Zeitschriftenartikel: „Planning optimisation“

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

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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3

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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4

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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5

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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6

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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7

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-echelon supply chain planning problem that is widely distributed in industry and its solution plays a vital role in increasing the competitiveness of a company. Three scenarios are implemented to optimise supply chain tactical planning processes at the chemical manufacturing company based on using different optimisation methods and software. The numerical results prove the competitive advantages of the developed two-phase optimisation method.

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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, called NP-hard, this study develops a new extension of the whale optimisation algorithm to allow it to solve discrete problems. The whale optimisation algorithm is a recently developed and successful meta-heuristic algorithm inspired by the behaviour of whales rounding up their prey. We have improved the whale optimisation algorithm for predation behaviour and added a local search strategy to improve its performance. The proposed algorithm is validated with a worm reducer example and compared with other state-of-the-art and recent metaheuristics. Finally, the results confirm the high solution quality and efficiency of the proposed improved whale algorithm.

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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 an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set.

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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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11

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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12

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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13

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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14

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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15

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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16

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, advantages and disadvantages, and important improvements of typical path planning algorithms as well as hybrid algorithms which are made by imitating and improving each other of several algorithms. Meanwhile, the development trend of path planning algorithms is also summarised, and the outlook of its development is made to provide reference for related fields.

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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, shifting LET hotspots outside critical structures. This review outlines the current status of LET optimisation in proton therapy, highlighting knowledge gaps and possible future research. Following the PRISMA 2020 guidelines, a search of the MEDLINE® and Scopus databases was performed in July 2023, identifying 70 relevant articles. Generally, LET optimisation methods achieved their treatment objectives; however, clinical benefit is patient-dependent. Inconsistencies in the reported data suggest further testing is required to identify therapeutically favourable methods. We discuss the methods which are suitable for near-future clinical deployment, with fast computation times and compatibility with existing treatment protocols. Although there is some clinical evidence of a correlation between high LET and adverse effects, further developments are needed to inform future patient selection protocols for widespread application of LET optimisation in proton therapy.

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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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19

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. Single-objective linear optimisations are initially performed to investigate the impact of each objective function on the resulting power generation mix. Minimising land use and discounted total costs favoured fossil fuels technologies, as opposed to minimising carbon emissions, which resulted in increased renewable energy shares. Minimising social opposition also favoured renewable energy shares, except for hydropower and onshore wind technologies. Accordingly, to investigate the trade-offs among the objective functions, Pareto front candidates for each pair of objective functions were generated, indicating a strong correlation between the minimisation of carbon emissions and the social opposition. Limited trade-offs were also observed between the minimisation of costs and land use. Integrating the objective functions in the multi-objective model resulted in various non-dominated solutions. This tool aims to enable decision-makers identify the trade-offs when optimising the power system under different objectives and determine the most suitable electricity generation mix.

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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, trust region policy optimisation and proximal policy optimisation, are used to find optimal paths for five test cases. The comparison is carried out based on six performance indicators. The investigation proves the importance of these reinforcement learning algorithms in optimal path planning. The results indicate that the proximal policy optimisation approach performed better for optimal paths in general.

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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 and approximation techniques are also presented to reduce the number of constraints, and to avoid local minima. The advantages of these approaches are that the mature techniques developed in optimisation theory which guarantee convergence, efficiency and numerical robustness can be directly applied to the robot path planning problem. Simulation results have been presented.

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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 of stores to identify typical dynamic demand patterns and product delivery tactical plans, and simulation optimisation to find optimal parameters of transportation or vehicle schedules. Here, a cluster analysis of the demand data by using the K-means clustering algorithm and silhouette plots mean values is performed, and an NBTree-based classification model is built. In order to find an optimal grouping of stores into regions based on their geographical locations and the total demand uniformly distributed over regions, a multiobjective optimisation problem is formulated and solved with the NSGA II algorithm.

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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 trajectory optimisation problem of robot manipulators while their performances are investigated. Based on this study, the best MH for robot trajectory planning is found while the results obtained from such a method are set as the baseline for further study of robot trajectory planning optimisation.

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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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25

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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26

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 for future research are sought to inform researchers and practitioners where they should best concentrate their efforts. In conclusion, we find that prior research has typically focussed on individual parts, but the hospital is one body that is made up of many interdependent parts. It is also evident that past attempts considering entire hospitals fail to incorporate all the detail that is necessary to provide solutions that can be implemented in the real world, across strategic, tactical and operational planning horizons. A holistic approach is needed that includes ancillary services, equipment medicines, utilities, instrument trays, supply chain and inventory considerations.

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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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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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29

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 applied to an important Italian fashion company in the complex field of fast-fashion, a sector in which promptness is a main competitive leverage and, therefore, the planning cannot exclude the time variable. The passage from a total push strategy, currently used by the company, to a push-pull one, suggested by the model, allowed us not only to estimate a reduction in goods quantities to purchase at the beginning of a sales period (with considerable economic savings), but also elaborate a focused replenishment plan that permits reduction and optimisation of departures from network warehouses to Points of Sale (POS).

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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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31

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 comparison with human operators.

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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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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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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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35

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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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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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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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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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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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 as fuel burn. In this study, the internal cost function of IM-SP is further analysed and optimised using speed-constrained multi-objective particle swarm optimisation to improve the performance of IM-SP under the multiple objectives of FIM. The optimisation renders new settings that address the problem areas, improve the speed commands and enhance the overall quality of IM-SP. Two distinctive solutions, viz. a spacing performance optimised setting and a fuel burn optimised setting, are further analysed and discussed, and directions for follow-up research are explored.

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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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42

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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43

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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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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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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46

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 optimisation software. With the program code developed, optimum results that cannot be manually calculated by an operator is obtained. The amounts of fabric to be used as a result of the cut order plan were applied to the samples taken from the enterprises, and the manual solution and model developed were compared. As a result of the comparisons made, thanks to the model, fabric developed, usage is reduced and fabric cost minimisation ensured.

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47

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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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. Drivers of water infrastructures comprise engineering and spatial factors and these are predicted by different methods and techniques. The calculated developments of the drivers are illustrated for a model municipality. The developed scenario-manager enables the generation of comprehensive scenarios by combining different drivers. The scenarios are integrated into the optimisation model as input parameters. Furthermore, the result of the optimisation process – an optimal transformation strategy for water infrastructures – can have impacts on the existing fee system. General adaptation possibilities of the present fee system are presented.

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Kuik, Swee, and Li Diong. "A model-driven decision approach to collaborative planning and obsolescence for manufacturing operations." Industrial Management & Data Systems 119, no. 9 (2019): 1926–46. http://dx.doi.org/10.1108/imds-05-2019-0264.

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Purpose The purpose of this paper is to present the model-driven decision support system (DSS) for small and medium manufacturing enterprises (SMMEs) that actively participates in collaborative activities and manages the planned obsolescence in production. In dealing with the complexity of such demand and supply scenario, the optimisation models are also developed to evaluate the performance of operations practices. Design/methodology/approach The model-driven DSS for SMMEs, which uses the optimisation models for managing and coordinating planned obsolescence, is developed to determine the optimal manufacturing plan and minimise operating costs. A case application with the planned obsolescence and production scenario is also provided to demonstrate the approach and practical insights of DSS. Findings Assessing planned obsolescence in production is a challenge for manufacturing managers. A DSS for SMMEs can enable the computerised support in decision making and understand the planned obsolescence scenarios. The causal relationship of different time-varying component obsolescence and availability in production are also examined, which may have an impact on the overall operating costs for producing manufactured products. Research limitations/implications DSS can resolve and handle the complexity of production and planned obsolescence scenarios in manufacturing industry. The optimisation models used in the DSS excludes the variability in component wear-out life and technology cycle. In the future study, the optimisation models in DSS will be extended by taking into the uncertainty of different component wear-out life and technology cycle considerations. Originality/value This paper demonstrates the flexibility of DSS that facilitates the optimisation models for collaborative manufacturing in planned obsolescence and achieves cost effectiveness.

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Ab. Rashid, M. F. F., N. M. Z. Nik Mohamed, and A. N. Mohd Rose. "A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing." Journal of Mechanical Engineering and Sciences 13, no. 4 (2019): 5905–21. http://dx.doi.org/10.15282/jmes.13.4.2019.13.0469.

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Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) are traditionally optimised independently. However recently, integrated ASP and ALB optimisation has become more relevant to obtain better quality solution and to reduce time to market. Despite many optimisation algorithms that were proposed to optimise this problem, the existing researches on this problem were limited to Evolutionary Algorithm (EA), Ant Colony Optimisation (ACO), and Particle Swarm Optimisation (PSO). This paper proposed a modified Artificial Bee Colony algorithm (MABC) to optimise the integrated ASP and ALB problem. The proposed algorithm adopts beewolves predatory concept from Grey Wolf Optimiser to improve the exploitation ability in Artificial Bee Colony (ABC) algorithm. The proposed MABC was tested with a set of benchmark problems. The results indicated that the MABC outperformed the comparison algorithms in 91% of the benchmark problems. Furthermore, a statistical test reported that the MABC had significant performances in 80% of the cases.

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