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Thematische Bibliographien / Planning optimisation / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Planning optimisation“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

Zuletzt aktualisiert am 31. Juli 2025

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1

Gerth, Hans-Peter. "Optimisation of Assembly Planning." ATZproduktion worldwide eMagazine 4, no. 3 (2011): 10–15. http://dx.doi.org/10.1365/s38312-011-0019-7.

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2

Pitka, Pavle, Milan Simeunović, Milica Miličić, et al. "Planning-Level Optimisation of Headway Regularity." Applied Sciences 14, no. 13 (2024): 5706. http://dx.doi.org/10.3390/app14135706.

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Headway variability has a negative impact on the public transport passengers’ perception of service quality. However, most of the existing methods aimed at improving the headway regularity operate in real time and require precise vehicle location data, making it difficult to implement them in practice. On the other hand, planning-level methods can be used to increase the resilience of public passenger transport (PPT) to the accumulation of headway disturbances. As this is typically done from the operator’s perspective, the passengers’ perspective tends to be overlooked, motivating the current

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3

Altobelli, Emma, Maurizio Amichetti, Alessio Langiu, et al. "Combinatorial optimisation in radiotherapy treatment planning." AIMS Medical Science 5, no. 3 (2018): 204–23. http://dx.doi.org/10.3934/medsci.2018.3.204.

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4

Bushuev, Maxim. "Convex optimisation for aggregate production planning." International Journal of Production Research 52, no. 4 (2013): 1050–58. http://dx.doi.org/10.1080/00207543.2013.831998.

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5

McGowan, S. E., N. G. Burnet, and A. J. Lomax. "Treatment planning optimisation in proton therapy." British Journal of Radiology 86, no. 1021 (2013): 20120288. http://dx.doi.org/10.1259/bjr.20120288.

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6

Bland, J. A. "Space-planning by ant colony optimisation." International Journal of Computer Applications in Technology 12, no. 6 (1999): 320. http://dx.doi.org/10.1504/ijcat.1999.000215.

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7

Noonan, John, and Michael Wallace. "Improved optimisation through advanced relationship planning." Supply Chain Management: An International Journal 11, no. 6 (2006): 483–90. http://dx.doi.org/10.1108/13598540610703873.

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8

Napalkova, Liana, and Galina Merkuryeva. "MULTI-OBJECTIVE STOCHASTIC SIMULATION-BASED OPTIMISATION APPLIED TO SUPPLY CHAIN PLANNING." Technological and Economic Development of Economy 18, no. 1 (2012): 132–48. http://dx.doi.org/10.3846/20294913.2012.661190.

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The paper discusses the optimisation of complex management processes, which allows the reduction of investment costs by setting the optimal balance between product demand and supply. The systematisation of existing methods and algorithms that are used to optimise complex processes by linking stochastic discrete-event simulation and multi-objective optimisation is given. The two-phase optimisation method is developed based on hybrid combination of compromise programming, evolutionary computation and response surface-based methods. Approbation of the proposed method is performed on the multi-ech

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9

Ab Rashid, Mohd Fadzil Faisae, Windo Hutabarat, and Ashutosh Tiwari. "Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 232, no. 8 (2016): 1444–59. http://dx.doi.org/10.1177/0954405416673095.

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In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise

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10

Yu, Dexin, Xuesong Zhang, Guangdong Tian, et al. "Disassembly Sequence Planning for Green Remanufacturing Using an Improved Whale Optimisation Algorithm." Processes 10, no. 10 (2022): 1998. http://dx.doi.org/10.3390/pr10101998.

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Currently, practical optimisation models and intelligent solution algorithms for solving disassembly sequence planning are attracting more and more attention. Based on the importance of energy efficiency in product disassembly and the trend toward green remanufacturing, this paper proposes a new optimisation model for the energy-efficient disassembly sequence planning. The minimum energy consumption is used as the evaluation criterion for disassembly efficiency, so as to minimise the energy consumption during the dismantling process. As the proposed model is a complex optimization problem, cal

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11

Hueber, Christian, Gudrun Fischer, Nikolaus Schwingshandl, and Ralf Schledjewski. "Production planning optimisation for composite aerospace manufacturing." International Journal of Production Research 57, no. 18 (2018): 5857–73. http://dx.doi.org/10.1080/00207543.2018.1554918.

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12

Giorgi, Giovanni, Leopoldo Avalle, Massimo Brignone, Michele Piana, and Giacomo Caviglia. "An optimisation approach to multiprobe cryosurgery planning." Computer Methods in Biomechanics and Biomedical Engineering 16, no. 8 (2013): 885–95. http://dx.doi.org/10.1080/10255842.2011.643469.

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13

Liu, Guoliang, Shijie Han, Xiuhai Zhao, John D. Nelson, Hongshu Wang, and Weiying Wang. "Optimisation algorithms for spatially constrained forest planning." Ecological Modelling 194, no. 4 (2006): 421–28. http://dx.doi.org/10.1016/j.ecolmodel.2005.10.028.

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14

Kok, H. P., A. N. T. J. Kotte, and J. Crezee. "Planning, optimisation and evaluation of hyperthermia treatments." International Journal of Hyperthermia 33, no. 6 (2017): 593–607. http://dx.doi.org/10.1080/02656736.2017.1295323.

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15

Andersson, Sven-Eric. "Forecasting and optimisation for airline operational planning." International Journal of Services Technology and Management 2, no. 1/2 (2001): 161. http://dx.doi.org/10.1504/ijstm.2001.001597.

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16

Dias, Fernando Maurício, Bruno Canizes, Hussein Khodr, and Manuel Cordeiro. "Distribution networks planning using decomposition optimisation technique." IET Generation, Transmission & Distribution 9, no. 12 (2015): 1409–20. http://dx.doi.org/10.1049/iet-gtd.2014.0860.

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17

Chen, Junchi. "Algorithmic implementation and optimisation of path planning." Applied and Computational Engineering 33, no. 1 (2024): 176–84. http://dx.doi.org/10.54254/2755-2721/33/20230263.

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Path planning algorithms are widely used in the fields of robotics and robotic arms, unmanned devices, automatic navigation, etc., and are an important technical basis for promoting the development of automation as well as the popularisation of artificial intelligence technology. This paper will briefly introduce various path planning algorithms implemented by mathematical models or inspired by biological features or genetics from the aspects of geometric search algorithms, intelligent search algorithms, artificial intelligence algorithms, and hybrid algorithms, including the characteristics,

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18

McIntyre, Melissa, Puthenparampil Wilson, Peter Gorayski, and Eva Bezak. "A Systematic Review of LET-Guided Treatment Plan Optimisation in Proton Therapy: Identifying the Current State and Future Needs." Cancers 15, no. 17 (2023): 4268. http://dx.doi.org/10.3390/cancers15174268.

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The well-known clinical benefits of proton therapy are achieved through higher target-conformality and normal tissue sparing than conventional radiotherapy. However, there is an increased sensitivity to uncertainties in patient motion/setup, proton range and radiobiological effect. Although recent efforts have mitigated some uncertainties, radiobiological effect remains unresolved due to a lack of clinical data for relevant endpoints. Therefore, RBE optimisations may be currently unsuitable for clinical treatment planning. LET optimisation is a novel method that substitutes RBE with LET, shift

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19

Guo, Y. W., W. D. Li, A. R. Mileham, and G. W. Owen. "Optimisation of integrated process planning and scheduling using a particle swarm optimisation approach." International Journal of Production Research 47, no. 14 (2009): 3775–96. http://dx.doi.org/10.1080/00207540701827905.

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20

Hameed, R. U., A. Maqsood, A. J. Hashmi, M. T. Saeed, and R. Riaz. "Reinforcement learning-based radar-evasive path planning: a comparative analysis." Aeronautical Journal 126, no. 1297 (2021): 547–64. http://dx.doi.org/10.1017/aer.2021.85.

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AbstractThis paper discusses the utilisation of deep reinforcement learning algorithms to obtain optimal paths for an aircraft to avoid or minimise radar detection and tracking. A modular approach is adopted to formulate the problem, including the aircraft kinematics model, aircraft radar cross-section model and radar tracking model. A virtual environment is designed for single and multiple radar cases to obtain optimal paths. The optimal trajectories are generated through deep reinforcement learning in this study. Specifically, three algorithms, namely deep deterministic policy gradient, trus

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21

Wang, Yongji, David M. Lane, and Gavin J. Falconer. "Two novel approaches for unmanned underwater vehicle path planning: constrained optimisation and semi-infinite constrained optimisation." Robotica 18, no. 2 (2000): 123–42. http://dx.doi.org/10.1017/s0263574799002015.

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In this paper, two novel approaches to unmanned underwater vehicle path planning are presented. The main idea of the first approach, referred to as Constrained Optimisation (CO) is to represent the free space of the workspace as a set of inequality constraints using vehicle configuration variables. The second approach converts robot path planning into a Semi-infinite Constrained Optimisation (SCO) problem. The function interpolation technique is adopted to satisfy the start and goal configuration requirements. Mathematical foundations for Constructive Solid Geometry (CSG), Boolean operations a

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22

Al Shidhani, Taimur, Anastasia Ioannou, and Gioia Falcone. "Multi-Objective Optimisation for Power System Planning Integrating Sustainability Indicators." Energies 13, no. 9 (2020): 2199. http://dx.doi.org/10.3390/en13092199.

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The increase in global electricity demand, along with its impact on climate change, call for integrating sustainability aspects in the power system expansion planning. Sustainable power generation planning needs to fulfill different, often contradictory, objectives. This paper proposes a multi-objective optimisation model integrating four objective functions, including minimisation of total discounted costs, carbon emissions, land use, and social opposition. Other factors addressed in the model include renewable energy share, jobs created, mortality rates, and energy diversity, among others. S

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23

Merkuryeva, Galina, Vitaly Bolshakov, and Maksims Kornevs. "An Integrated Approach to Product Delivery Planning and Scheduling." Scientific Journal of Riga Technical University. Computer Sciences 45, no. 1 (2011): 97–103. http://dx.doi.org/10.2478/v10143-011-0049-7.

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An Integrated Approach to Product Delivery Planning and SchedulingProduct delivery planning and scheduling is a task of high priority in transport logistics. In distribution centres this task is related to deliveries of various types of goods in predefined time windows. In real-life applications the problem has different stochastic performance criteria and conditions. Optimisation of schedules itself is time consuming and requires an expert knowledge. In this paper an integrated approach to product delivery planning and scheduling is proposed. It is based on a cluster analysis of demand data o

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24

Wichapong, Kritsada, Nantiwat Pholdee, Sujin Bureerat, and Thana Radpukdee. "Trajectory Planning of a 6D Robot based on Meta Heuristic Algorithms." MATEC Web of Conferences 220 (2018): 06004. http://dx.doi.org/10.1051/matecconf/201822006004.

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In this work, several established meta-heuristics (MHs) were employed for solving 6-DOF robot trajectory planning. A fourth order polynomial function is used to represent a motion path of the robot from initial to final points while an optimisation problem is posed to minimise travelling time subject to velocity, acceleration and jerk constraints. The design variables are joint velocities and accelerations at intermediate positions, and moving time from the initial position to the intermediate position and from the intermediate position to the final position. Several MHs are used to solve the

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25

Baron, Silja, Jannis Hoek, Inka Kaufmann Alves, and Sabine Herz. "Comprehensive scenario management of sustainable spatial planning and urban water services." Water Science and Technology 73, no. 5 (2015): 1041–51. http://dx.doi.org/10.2166/wst.2015.578.

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Adaptations of existing central water supply and wastewater disposal systems to demographic, climatic and socioeconomic changes require a profound knowledge about changing influencing factors. The paper presents a scenario management approach for the identification of future developments of drivers influencing water infrastructures. This method is designed within a research project with the objective of developing an innovative software-based optimisation and decision support system for long-term transformations of existing infrastructures of water supply, wastewater and energy in rural areas.

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26

Ooi, Gim Chee, and Iskandar Shahrim Bin Mustafa. "Utilising virtual bolus in superficial planning target volume dose optimisation (TomoTherapy): a phantom study." Journal of Radiotherapy in Practice 19, no. 1 (2019): 65–70. http://dx.doi.org/10.1017/s1460396919000438.

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AbstractAim:This is a phantom study to evaluate the dosimetry effects of using virtual bolus (VB) in TomoTherapy Treatment Planning System (TPS) optimisation for superficial planning target volume (PTV) that extends to the body surface. Without VB, the inverse-planning TPS will continuously boost the photon fluence at the surface of the superficial PTV due to lack of build-up region. VB is used during TPS optimisation only and will not be present in actual treatment delivery.Materials and methods:In this study, a dummy planning target was contoured on a cylindrical phantom which extends to the

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27

Küttner, R., and J. Majak. "Multistage Optimisation Strategy For Solving Production Planning Problems." IFAC Proceedings Volumes 42, no. 2 (2009): 332–37. http://dx.doi.org/10.3182/20090506-3-sf-4003.00061.

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28

Kayacan, M. Cengiz, and Celik, S. A. . "Optimisation of Process Planning System for Prismatic Parts." Journal of Applied Sciences 1, no. 3 (2001): 309–16. http://dx.doi.org/10.3923/jas.2001.309.316.

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29

Humphreys, Peter, Belinda Spratt, Mersedeh Tariverdi, et al. "An Overview of Hospital Capacity Planning and Optimisation." Healthcare 10, no. 5 (2022): 826. http://dx.doi.org/10.3390/healthcare10050826.

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Health care is uncertain, dynamic, and fast growing. With digital technologies set to revolutionise the industry, hospital capacity optimisation and planning have never been more relevant. The purposes of this article are threefold. The first is to identify the current state of the art, to summarise/analyse the key achievements, and to identify gaps in the body of research. The second is to synthesise and evaluate that literature to create a holistic framework for understanding hospital capacity planning and optimisation, in terms of physical elements, process, and governance. Third, avenues f

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30

Sales, Leonardo, Johannes Jäschke, and Milan Stanko. "Early field planning using optimisation and considering uncertainties." Journal of Petroleum Science and Engineering 207 (December 2021): 109058. http://dx.doi.org/10.1016/j.petrol.2021.109058.

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31

Chakrabarty, B. K. "Conventional Planning Practice and Optimisation in Residential Layout." Third World Planning Review 9, no. 2 (1987): 117. http://dx.doi.org/10.3828/twpr.9.2.856322635644w17u.

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32

Iannone, Raffaele, Angela Ingenito, Giada Martino, Salvatore Miranda, Claudia Pepe, and Stefano Riemma. "Merchandise and Replenishment Planning Optimisation for Fashion Retail." International Journal of Engineering Business Management 5 (January 1, 2013): 26. http://dx.doi.org/10.5772/56836.

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The integration among different companies functions, collaborative planning and the elaboration of focused distribution plans are critical to the success of each kind of company working in the complex retail sector. In this contest, the present work proposes the description of a model able to support coordinated strategic choices continually made by Supply Chain (SC) actors. The final objective is achievement of the full optimisation of Merchandise & Replenishment Planning phases, identifying the right replenishment quantities and periods. To test the proposed model's effectiveness, it was

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33

Kemp, Samuel E., Paul A. Roach, Andrew J. Ware, and Ian D. Wilson. "Artificial Intelligence for Automatic Container Stowage Planning Optimisation." Ship Technology Research 50, no. 4 (2003): 151–56. http://dx.doi.org/10.1179/str.2003.50.4.002.

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34

Dziurzanski, Piotr, Shuai Zhao, Sebastian Scholze, Albert Zilverberg, Karl Krone, and Leandro Soares Indrusiak. "Process planning and scheduling optimisation with alternative recipes." at - Automatisierungstechnik 68, no. 2 (2020): 140–47. http://dx.doi.org/10.1515/auto-2019-0104.

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AbstractThis paper considers an application of a new variant of a multi-objective flexible job-shop scheduling problem, featuring multisubset selection of manufactured recipes, to a real-world chemical plant. The problem is optimised using a multi-objective genetic algorithm with customised mutation and elitism operators that minimises both the total production time and the produced commodity surplus. The algorithm evaluation is performed with both random and historic manufacturing orders. The latter demonstrated that the proposed system can lead to more than 10 % makespan improvements in comp

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35

Xu, Xiandong, Yi Guan, Bingrong Hong, Wende Ke, Qiubo Zhong, and Songhao Piao. "Multi-objective optimisation for humanoid robot motion planning." International Journal of Wireless and Mobile Computing 10, no. 2 (2016): 112. http://dx.doi.org/10.1504/ijwmc.2016.076178.

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36

Ansorena, Iñigo L. "Work planning optimisation in ports: a simplex application." International Journal of Mathematics in Operational Research 14, no. 1 (2019): 146. http://dx.doi.org/10.1504/ijmor.2019.096985.

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37

Ansorena, Iñigo L. "Work planning optimisation in ports: a simplex application." International Journal of Mathematics in Operational Research 14, no. 1 (2019): 146. http://dx.doi.org/10.1504/ijmor.2019.10017881.

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38

Goerigk, Marc, and Horst W. Hamacher. "Optimisation models to enhance resilience in evacuation planning." Civil Engineering and Environmental Systems 32, no. 1-2 (2015): 90–99. http://dx.doi.org/10.1080/10286608.2015.1022729.

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39

Tseng, Fan-Hsun, Chi-Yuan Chen, and Han-Chieh Chao. "Multi-objective optimisation for heterogeneous cellular network planning." IET Communications 13, no. 3 (2019): 322–30. http://dx.doi.org/10.1049/iet-com.2018.5332.

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40

Dereli, Türkay, and İ. Hüseyin Filiz. "Optimisation of process planning functions by genetic algorithms." Computers & Industrial Engineering 36, no. 2 (1999): 281–308. http://dx.doi.org/10.1016/s0360-8352(99)00133-3.

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41

Marian, Romeo M., Lee H. S. Luong, and Kazem Abhary. "Assembly sequence planning and optimisation using genetic algorithms." Applied Soft Computing 2, no. 3 (2003): 223–53. http://dx.doi.org/10.1016/s1568-4946(02)00064-9.

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42

Karageorgos, Anthony, Nikolay Mehandjiev, Alexander Hämmerle, and Georg Weichhart. "Agent-Based Optimisation of Logistics and Production Planning." IFAC Proceedings Volumes 36, no. 3 (2003): 121–26. http://dx.doi.org/10.1016/s1474-6670(17)37745-5.

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43

Riedel, T., M. Takahashi, and E. Itoh. "Optimisation of interval management – speed planning using SMPSO." Aeronautical Journal 124, no. 1281 (2020): 1819–45. http://dx.doi.org/10.1017/aer.2020.77.

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ABSTRACTRecent research on Flight-deck Interval Management (FIM), a modern technology for increasing safety and improving airspace and runway utilisation through self-spacing, has led to the development of a new rule-based logic for FIM, namely Interval Management – Speed Planning (IM-SP). In an initial benchmark study, IM-SP showed good spacing performance with a significant reduction in speed commands, a major area of concern with previous FIM logics, resulting in a lower burden on the flight crew during FIM operation. Nevertheless, there remains scope for improvement in other aspects, such

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44

Suarez, Nicolas, Matthias Poppe, and Nigel Makins. "TORCH: from strategic planning to real-time optimisation." Air & Space Europe 3, no. 3-4 (2001): 258–61. http://dx.doi.org/10.1016/s1290-0958(01)90112-1.

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45

Karageorgos, Anthony, Nikolay Mehandjiev, Georg Weichhart, and Alexander Hämmerle. "Agent-based optimisation of logistics and production planning." Engineering Applications of Artificial Intelligence 16, no. 4 (2003): 335–48. http://dx.doi.org/10.1016/s0952-1976(03)00076-9.

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46

Jennings, Mark G., and Nilay Shah. "Workforce planning and technology installation optimisation for utilities." Computers & Industrial Engineering 67 (January 2014): 72–81. http://dx.doi.org/10.1016/j.cie.2013.10.008.

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47

Nemet, Andreja, Jiří Jaromír Klemeš, Neven Duić, and Jinyue Yan. "Improving sustainability development in energy planning and optimisation." Applied Energy 184 (December 2016): 1241–45. http://dx.doi.org/10.1016/j.apenergy.2016.10.111.

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48

Chicherin, Stanislav, Anna Volkova, and Eduard Latõšov. "GIS-based optimisation for district heating network planning." Energy Procedia 149 (September 2018): 635–41. http://dx.doi.org/10.1016/j.egypro.2018.08.228.

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49

Ünal, Can, and Alime Dere Yüksel. "Cut Order Planning Optimisation in the Apparel Industry." Fibres and Textiles in Eastern Europe 28, no. 1(139) (2020): 8–13. http://dx.doi.org/10.5604/01.3001.0013.5851.

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Nowadays, apparel businesses have to cut down on their costs in order to ensure the continuity of their activities and to expand them. Fabric costs cover about 50-60% of production costs. In this study, for the minimisation of fabric costs, the cutting department in enterprises was investigated and the cut order plans developed by mixed integer nonlinear programming. Examples were taken from the enterprises that were implemented. A mathematical model was then developed to be used in mixed integer nonlinear programming for a manual cut order plan, and a program code was created in LINGO optimis

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50

He, Y. Q. "Advances in global optimisation for generation expansion planning." IEE Proceedings - Generation, Transmission and Distribution 142, no. 4 (1995): 423. http://dx.doi.org/10.1049/ip-gtd:19951978.

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