Relevant bibliographies by topics / Capacity Planning

Academic literature on the topic 'Capacity Planning'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Capacity Planning"

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Wallace, Robert E. "Planning Computer Capacity." Journal of Information Systems Management 2, no. 2 (January 1985): 72–74. http://dx.doi.org/10.1080/07399018508967758.

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Jinks, Daniel W. "BETTER CAPACITY PLANNING." Journal of Information Systems Management 8, no. 4 (January 1991): 76–79. http://dx.doi.org/10.1080/07399019108965018.

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Wombell, S. "Network capacity planning." IEE Review 45, no. 2 (March 1, 1999): 79–80. http://dx.doi.org/10.1049/ir:19990211.

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Permana, Sidik, Meri Andriani, and Dewiyana Dewiyana. "Production Capacity Requirements Planning Using The Capacity Method Requirement Planning." International Journal of Engineering, Science and Information Technology 1, no. 4 (November 5, 2021): 36–40. http://dx.doi.org/10.52088/ijesty.v1i4.165.

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PT. ABC is a company engaged in the manufacture of Dolomite Fertilizer. The number of requests is greater than the amount of production. Due to fluctuations in the number of requests that tend to increase, this occurs due to a lack of capacity at the workstation. Therefore, it is necessary to calculate the capacity requirements planning analysis for each work station to know the company's capacity needs. The research aims to identify the shortage/excess production capacity and provide proposals for the balance of production capacity at PT. A B C. Production capacity research was conducted using the Capacity Requirement Planning (CRP) method. Results and Discussion, comparison of available capacity and required capacity (load) are as follows, work station jaw crusher available capacity 36.74 hours/week while required capacity (load) 36 hours/week, work station bucket elevator available capacity 36, 74 hours/week while the required capacity (load) is 14.4 hours/week, work station ball mill available capacity is 36.74 hours/week while the required capacity (load) is 45.6 hours/week, and work station silo flour the available capacity is 36.74 hours/week while the required capacity (load) is 51.59 hours/week. In conclusion, two stations experience excess capacity, namely the jaw crusher work station with an excess capacity of 0.74 hours/week and the bucket elevator work station with an excess capacity of 22.34 hours/week. The other two work stations experienced a lack of capacity, namely the ball mill work station with a capacity shortage of 22.34 hours/week and the silo flour work station with a capacity shortage of 14.85 hours/week. Efforts to balance capacity by scheduling overtime and adding equipment (machinery) to work centers that lack capacity, so that the company's production targets are achieved.
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Solak, Senay, John-Paul B. Clarke, and Ellis L. Johnson. "Airport terminal capacity planning." Transportation Research Part B: Methodological 43, no. 6 (July 2009): 659–76. http://dx.doi.org/10.1016/j.trb.2009.01.002.

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Jodlbauer, Herbert, and Sonja Strasser. "Capacity-driven production planning." Computers in Industry 113 (December 2019): 103126. http://dx.doi.org/10.1016/j.compind.2019.103126.

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Garth, Mike. "Benefits of capacity planning." Data Processing 27, no. 10 (December 1985): 9–12. http://dx.doi.org/10.1016/0011-684x(85)90027-9.

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Ceryan, O., and Y. Koren. "Manufacturing capacity planning strategies." CIRP Annals 58, no. 1 (2009): 403–6. http://dx.doi.org/10.1016/j.cirp.2009.03.034.

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Chakravarty, Amiya, and Hemant K. Jain. "Distributed Computer System Capacity Planning and Capacity Loading." Decision Sciences 21, no. 2 (June 1990): 253–62. http://dx.doi.org/10.1111/j.1540-5915.1990.tb01684.x.

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Marot, Naja. "Planning capacity of Slovenian municipalities." Acta geographica Slovenica 50, no. 1 (December 15, 2010): 131–57. http://dx.doi.org/10.3986/ags50106.

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Dissertations / Theses on the topic "Capacity Planning"

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Alsultan, Sami H. "Air fleet capacity planning." Thesis, University of Salford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308089.

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Niles, Augusta (Augusta L. ). "Stochastic capacity modeling to support demand/capacity gap planning." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90770.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2014. In conjunction with the Leaders for Global Operations Program at MIT.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014. In conjunction with the Leaders for Global Operations Program at MIT.
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Cataloged from PDF version of thesis.
Includes bibliographical references (pages 62-63).
Capacity strategy has established methods of dealing with uncertainty in future demand. This project advances the concept of capacity strategy under conditions of uncertainty in cases where capacity is the primary source of uncertainty. Novartis Vaccines, one of five divisions of Novartis AG, produces nearly two dozen vaccines which are offered in syringes, vials, multi or single pack, and multi or single dose and delivered in language-specific packaging to countries all over the world. Bexsero is a new product in 2013. As demand for Bexsero and other products increases over the next ten years, the production lines used to package them will need to accommodate more and more volume. Capacity planning compares capacity gaps between future demand and current estimated capacity. Because of recurring shortfalls in production relative to planned capacity, current estimates of capacity are not trusted for long-term planning. Understanding how international product demand will be allocated to each production line and what drives current capacity limitations will help Novartis Vaccines prioritize investment to optimally develop this capacity over time. Thus, the purpose of this model is to establish baseline capacity estimates using historical data and allow for the simulation of new production scenarios in order to demonstrate the impact of production policy on mean and variance of capacity over a specified time horizon. Incorporating simulated results produces a mean and standard deviation of capacity we are likely to see. Long-term demand was assessed, capacity versus peak demand views were created, and production scenarios were simulated on a single line/product/format basis over the time horizon to determine expected capacity. Recommendations were made for each of the pre-filled syringe, multi-format, and vial format lines and these results were used to shape an overall packaging capacity development plan.
by Augusta Niles.
M.B.A.
S.M.
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Verma, Manoj Kumar. "Capacity planning under fuzzy environment." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ57589.pdf.

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Rajendran, Hari Kumar. "Process quality and capacity planning." Thesis, Wichita State University, 2008. http://hdl.handle.net/10057/2040.

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Production planning is a function performed in isolation from process capability to estimate available capacity. Process capability is a systematic study performed to understand the process performance. After a complete review of the literature available on capacity and capability a gap was identified between them. This research is aimed at proposing a model for representing a relationship between machine capacity and performance capability. Also presented are the impact of capability on capacity utilization and capacity planning. A traditional machine capacity calculation model is replaced with a modified model, which incorporates the yield percentage. Where, capacity is estimated as a product of available time, productivity and yield percentage .The yield percentage is estimated based on the performance capability .A systematic methodology is provided for the manufacturer to arrive at identify the root cause of capacity related problems. The importance of quality in capacity planning is emphasized by explaining the effects of deviation to capacity plan that can occur due to variability in the process. A case study is carried out in an aircraft company on a single machine to estimate performance capability and capacity of the machine in comparison to the demand. The results from case study indicate that there exists a 32% deviation from the required capacity calculated considering the process performance. The manufacturer decision based on outcome of the proposed model, points out the need for improving both productivity and utilization of the machine. An alternative to the current decision was also presented to the manufacturer, to increase the available time of the machine that is to increase the machine operation time from 7.6 Hrs to 10 Hrs in order to meet customer demand. It is left to the discretion of the manufacturer to decide on a corrective action after giving due consideration for the costs involved in the solution to meet customer demand.
Thesis (M.S.) - Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering
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HARROD, STEVEN S. "RAILWAY CAPACITY MANAGEMENT AND PLANNING." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1186181286.

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Rajendran, Hari Kumar Weheba Gamal S. "Process quality and capacity planning." A link to full text of this thesis in SOAR, 2008. http://hdl.handle.net/10057/2040.

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Gatica, Diaz Escobar Gabriel. "Capacity planning under clinical trials uncertainty." Thesis, Imperial College London, 2004. http://hdl.handle.net/10044/1/8400.

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Pang, Jee Fung. "Characterizing user workload for capacity planning." Thesis, University of British Columbia, 1986. http://hdl.handle.net/2429/26021.

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With the widespread use of computers in today's industry, planning system configurations in computer sites plays an increasingly important role. The process of planning system configurations or determining hardware requirements for new or existing systems is commonly known as capacity planning among performance researchers and analysts. This thesis presents a refined capacity planning process for centralized computing system, with special attention to characterizing user workload for capacity planning. The objective is to make the entire process simpler for the computer user community, while relieving the capacity planner or performance analyst from having to rely on guesswork for the user workload performance factors. The process is divided into four phases; namely, data collection, data reduction, workload/user classification and, modeling and performance analysis. The second and third phases are collectively known as user workload characterization. The main objective of our workload characterization is to avoid any guess work on the performance factors that cannot be easily measured. The results of the workload characterization process are specifically meant to be used in analytic and simulation modeling. Three software tools required for the data reduction, workload/user classification and performance analysis phases have been developed and are discussed in the thesis.
Science, Faculty of
Computer Science, Department of
Graduate
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Balaraman, Subha. "Bill Share - Capacity Planning and Management." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1339698740.

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Yarde, Richard Roy 1969. "State capacity for water resources planning." Thesis, The University of Arizona, 1997. http://hdl.handle.net/10150/291640.

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Since the end of federal funds from the Water Resources Planning Act of 1964, it has been largely the responsibility of the states to plan for their water resources. This study will report on the current status of state water planning, suggest some variables that may have an influence on a state's decision to prepare a state water plan, and test the variables through statistical analysis. Some of the variables that are suggested as having some influence on state water planning are precipitation, population density, large metropolitan areas, median per capita income (as a measure of state affluence), and percent of land irrigated. Among these, it is only precipitation that has a clearly significant correlation to the preparation of a state water plan. It is concluded that no single variable is an accurate predictor of state behavior, but that a combination of variables act together to influence state behavior.
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Books on the topic "Capacity Planning"

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Auditor-General, Victoria Office of the. Prison capacity planning. Melbourne, Vic: Victorian Government Printer, 2012.

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Alsultan, Sami h. Air fleet capacity planning. Salford: University of Salford, 1995.

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Washington (State). Legislature. Joint Legislative Audit and Review Committee. Juvenile rehabilitation capacity planning. Olympia: Joint Legislative Audit and Review Committee, 1997.

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Browning, Tim. Capacity planning for computer systems. Boston: AP Professional, 1995.

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Stewart, John. Capacity planning: Anticipating the need. Chichester: Wiley, 1989.

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Cunico, Hernan. WebSphere Commerce V5.5 capacity planning. [United States]: IBM, International Support Organization, 2004.

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Kleindorfer, Paul R. Optimal capacity planning under stagnating demand. Berlin: Wissenchaftszentrum Berlin, 1985.

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A, Langenwalter Gary, and Lucier Raymond A, eds. Handbook of material & capacity requirements planning. New York: McGraw-Hill, 1993.

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Hung, Chan K., ed. Computer capacity planning: Theory and practice. Boston: Academic Press, 1987.

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Keller, Ted C. CICS: Capacity planning and performance management. New York: McGraw-Hill, 1993.

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Book chapters on the topic "Capacity Planning"

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Sabharwal, Navin, and Prashant Wali. "Capacity Planning." In Cloud Capacity Management, 55–60. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-4924-5_6.

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ElMaraghy, Hoda, and Ahmed M. Deif. "Capacity Planning." In CIRP Encyclopedia of Production Engineering, 1–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-642-35950-7_6388-3.

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Garverick, Josh. "Capacity Planning." In Migrating to Azure, 105–20. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3585-0_6.

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ElMaraghy, Hoda A., and Ahmed M. Deif. "Capacity Planning." In CIRP Encyclopedia of Production Engineering, 181–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-53120-4_6388.

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ElMaraghy, Hoda, and Ahmed M. Deif. "Capacity Planning." In CIRP Encyclopedia of Production Engineering, 123–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-20617-7_6388.

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Toomey, John W. "Capacity Planning." In Mrp II, 94–111. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4615-4117-2_7.

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Peasland, Brian. "Capacity Planning." In Oracle DBA Mentor, 363–80. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4321-3_19.

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Chemuturi, Murali, and Vijay Chemuturi. "Capacity Planning." In Managing of People at Work, 33–57. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338802-3.

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Utley, Martin, and Dave Worthington. "Capacity Planning." In Handbook of Healthcare System Scheduling, 11–30. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4614-1734-7_2.

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Goel, Himanshu. "Capacity Requirement Planning." In Handbook for SAP PP in S/4HANA, 201–25. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8566-4_7.

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Conference papers on the topic "Capacity Planning"

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Besbes, Omar, Francisco Castro, and Ilan Lobel. "Spatial Capacity Planning." In EC '19: ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3328526.3329614.

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Heath, William. "Waterfront capacity-planning simulations." In the 25th conference. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/256563.257028.

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Duin, Heiko, Peter Hofbauer, Omer Karacan, Erich Markl, Josef Withalm, Walter Wolfel, and Darius Zand. "Collaborative Demand Capacity Planning." In 2009 IEEE International Technology Management Conference (ICE). IEEE, 2009. http://dx.doi.org/10.1109/itmc.2009.7461413.

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Rizk, Lynn, Farook Hamzeh, and Samir Emdanat. "Introducing New Capacity Planning Metrics in Production Planning." In 25th Annual Conference of the International Group for Lean Construction. International Group for Lean Construction, 2017. http://dx.doi.org/10.24928/2017/0203.

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Jiang, Yexi, Chang-shing Perng, Tao Li, and Rong Chang. "Self-Adaptive Cloud Capacity Planning." In 2012 IEEE International Conference on Services Computing (SCC). IEEE, 2012. http://dx.doi.org/10.1109/scc.2012.8.

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Babu, Neela P., Jan Kuakkel, Daniel P. Loucks, and Warren E. Walker. "Capacity Planning under Nonstationary Uncertainties." In World Environmental and Water Resources Congress 2011. Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41173(414)393.

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Castellani, Chiara, Ivan Famoso, Luca Galli, Carlo Zelli, and Tommaso Parrinello. "GMES Mission Capacity Planning Tool." In SpaceOps 2008 Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-3489.

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Waldman, Helio, Rodrigo Campos Bortoletto, and Gustavo Sousa Pavani. "Network capacity planning under competition." In 2010 Asia Communications and Photonics Conference and Exhibition (ACP 2010). IEEE, 2010. http://dx.doi.org/10.1109/acp.2010.5682851.

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Mitchell, Nick, and Peter F. Sweeney. "On-the-fly capacity planning." In SPLASH '13: Conference on Systems, Programming, and Applications: Software for Humanity. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2509136.2509540.

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Babu, Neela P., Jan Kuakkel, Daniel P. Loucks, and Warren E. Walker. "Capacity Planning under Nonstationary Uncertainties." In First International Symposium on Uncertainty Modeling and Analysis and Management (ICVRAM 2011); and Fifth International Symposium on Uncertainty Modeling and Anaylsis (ISUMA). Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41170(400)101.

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Reports on the topic "Capacity Planning"

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Diaz, Paul, Ryan King, Devon Sigler, Wesley Cole, and Wesley Jones. Uncertainty Quantification for Capacity Expansion Planning. Office of Scientific and Technical Information (OSTI), June 2020. http://dx.doi.org/10.2172/1659787.

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Lu, Qingsong, Betsy George, and Shashi Shekhar. Capacity Constrained Routing Algorithms for Evacuation Route Planning. Fort Belvoir, VA: Defense Technical Information Center, May 2006. http://dx.doi.org/10.21236/ada447888.

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Smith, Megan. Providing Capacity in Rural Communities: Planning for Alternative Transportation. Portland State University Library, March 2011. http://dx.doi.org/10.15760/trec.94.

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Dunleavy, G. J. Configuration Management, Capacity Planning Decision Support, Modeling and Simulation. Fort Belvoir, VA: Defense Technical Information Center, December 1988. http://dx.doi.org/10.21236/ada218302.

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Tonn, B., Ho-Ling Hwang, S. Elliot, J. Peretz, R. Bohm, and B. Hendrucko. Methodologies for estimating one-time hazardous waste generation for capacity generation for capacity assurance planning. Office of Scientific and Technical Information (OSTI), April 1994. http://dx.doi.org/10.2172/10191446.

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Mate, D., B. Bowron, B. Brown, G. Davidson, and P. Hill. Building climate change adaptation capacity in the canadian planning community. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2012. http://dx.doi.org/10.4095/290173.

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Keen, Jeremy, Julieta Giraldez, Elizabeth Cook, Andy Eiden, Scott Placide, Alan Hirayama, Brian Monson, David Mino, and Fathalla Eldali. Distribution Capacity Expansion Planning: Current Practice, Opportunities, and Decision Support. Office of Scientific and Technical Information (OSTI), November 2022. http://dx.doi.org/10.2172/1898008.

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Singh, Kavinesh J., Andy B. Philpott, and R. K. Wood. Dantzig-Wolfe Decomposition for Solving Multi-Stage Stochastic Capacity-Planning Problems. Fort Belvoir, VA: Defense Technical Information Center, July 2005. http://dx.doi.org/10.21236/ada486953.

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VanKuiken, J., and B. Hamilton. Examining advanced fossil-fueled technologies with a utility capacity-planning model. Office of Scientific and Technical Information (OSTI), April 1989. http://dx.doi.org/10.2172/5865706.

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Foreit, James R. FRONTIERS capacity building: An overview. Population Council, 2002. http://dx.doi.org/10.31899/rh2002.1005.

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The Population Council’s Frontiers in Reproductive Health Program is a cooperative agreement with USAID to improve family planning and reproductive health service delivery through operations research (OR). Frontiers builds on more than 20 years of research to improve family planning service delivery programs. A major goal of Frontiers is to transfer skills in OR so that public and private agencies in developing countries can conduct OR and apply research findings to reproductive health programs and policies. OR addresses problems in operational effectiveness, access, quality, and efficiency by investigating facets of programs that managers can control and change. As concluded in this overview report, Frontiers seeks to institutionalize capacity building by increasing the number of service delivery organizations that use OR and increasing the number of research organizations capable of conducting and teaching OR. Investment in capacity building will ensure that OR will make a continuing, significant contribution to reproductive health and family planning programs and policies.
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