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Journal articles on the topic 'Planning Optimization'

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Published: 9 March 2023
Last updated: 10 March 2023

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1

Baltas, D. "INVERSE PLANNING OPTIMIZATION." Radiotherapy and Oncology 92 (August 2009): S99. http://dx.doi.org/10.1016/s0167-8140(12)72851-7.

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2

Ouazene, Yassine, Taha Arbaoui, and Farouk Yalaoui. "Planning and Scheduling Optimization." Applied Sciences 11, no. 19 (September 27, 2021): 8980. http://dx.doi.org/10.3390/app11198980.

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3

Pepelyaev, V. A. "Planning optimization-simulation experiments." Cybernetics and Systems Analysis 42, no. 6 (November 2006): 866–75. http://dx.doi.org/10.1007/s10559-006-0126-z.

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4

Shih, Kuo-Chuan, and Shu-Shun Liu. "AN OPTIMIZATION MODEL FOR PRECAST PROJECT PLANNING USING GROUP CONCEPTS." Journal of the Operations Research Society of Japan 53, no. 3 (2010): 189–206. http://dx.doi.org/10.15807/jorsj.53.189.

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5

Brown, Gerald G., Dennis M. Coulter, and Alan R. Washburn. "Sortie Optimization and Munitions Planning." Military Operations Research 1, no. 1 (June 1, 1994): 13–18. http://dx.doi.org/10.5711/morj.1.1.13.

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6

Melnikova, Yu. "OPTIMIZATION MODEL FOR TRAFFIC PLANNING." Agrosvit, no. 9 (May 20, 2020): 127. http://dx.doi.org/10.32702/2306-6792.2020.9.127.

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7

Harada, Kensuke. "Optimization in Robot Motion Planning." Journal of the Robotics Society of Japan 32, no. 6 (2014): 508–11. http://dx.doi.org/10.7210/jrsj.32.508.

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8

Paudyal, Guna Nidhi, and Ashim Das Gupta. "Irrigation Planning by Multilevel Optimization." Journal of Irrigation and Drainage Engineering 116, no. 2 (March 1990): 273–91. http://dx.doi.org/10.1061/(asce)0733-9437(1990)116:2(273).

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9

Bermon, Stuart, and Sarah Jean Hood. "Capacity Optimization Planning System (CAPS)." Interfaces 29, no. 5 (October 1999): 31–50. http://dx.doi.org/10.1287/inte.29.5.31.

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10

Knowles, Thomas W. "Optimization models for mine planning." Computers & Industrial Engineering 37, no. 1-2 (October 1999): 469–72. http://dx.doi.org/10.1016/s0360-8352(99)00120-5.

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11

Chen, Huixiao, David L. Craft, and David P. Gierga. "Multicriteria optimization informed VMAT planning." Medical Dosimetry 39, no. 1 (2014): 64–73. http://dx.doi.org/10.1016/j.meddos.2013.10.001.

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12

Petro, Clayton C., and Ajita S. Prabhu. "Preoperative Planning and Patient Optimization." Surgical Clinics of North America 98, no. 3 (June 2018): 483–97. http://dx.doi.org/10.1016/j.suc.2018.01.005.

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13

Gurgur, Cigdem Z. "Mid-Cycle Capital Planning Optimization." Engineering Economist 54, no. 3 (September 11, 2009): 250–65. http://dx.doi.org/10.1080/00137910903116214.

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14

Janez, Fabrice. "Optimization method for sensor planning." Aerospace Science and Technology 11, no. 4 (May 2007): 310–16. http://dx.doi.org/10.1016/j.ast.2006.12.005.

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15

Barnard, Richard C., Martin Frank, and Michael Herty. "Treatment Planning Optimization for Radiotherapy." PAMM 13, no. 1 (November 29, 2013): 339–40. http://dx.doi.org/10.1002/pamm.201310165.

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16

Consigli, Giorgio, Paolo Brandimarte, and Daniel Kuhn. "Financial Optimization: optimization paradigms and financial planning under uncertainty." OR Spectrum 37, no. 3 (June 19, 2015): 553–57. http://dx.doi.org/10.1007/s00291-015-0406-y.

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17

Xhaferllari, Ilma, Eugene Wong, Karl Bzdusek, Michael Lock, and Jeff Z. Chen. "Automated IMRT planning with regional optimization using planning scripts." Journal of Applied Clinical Medical Physics 14, no. 1 (January 2013): 176–91. http://dx.doi.org/10.1120/jacmp.v14i1.4052.

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18

Iwamura, Kazuaki, Yosuke Nakanishi, Udom Lewlomphaisarl, Noel Estoperez, and Abraham Lomi. "Facility Planning Optimization Platform, GGOD, for Expandable Cluster-type Micro-grid Installations and Operations." Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences 58, S (October 12, 2021): 101–7. http://dx.doi.org/10.53560/ppasa(58-sp1)742.

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This paper describes the architecture and the utilization for a facility planning optimization platform called GGOD, “Grid of Grids Optimal Designer” and applies it to expandable cluster-type micro-grid installations and operations. The expandable cluster-type micro-grid is defined as a group of micro-grids that are connected by bi-directional power transfer networks. Furthermore, power sources are also networked. Especially, by networking among power sources, powers necessary for social activities in-demand areas are secured. The proposed architecture is based on service-oriented architecture, meaning that optimization functions are executed as services. For flexibility, these services are executed by requests based on extensible mark-up language texts. The available optimizations are written in meta-data, which are accessible to end-users from the meta-data database system called clearinghouse. The meta-data are of two types, one for single optimization and the other for combined optimization. The processes in GGOD are conducted by the management function which interprets descriptions in meta-data. In meta-data, the names of optimization functions and activation orders are written. The basic executions follow sequential, branch, or loop flow processes, which execute combined optimizations, compare more than two kinds of optimization processes, and perform iterative simulations, respectively. As an application of the proposed architecture, the power generation sites and transmission networks are optimized in a geospatial integrated-resource planning scenario. In this application, a structure and a method for the combination of component functions in GGOD are exemplified. Moreover, GGOD suggests promotions of a lot of applications by effective combinations of basic optimization functions.
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19

Rahmalia, Dinita, Teguh Herlambang, and Thomy Eko Saputro. "Fertilizer Production Planning Optimization Using Particle Swarm Optimization-Genetic Algorithm." Journal of Information Systems Engineering and Business Intelligence 5, no. 2 (October 24, 2019): 120. http://dx.doi.org/10.20473/jisebi.5.2.120-130.

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Background: The applications of constrained optimization have been developed in many problems. One of them is production planning. Production planning is the important part for controlling the cost spent by the company.Objective: This research identifies about production planning optimization and algorithm to solve it in approaching. Production planning model is linear programming model with constraints : production, worker, and inventory.Methods: In this paper, we use heurisitic Particle Swarm Optimization-Genetic Algorithm (PSOGA) for solving production planning optimization. PSOGA is the algorithm combining Particle Swarm Optimization (PSO) and mutation operator of Genetic Algorithm (GA) to improve optimal solution resulted by PSO. Three simulations using three different mutation probabilies : 0, 0.01 and 0.7 are applied to PSOGA. Futhermore, some mutation probabilities in PSOGA will be simulated and percent of improvement will be computed.Results: From the simulations, PSOGA can improve optimal solution of PSO and the position of improvement is also determined by mutation probability. The small mutation probability gives smaller chance to the particle to explore and form new solution so that the position of improvement of small mutation probability is in middle of iteration. The large mutation probability gives larger chance to the particle to explore and form new solution so that the position of improvement of large mutation probability is in early of iteration.Conclusion: Overall, the simulations show that PSOGA can improve optimal solution resulted by PSO and therefore it can give optimal cost spent by the company for the planning.Keywords: Constrained Optimization, Genetic Algorithm, Linear Programming, Particle Swarm Optimization, Production Planning
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20

Jeng-Shyang Pan, Jeng-Shyang Pan, Qingyong Yang Jeng-Shyang Pan, Chin-Shiuh Shieh Qingyong Yang, and Shu-Chuan Chu Chin-Shiuh Shieh. "Tumbleweed Optimization Algorithm and Its Application in Vehicle Path Planning in Smart City." 網際網路技術學刊 23, no. 5 (September 2022): 927–45. http://dx.doi.org/10.53106/160792642022092305002.

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<p>With the increasing complexity of optimization problems, the requirements for algorithm optimization capabilities are getting higher and higher. In order to better solve complex optimization problems, this paper proposes a new swarm intelligence optimization algorithm named Tumbleweed Optimization Algorithm (TOA). The TOA algorithm consists of two stages, which simulate the seedling growth phase and seed propagation phase of tumbleweed respectively. The TOA algorithm adopts a multi-group structure to improve the global searching ability of the algorithm. In order to verify the performance of the TOA algorithm in numerical optimization and solving practical application problems, this paper selects the CEC2013 benchmark function library and the vehicle path planning in the smart city for testing. Through the comparison of experimental results, the TOA algorithm can both show strong optimization capabilities. Compared with the other ten intelligent optimization algorithms, the TOA algorithm proposed in this paper can also show strong competitiveness.</p> <p>&nbsp;</p>
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21

C.Kavitha, C. Kavitha, and C. Vijayalakshmi C. Vijayalakshmi. "Design and Implementation of Fuzzy Multi Objective Optimization Model for Production Planning." Indian Journal of Applied Research 3, no. 12 (October 1, 2011): 372–75. http://dx.doi.org/10.15373/2249555x/dec2013/113.

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22

Kim, Jeong-Jung, and Ju-Jang Lee. "Trajectory Optimization With Particle Swarm Optimization for Manipulator Motion Planning." IEEE Transactions on Industrial Informatics 11, no. 3 (June 2015): 620–31. http://dx.doi.org/10.1109/tii.2015.2416435.

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23

Liu, Xiao-jun, Hong Yi, and Zhong-hua Ni. "Application of ant colony optimization algorithm in process planning optimization." Journal of Intelligent Manufacturing 24, no. 1 (May 15, 2010): 1–13. http://dx.doi.org/10.1007/s10845-010-0407-2.

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24

Khalafallah, Ahmed, and Khaled Hesham Hyari. "Optimization Parameter Variation: Improving Biobjective Optimization of Temporary Facility Planning." Journal of Computing in Civil Engineering 32, no. 5 (September 2018): 04018036. http://dx.doi.org/10.1061/(asce)cp.1943-5487.0000780.

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25

Zeleny, Milan. "Asset optimization and multi-resource planning." Human Systems Management 15, no. 3 (1996): 153–55. http://dx.doi.org/10.3233/hsm-1996-15301.

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26

Baioletti, Marco, Alfredo Milani, Valentina Poggioni, and Fabio Rossi. "Ant Search Strategies For Planning Optimization." Proceedings of the International Conference on Automated Planning and Scheduling 19 (October 16, 2009): 334–37. http://dx.doi.org/10.1609/icaps.v19i1.13394.

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In this paper a planning framework based on Ant Colony Optimization techniques is presented. It is well known that finding optimal solutions to planning problems is a very hard computational problem. Stochastic methods do not guarantee either optimality or completeness, but it has been proved that in many applications they are able to find very good, often optimal, solutions. We propose several approaches based both on backward and forward search over the state space, using several heuristics and testing different pheromone models in order to solve sequential optimization planning problems.
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27

Pozzi, Matteo, Andrea Bettinelli, Fabrizio Detassis, Ettore Filippini, Simone Graziani, Stefano Morgione, and Daniele Vigo. "District heating network maintenance planning optimization." Energy Reports 7 (October 2021): 184–92. http://dx.doi.org/10.1016/j.egyr.2021.08.156.

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28

Liu, Zi Fa, Gang Liu, and Wei Zhang. "Substation Optimization Planning Considering Distributed Generation." Advanced Materials Research 732-733 (August 2013): 1314–19. http://dx.doi.org/10.4028/www.scientific.net/amr.732-733.1314.

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This paper established a transformer substation comprehensive optimal planning model considering distribution generation (DG) and different block geographic information factors (GIF), set form, volume, location of existing DG in planning area and transformer substation load-bearing capacity as constraint condition, taking construction cost of distribution transform substation and feeder and operation cost including current supply loss into account, in the meantime, regarding the influence of GIF such as land properties and so on to location and cost of construction with load demand satisfied. Furthermore, influence factors of different block information factor to construction cost were work out by means of interval analytical hierarchy process. On the basis of the established objective function, an particle swarm optimization (PSO) algorithm is proposed to solve the problem in this paper. By empirical study of certain planning area, the proposed model and algorithm are proved to be scientific and effective.
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29

Moret, Stefano, Michel Bierlaire, and François Maréchal. "Robust Optimization for Strategic Energy Planning." Informatica 27, no. 3 (January 1, 2016): 625–48. http://dx.doi.org/10.15388/informatica.2016.103.

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30

Wang, Shaowei, and Chen Ran. "Rethinking cellular network planning and optimization." IEEE Wireless Communications 23, no. 2 (April 2016): 118–25. http://dx.doi.org/10.1109/mwc.2016.7462493.

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31

Mityagin, Sergey D. "Technological Optimization of Town-Planning Activity." Scientific journal “ACADEMIA. ARCHITECTURE AND CONSTRUCTION”, no. 1 (March 22, 2018): 67–72. http://dx.doi.org/10.22337/2077-9038-2018-1-67-72.

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The town-planning code of the Russian Federation allows to prepare the documents regulating design and planning activity independently by each subject of the town-planning relations. At the same time violations in the form of mismatch of information contents between separate town-planning documents are possible. These defects of a legal design can be eliminated by special federal instructive-methodological document preparation which is constructed on the basis of optimization of technological model of town-planning design activity which assumes in particular:1) strict execution of functional priorities establishment tasks of the Russian Federation Town-planning code in territories assignment at the Russian Federation territorial planning schemes level functional zones (land categories) dislocation definition in territorial planning schemes of the territorial subjects of the Russian Federation municipal districts and also in city district master plan drafts city and rural settlements2) town-planning zoning works reference to the level of territory planning documents preparation where the territorial zones of placement of capital construction projects would be established in the borders of planning structure elements of the settlement3) land plots surveying in the borders of territorial zones perform on the basis of the complex of conciliation procedures and public hearings of volume spatial decisions (sketches) of building undergoneThe offered town-planning design actions organization technological model is directed to planning solutions consecutive specification from different level territorial planning schemes, including city district master plans, city and rural settlements to theland plot planning organization schemes of the capital construction projects, i.e. from an investment plan to project realization.
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32

Osa, Takayuki. "Multimodal trajectory optimization for motion planning." International Journal of Robotics Research 39, no. 8 (June 4, 2020): 983–1001. http://dx.doi.org/10.1177/0278364920918296.

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Existing motion planning methods often have two drawbacks: (1) goal configurations need to be specified by a user, and (2) only a single solution is generated under a given condition. In practice, multiple possible goal configurations exist to achieve a task. Although the choice of the goal configuration significantly affects the quality of the resulting trajectory, it is not trivial for a user to specify the optimal goal configuration. In addition, the objective function used in the trajectory optimization is often non-convex, and it can have multiple solutions that achieve comparable costs. In this study, we propose a framework that determines multiple trajectories that correspond to the different modes of the cost function. We reduce the problem of identifying the modes of the cost function to that of estimating the density induced by a distribution based on the cost function. The proposed framework enables users to select a preferable solution from multiple candidate trajectories, thereby making it easier to tune the cost function and obtain a satisfactory solution. We evaluated our proposed method with motion planning tasks in 2D and 3D space. Our experiments show that the proposed algorithm is capable of determining multiple solutions for those tasks.
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33

Trappey, Charles V., and Amy J. C. Trappey. "Planning Merchandise Investments Using Fuzzy Optimization." Journal of Intelligent and Fuzzy Systems 1, no. 3 (1993): 189–97. http://dx.doi.org/10.3233/ifs-1993-1301.

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34

Legg, Meredith, Rachel A. Davidson, and Linda K. Nozick. "Optimization-Based Regional Hurricane Mitigation Planning." Journal of Infrastructure Systems 19, no. 1 (March 2013): 1–11. http://dx.doi.org/10.1061/(asce)is.1943-555x.0000106.

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35

Draxler, Roland R. "Trajectory Optimization for Balloon Flight Planning." Weather and Forecasting 11, no. 1 (March 1996): 111–14. http://dx.doi.org/10.1175/1520-0434(1996)011<0111:tofbfp>2.0.co;2.

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36

Ikkai, Yoshitomo, Takashi Sasai, Takenao Ohkawa, and Norihisa Komoda. "Dispatching Rule Exchangeable Optimization Planning System." IEEJ Transactions on Electronics, Information and Systems 114, no. 10 (1994): 1046–51. http://dx.doi.org/10.1541/ieejeiss1987.114.10_1046.

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37

Messac, Achille, Wafa M. Batayneh, and Amir Ismail-Yahaya. "Production planning optimization with physical programming." Engineering Optimization 34, no. 4 (January 2002): 323–40. http://dx.doi.org/10.1080/03052150212726.

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38

Conceição Cunha, Maria Da. "Water Systems Planning: The Optimization Perspective." Engineering Optimization 35, no. 3 (June 2003): 255–66. http://dx.doi.org/10.1080/0305215031000109613.

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39

Jabr, Rabih A. "Optimization of AC Transmission System Planning." IEEE Transactions on Power Systems 28, no. 3 (August 2013): 2779–87. http://dx.doi.org/10.1109/tpwrs.2012.2228507.

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40

Liu, Ming Long, and Nikolaos V. Sahinidis. "Optimization in Process Planning under Uncertainty." Industrial & Engineering Chemistry Research 35, no. 11 (January 1996): 4154–65. http://dx.doi.org/10.1021/ie9504516.

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41

Brahme, Anders. "103Inverse therapy planning and treatment optimization." Radiotherapy and Oncology 40 (January 1996): S28. http://dx.doi.org/10.1016/s0167-8140(96)80110-1.

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42

Tulovsky, Vladimir, Michael Ringor, and Lech Papież. "Optimization of rotational radiotherapy treatment planning." International Journal of Radiation Oncology*Biology*Physics 32, no. 4 (July 1995): 1205–14. http://dx.doi.org/10.1016/0360-3016(95)00569-k.

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43

Haugen, Joakim, and Lars Imsland. "Optimization-based motion planning for trawling." Journal of Marine Science and Technology 24, no. 3 (October 3, 2018): 984–95. http://dx.doi.org/10.1007/s00773-018-0600-0.

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44

Halvorsen-Weare, E. E., and K. Fagerholt. "Optimization in offshore supply vessel planning." Optimization and Engineering 18, no. 1 (March 31, 2016): 317–41. http://dx.doi.org/10.1007/s11081-016-9315-4.

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45

Dietz, Dennis C., Amie J. Elcan, and Daphne E. Skipper. "Optimization models for ATM network planning." Computers & Operations Research 30, no. 4 (April 2003): 625–41. http://dx.doi.org/10.1016/s0305-0548(02)00029-1.

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46

Sepúlveda, Giovanni Franco, Patricia Jaramillo Álvarez, and John Branch Bedoya. "Stochastic optimization in mine planning scheduling." Computers & Operations Research 115 (March 2020): 104823. http://dx.doi.org/10.1016/j.cor.2019.104823.

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47

Lund, Jay R. "Floodplain Planning with Risk-Based Optimization." Journal of Water Resources Planning and Management 128, no. 3 (May 2002): 202–7. http://dx.doi.org/10.1061/(asce)0733-9496(2002)128:3(202).

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48

Llopis-Albert, Carlos, Francisco Rubio, and Francisco Valero. "Optimization approaches for robot trajectory planning." Multidisciplinary Journal for Education, Social and Technological Sciences 5, no. 1 (March 29, 2018): 1. http://dx.doi.org/10.4995/muse.2018.9867.

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<p class="Textoindependiente21">The development of optimal trajectory planning algorithms for autonomous robots is a key issue in order to efficiently perform the robot tasks. This problem is hampered by the complex environment regarding the kinematics and dynamics of robots with several arms and/or degrees of freedom (dof), the design of collision-free trajectories and the physical limitations of the robots. This paper presents a review about the existing robot motion planning techniques and discusses their pros and cons regarding completeness, optimality, efficiency, accuracy, smoothness, stability, safety and scalability.</p>
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49

Jabr, R. A. "Optimization of Reactive Power Expansion Planning." Electric Power Components and Systems 39, no. 12 (August 2, 2011): 1285–301. http://dx.doi.org/10.1080/15325008.2011.567220.

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50

hejrati, Zakariya, Ebrahim Hejrati, and Atefeh Taheri.moghadam. "Optimization Generation Expansion Planning by HBMO." International Journal of Computer Applications 37, no. 7 (January 28, 2012): 25–31. http://dx.doi.org/10.5120/4620-6629.

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