation. The analysis of risk associated with flood risk management (FRM) assets includes consideration of how each asset contributes to its associated FRM watershed system, understanding the consequences of the asset’s performance degradation, and a determination of the likelihood that the asset will
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Sen, Suvrajeet. Models and Algorithms Involving Very Large Scale Stochastic Mixed-Integer Programs. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada546972.

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An Optimization Model for Die Sets Allocation to Minimize Supply Chain Cost. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-5057.

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In this paper, a novel mixed-integer programming model is developed to optimally assign the die sets to candidate plants to minimize the total costs. The total costs include freight shipping stamped parts to assembly plants, die set movement, outsourcing, and utilization. Therefore, the objective function is weighted multi-criteria and it takes into consideration some of the key constraints in the real-world condition including “must-move die sets”. An optimization tool has been developed that takes several inputs and feeds them as the input to the mathematical model and generates the optimal
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