Journal articles on the topic 'Cost model optimisation'
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1
Sahay, S. S., and K. Mitra. "Cost Model Based Optimisation Of Carburising Operation." Surface Engineering 20, no. 5 (October 2004): 379–84. http://dx.doi.org/10.1179/026708404x1143.
2
Yeo, S. H., B. K. A. Ngoi, and H. Chen. "A cost-tolerance model for process sequence optimisation." International Journal of Advanced Manufacturing Technology 12, no. 6 (November 1996): 423–31. http://dx.doi.org/10.1007/bf01186931.
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Khalil, Jean, Sameh M. Saad, and Nabil Gindy. "An integrated cost optimisation maintenance model for industrial equipment." Journal of Quality in Maintenance Engineering 15, no. 1 (March 27, 2009): 106–18. http://dx.doi.org/10.1108/13552510910943912.
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Pati, Rupesh Kumar, Prem Vrat, and Pradeep Kumar. "Cost optimisation model in recycled waste reverse logistics system." International Journal of Business Performance Management 6, no. 3/4 (2004): 245. http://dx.doi.org/10.1504/ijbpm.2004.005631.
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Glavan, Miha, Dejan Gradišar, Serena Invitto, Iztok Humar, Ðani Juričić, Cesare Pianese, and Damir Vrančić. "Cost optimisation of supermarket refrigeration system with hybrid model." Applied Thermal Engineering 103 (June 2016): 56–66. http://dx.doi.org/10.1016/j.applthermaleng.2016.03.177.
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Delelegn, S. W., A. Pathirana, B. Gersonius, A. G. Adeogun, and K. Vairavamoorthy. "Multi-objective optimisation of cost–benefit of urban flood management using a 1D2D coupled model." Water Science and Technology 63, no. 5 (March 1, 2011): 1053–59. http://dx.doi.org/10.2166/wst.2011.290.
Abstract:
This paper presents a multi-objective optimisation (MOO) tool for urban drainage management that is based on a 1D2D coupled model of SWMM5 (1D sub-surface flow model) and BreZo (2D surface flow model). This coupled model is linked with NSGA-II, which is an Evolutionary Algorithm-based optimiser. Previously the combination of a surface/sub-surface flow model and evolutionary optimisation has been considered to be infeasible due to the computational demands. The 1D2D coupled model used here shows a computational efficiency that is acceptable for optimisation. This technological advance is the result of the application of a triangular irregular discretisation process and an explicit finite volume solver in the 2D surface flow model. Besides that, OpenMP based parallelisation was employed at optimiser level to further improve the computational speed of the MOO tool. The MOO tool has been applied to an existing sewer network in West Garforth, UK. This application demonstrates the advantages of using multi-objective optimisation by providing an easy-to-comprehend Pareto-optimal front (relating investment cost to expected flood damage) that could be used for decision making processes, without repeatedly going through the modelling–optimisation stage.
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March, A., K. Willcox, and Q. Wang. "Gradient-based multifidelity optimisation for aircraft design using Bayesian model calibration." Aeronautical Journal 115, no. 1174 (December 2011): 729–38. http://dx.doi.org/10.1017/s0001924000006473.
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Abstract Optimisation of complex systems frequently requires evaluating a computationally expensive high-fidelity function to estimate a system metric of interest. Although design sensitivities may be available through either direct or adjoint methods, the use of formal optimisation methods may remain too costly. Incorporating low-fidelity performance estimates can substantially reduce the cost of the high-fidelity optimisation. In this paper we present a provably convergent multifidelity optimisation method that uses Cokriging Bayesian model calibration and first-order consistent trust regions. The technique is compared with a single-fidelity sequential quadratic programming method and a conventional first-order trust-region method on both a two-dimensional structural optimisation and an aerofoil design problem. In both problems adjoint formulations are used to provide inexpensive sensitivity information.
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Rybakov, Dmitriy S. "Total cost optimisation model for logistics systems of trading companies." International Journal of Logistics Systems and Management 27, no. 3 (2017): 318. http://dx.doi.org/10.1504/ijlsm.2017.084469.
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Rybakov, Dmitriy S. "Total cost optimisation model for logistics systems of trading companies." International Journal of Logistics Systems and Management 27, no. 3 (2017): 318. http://dx.doi.org/10.1504/ijlsm.2017.10005118.
10
Fafandjel, Nikša, Albert Zamarin, and Marko Hadjina. "Shipyard production cost structure optimisation model related to product type." International Journal of Production Research 48, no. 5 (January 28, 2009): 1479–91. http://dx.doi.org/10.1080/00207540802609665.
11
Huang, Qinxia, Cheng Zhang, Jing Liu, and Shilin Wu. "Optimisation of Cost 231-Hata model based on deep learning." International Journal of Innovative Computing and Applications 13, no. 5/6 (2022): 259. http://dx.doi.org/10.1504/ijica.2022.128433.
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Altin, Cemil. "Differential Evolution Algorithm Based Very Fast Renewable Energy System Optimisation Tool Design." Elektronika ir Elektrotechnika 29, no. 4 (September 7, 2023): 44–53. http://dx.doi.org/10.5755/j02.eie.33872.
Abstract:
In this study, an optimisation tool that uses the differential evolution algorithm with a special distribution strategy is designed for the first time to be used in the optimisation of hybrid renewable energy systems. The developed tool and the hybrid optimisation model for multiple energy resources (HOMER) optimisation programme were compared. The tool is much faster than the HOMER programme and can produce almost the same results as HOMER. In addition, a heuristic-based optimisation technique was used for the first time to generate extremely comprehensive findings. The capacity shortage parameter, which is not used much in the literature, is used as a reliability parameter. The cost of energy (COE) was used as the cost function. The results are promising for detailed optimisation studies in this area.
13
Gorman, Richard M., and Hilary J. Oliver. "Automated model optimisation using the Cylc workflow engine (Cyclops v1.0)." Geoscientific Model Development 11, no. 6 (June 12, 2018): 2153–73. http://dx.doi.org/10.5194/gmd-11-2153-2018.
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Abstract. Most geophysical models include many parameters that are not fully determined by theory, and can be “tuned” to improve the model's agreement with available data. We might attempt to automate this tuning process in an objective way by employing an optimisation algorithm to find the set of parameters that minimises a cost function derived from comparing model outputs with measurements. A number of algorithms are available for solving optimisation problems, in various programming languages, but interfacing such software to a complex geophysical model simulation presents certain challenges. To tackle this problem, we have developed an optimisation suite (“Cyclops”) based on the Cylc workflow engine that implements a wide selection of optimisation algorithms from the NLopt Python toolbox (Johnson, 2014). The Cyclops optimisation suite can be used to calibrate any modelling system that has itself been implemented as a (separate) Cylc model suite, provided it includes computation and output of the desired scalar cost function. A growing number of institutions are using Cylc to orchestrate complex distributed suites of interdependent cycling tasks within their operational forecast systems, and in such cases application of the optimisation suite is particularly straightforward. As a test case, we applied the Cyclops to calibrate a global implementation of the WAVEWATCH III (v4.18) third-generation spectral wave model, forced by ERA-Interim input fields. This was calibrated over a 1-year period (1997), before applying the calibrated model to a full (1979–2016) wave hindcast. The chosen error metric was the spatial average of the root mean square error of hindcast significant wave height compared with collocated altimeter records. We describe the results of a calibration in which up to 19 parameters were optimised.
14
Tee, Kong Fah, and Ejiroghene Ekpiwhre. "Strategic cost modelling and optimisation for highway asset maintenance." Journal of Quality in Maintenance Engineering 26, no. 2 (October 14, 2019): 198–212. http://dx.doi.org/10.1108/jqme-12-2016-0084.
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Purpose The purpose of this paper is to propose and confer a strategic cost model as a concept for good decision making for highway asset treatment in the transport industry. Design/methodology/approach The paper is based on asset performance condition and treatment renewals using a five-point performance for developing prospective treatment strategies. The strategic cost model is presented in the similitude of picturesque and its outcomes via an exploratory data analysis. Findings The results articulate the best maintenance plan for the forthcoming and future years. The strategic cost model uses the combination of the current condition band, the sample area and likely treatment cost for proposing the optimal treatment solution based on consideration of desired treatment level. Practical implications The strategic cost model is suitable for outlining the asset performance condition, treatment renewals and analytical cost optimisation. The formulated analytical cost model and developed prospective strategies enable good decision making, long-term contract negotiations and whole life cost maintenance and management. Originality/value Embracing eminent performance condition from the research area of lifecycle planning and deterioration models of a physical asset, a prospective cost strategy for asset maintenance is proposed in the study. The resultant treatment strategies using the analytical approach portray the ability that enables the adaptation of expected outcomes.
15
Mahmood, Farrukh, and Haider Ali. "Energy-Cost Optimisation in Water-Supply System." Pakistan Development Review 52, no. 4I (December 1, 2013): 437–46. http://dx.doi.org/10.30541/v52i4ipp.437-446.
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Water, being the basic requirement of life, is important to all living organism, human health and food production. A positive correlation between economic growth and rate of water utilisation has also been observed in a growth model with water as a productive input for private producers [Barbier (2004)]. In addition, high per-capita consumption (PCC) of water is regarded as an indicator of the level of economic development where per-capita water consumption is defined as the average of water consumed by a person in a day. The declining availability of water supply, mainly due to global climate change, is one of the important issues faced by many developing countries at the present time. It is estimated that nearly two third of nations across the globe will experience water stress by 2025.1 Thus, the safety and availability of clean water is an on-going concern within the global village
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Li, Xiaodong, Xiang Song, and Djamila Ouelhadj. "A Cost Optimisation Model for Maintenance Planning in Offshore Wind Farms with Wind Speed Dependent Failure Rates." Mathematics 11, no. 13 (June 22, 2023): 2809. http://dx.doi.org/10.3390/math11132809.
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This paper presents an optimisation model for cost optimisation of maintenance at an offshore wind farm (OWF). The model is created for OWF project developers to optimise strategic resources to meet their maintenance demand. The model takes into account various maintenance categories on a full range of wind turbine components; the failure rate associated with each component is dependent on wind speed in order to consider weather uncertainty. Weibull distribution is used to predict the probability of wind speed occurring during a given period based on available historical data. The performance of the proposed optimisation model has been validated using reference cases and a UK OWF in operation. Various optimal solutions are investigated for the problems with increased and decreased mean turbine failure rates as a sensitivity test of the model.
17
Yamanaka, O., T. Obara, and K. Yamamoto. "Total cost minimization control scheme for biological wastewater treatment process and its evaluation based on the COST benchmark process." Water Science and Technology 53, no. 4-5 (February 1, 2006): 203–14. http://dx.doi.org/10.2166/wst.2006.125.
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This paper proposes a new cost minimisation control scheme for a predenitrification type of biological wastewater treatment process and evaluates the validity of the control scheme based on the benchmark process presented by Copp. The control scheme adopting a hierarchical control structure incorporates lower-level controllers that consist of a set of local dynamic controllers and a higher-level static optimiser that provides the set points of the lower-level controllers based on the total cost index of Vanrolleghem and Gillot. Prior to benchmarking, this paper derives a simplified process model used for the optimiser, which is able to approximate the benchmark process model effectively as well as is simplified sufficiently for faster set point optimisation for on-line purposes. Numerical experiments evaluate the effectiveness of the proposed control scheme from various viewpoints including process operational and optimisation viewpoints.
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Ławryńczuk, Maciej, and Robert Nebeluk. "Computationally Efficient Nonlinear Model Predictive Control Using the L1 Cost-Function." Sensors 21, no. 17 (August 30, 2021): 5835. http://dx.doi.org/10.3390/s21175835.
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Model Predictive Control (MPC) algorithms typically use the classical L2 cost function, which minimises squared differences of predicted control errors. Such an approach has good numerical properties, but the L1 norm that measures absolute values of the control errors gives better control quality. If a nonlinear model is used for prediction, the L1 norm leads to a difficult, nonlinear, possibly non-differentiable cost function. A computationally efficient alternative is discussed in this work. The solution used consists of two concepts: (a) a neural approximator is used in place of the non-differentiable absolute value function; (b) an advanced trajectory linearisation is performed on-line. As a result, an easy-to-solve quadratic optimisation task is obtained in place of the nonlinear one. Advantages of the presented solution are discussed for a simulated neutralisation benchmark. It is shown that the obtained trajectories are very similar, practically the same, as those possible in the reference scheme with nonlinear optimisation. Furthermore, the L1 norm even gives better performance than the classical L2 one in terms of the classical control performance indicator that measures squared control errors.
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Choudhari, Sanjay, and Amit Tindwani. "Logistics optimisation in road construction project." Construction Innovation 17, no. 2 (April 3, 2017): 158–79. http://dx.doi.org/10.1108/ci-03-2016-0014.
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Purpose This study aims to assist the project manager in minimising the material logistics cost of road project by planning the optimal movement of aggregate across three stages of supply chain: sourcing, processing and distribution. Design/methodology/approach The paper conceptualises the raw material consumption in a road project as a logistics network distribution problem. A linear programming (LP) formulation is constructed with appropriate decision variables by integrating the three stages of material movement. The series of LP scenarios are solved using an LP solver to decide the optimal movement of the aggregate to be consumed in different layers of road segments. Findings The results obtained from the model show that planning material logistics of an entire road project using optimisation provides substantial saving in logistics costs than using common sense. Further, the magnitude of cost saving improves as the complexity of the model increases in term of enormous feasible options. Practical implications The model shown in this paper may serve as a basis for planning the logistics of raw materials consumed in the road projects. The small improvement in material flows by optimising supply chain shows sensible cost benefit to the project manager and hence control and monitor the overall cost and activities of the project. The output of the model is also expected to help the project team as an input in the decision-making processes such as appropriate material sourcing contract, capacity assessment of material processing facility and transportation planning. Originality/value While the optimisation models are widely used and popular among the many industrial applications, this research shows distinct application of such a model in managing the logistics of the road construction project.
20
P. S, Divya, Vijila Moses, Manoj G, and Lydia M. "Wind Turbine Energy Cost Optimisation Using Various Power Models." WSEAS TRANSACTIONS ON POWER SYSTEMS 17 (September 9, 2022): 261–68. http://dx.doi.org/10.37394/232016.2022.17.27.
Abstract:
In modern times, the worldwide wind turbine installations have developed swiftly resulting in the decrease of green gas emissions. Though wind is a free gift of nature, it is expensive to harness this energy for useful applications like electricity generation. The cost of installation of the wind turbine at a particular station does not depend only on the wind resource, but also on the structure of the turbine and the energy conversion technology. The wind turbine Cost of Energy (CoE) is used to estimate the payback time for the return on the investment made by the wind farm owners for the turbine. Meticulous research is required to optimize the turbine CoE which will make wind a very competent source of energy. In this article, in order to minimize the wind turbine CoE, the wind speed is modelled using three different distributions namely, Dagum, Gamma and Weibull and the evaluation of the turbine Annual Energy Production (AEP) is carried out. Mathematical functions such as linear, quadratic and cubic have been used to model the wind power. For the cost analysis of the turbine, the price model which was established by United States, National Renewable Energy Laboratory (NREL) is employed. The comparative study of the proposed methodology have been done for six different stations. The turbine CoE model is an element of two factors, the rated power Pr of a turbine and the rated wind speed Vr of a turbine. Based on the results obtained, a broad recommendation to reduce the turbine CoE is presented. This study enables us to figure out the minimum turbine CoE among the three discussed mathematical distributions, the finest distribution for wind speed modelling and the optimum mathematical function for wind power modelling. The suitable size of the wind turbine also can be found by optimizing the rotor radius R of the turbine for each data.
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Giglio, Enrico, Ermando Petracca, Bruno Paduano, Claudio Moscoloni, Giuseppe Giorgi, and Sergej Antonello Sirigu. "Estimating the Cost of Wave Energy Converters at an Early Design Stage: A Bottom-Up Approach." Sustainability 15, no. 8 (April 17, 2023): 6756. http://dx.doi.org/10.3390/su15086756.
Abstract:
The role of ocean energy is expected to grow rapidly in the coming years, and techno-economic analysis will play a crucial role. Nowadays, despite strong assumptions, the vast majority of studies model costs using a top-down approach (the TdA) that leads to an unrepresentative economic model. WEC developers usually go through the the TdA approach because more detailed cost data are not available at an earlier design stage. At a very advanced design stage, some studies have also proposed techno-economic optimisation based on the bottom-up approach (BuA). This entails that the detailed cost metrics presented in the literature are very specific to the WEC type (hence not applicable to other cases) or unrepresentative. This lack of easily accessible detailed cost functions in the current state of the art leads to ineffective optimisations at an earlier stage of WEC development. In this paper, a BuA for WECs is proposed that can be used for techno-economic optimisation at the early design stage. To achieve this goal, cost functions of most common components in the WEC field are retrieved from the literature, exposed, and critically compared. The large number of components considered allows the results of this work to be applied to a vast pool of WECs. The novelty of the presented cost functions is their parameterization with respect to the technological specifications, which already enables their adoption in the design optimisation phase. With the goal of quantifying the results and critically discuss the differences between the TdA and the BuA, the developed methodology and cost functions are applied to a case study and specifically adopted for the calculation of the capital cost of PeWEC (pendulum wave energy converter). In addition, a hybrid approach (HyA) is presented and discussed as an intermediate approach between the TdA and the BdA. Results are compared in terms of capital expenditure (CapEx) and pie cost distribution: the impact of adopting different cost metrics is discussed, highlighting the role that reliable cost functions can have on early stage technology development. This paper proposes more than 50 cost functions for WEC components. Referring to the case study, it is shown that while the total cost differs only slightly (11%), the pie distribution changes by up to 22%. Mooring system and power take-off are the cost items where the TdA and the HyA differ more from the BuA cost estimate.
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Vojinovic, Z., D. Solomatine, and R. K. Price. "Dynamic least-cost optimisation of wastewater system remedial works requirements." Water Science and Technology 54, no. 6-7 (September 1, 2006): 467–75. http://dx.doi.org/10.2166/wst.2006.574.
Abstract:
In recent years, there has been increasing concern for wastewater system failure and identification of optimal set of remedial works requirements. So far, several methodologies have been developed and applied in asset management activities by various water companies worldwide, but often with limited success. In order to fill the gap, there are several research projects that have been undertaken in exploring various algorithms to optimise remedial works requirements, but mostly for drinking water supply systems, and very limited work has been carried out for the wastewater assets. Some of the major deficiencies of commonly used methods can be found in either one or more of the following aspects: inadequate representation of systems complexity, incorporation of a dynamic model into the decision-making loop, the choice of an appropriate optimisation technique and experience in applying that technique. This paper is oriented towards resolving these issues and discusses a new approach for the optimisation of wastewater systems remedial works requirements. It is proposed that the optimal problem search is performed by a global optimisation tool (with various random search algorithms) and the system performance is simulated by the hydrodynamic pipe network model. The work on assembling all required elements and the development of an appropriate interface protocols between the two tools, aimed to decode the potential remedial solutions into the pipe network model and to calculate the corresponding scenario costs, is currently underway.
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Ye, Wong. "HEAT EXCHANGER DESIGN OPTIMISATION." INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES 9, no. 2 (June 30, 2021): 19–28. http://dx.doi.org/10.55083/irjeas.2021.v09i02008.
Abstract:
In this paper, an optimized model of Shell and Tube Parallel Flow Heat Exchanger of water and oil type is proposed using C++ programming language. Shell and tube heat exchangers are of special importance in boilers, oil coolers, condensers, pre-heaters etc. They are also used in high pressure operations, refrigeration and air conditioning industry and process applications. In this paper, a number of practical cases of shell and tube heat exchangers are taken and the analysis of thermal and constructional design of every case is done. The optimised design is obtained by minimising the pressure drop by maximising the heat exchanger area to reduce pumping and running cost. Also considering that large sizes lead to increased capital cost, heat exchanger area is also optimised to overcome this problem. Effect of design parameters on pressure drop and heat exchanger area is also explained.
24
Sun, Liang, and Bing Wang. "Robust Optimisation Approach for Vehicle Routing Problems with Uncertainty." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/901583.
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We formulated a solution procedure for vehicle routing problems with uncertainty (VRPU for short) with regard to future demand and transportation cost. Unlike E-SDROA (expectation semideviation robust optimisation approach) for solving the proposed problem, the formulation focuses on robust optimisation considering situations possibly related to bidding and capital budgets. Besides, numerical experiments showed significant increments in the robustness of the solutions without much loss in solution quality. The differences and similarities of the robust optimisation model and existing robust optimisation approaches were also compared.
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Korponai, János, Ágota Bányainé Tóth, and Béla Illés. "Context of the inventory management expenses in the case of planned shortages." Engineering Management in Production and Services 9, no. 1 (March 1, 2017): 26–35. http://dx.doi.org/10.1515/emj-2017-0003.
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AbstractThe main purpose of the paper is to present the relations between the different cost factors of the inventory management systems, and the context between the order quantities and the cost level. The theoretical approach of the model assumes a deterministic operational environment with planned shortages. We make the examination of the contexts by applying the ceteris paribus principle; we change only one cost factor from among the initial conditions at once and examine its effect on the cost level.By using the economic order quantity with the planned shortage model, we can define the optimal order quantity, along which our stock management can be guaranteed by the most favourable cost level. The optimisation of the inventory level and the inventory management expenses together means an important factor in the competitiveness of the company. During the definition of the optimal inventory level of purchased parts, the purchasing and stock holding costs, and also the consequence of shortages play an important role. The presentation of the specific expense factors in each other’s function, and the representation of the onetime order expenses show their proportion compared to each other and the effect of their change on the total cost, and define the opportunities of the optimisation. The significance of the model is that it represents the level line of costs, the movement of the different cost factors in relation to others and their operating mechanism. Thus, it facilitates the representation of costs and the definition of the direction of optimisation.
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Rasku, Topi, Toni Lastusilta, Ala Hasan, Rakesh Ramesh, and Juha Kiviluoma. "Economic Model-Predictive Control of Building Heating Systems Using Backbone Energy System Modelling Framework." Buildings 13, no. 12 (December 12, 2023): 3089. http://dx.doi.org/10.3390/buildings13123089.
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Accessing the demand-side management potential of the residential heating sector requires sophisticated control capable of predicting buildings’ response to changes in heating and cooling power, e.g., model-predictive control. However, while studies exploring its impacts both for individual buildings as well as energy markets exist, building-level control in large-scale energy system models has not been properly examined. In this work, we demonstrate the feasibility of the open-source energy system modelling framework Backbone for simplified model-predictive control of buildings, helping address the above-mentioned research gap. Hourly rolling horizon optimisations were performed to minimise the costs of flexible heating and cooling electricity consumption for a modern Finnish detached house and an apartment block with ground-to-water heat pump systems for the years 2015–2022. Compared to a baseline using a constant electricity price signal, optimisation with hourly spot electricity market prices resulted in 3.1–17.5% yearly cost savings depending on the simulated year, agreeing with comparable literature. Furthermore, the length of the optimisation horizon was not found to have a significant impact on the results beyond 36 h. Overall, the simplified model-predictive control was observed to behave rationally, lending credence to the integration of simplified building models within large-scale energy system modelling frameworks.
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S.Sukumar, Sukhadeva, Abu Hanifah Yusof, Muhamad Iqbal Aslam Abd Hafiz, Muhamad Razuhanafi Mat Yazid, Mohd Azizul Ladin, and Mukhlis Nahriri Bastam. "Sustainable Cost Optimisation Measures for The Lifecycle of Tolled Highway Projects in Malaysia." Jurnal Kejuruteraan 35, no. 2 (March 30, 2023): 475–84. http://dx.doi.org/10.17576/jkukm-2023-35(2)-19.
Abstract:
The implementation and maintenance of highway infrastructure often requires significant capital throughout its life cycle which affects stakeholders including the government, developers, operators, users, etc. Furthermore, the sustainability aspect and existing toll systems in Malaysia are currently in the midst of being re-evaluated in order to attain a long-term gain that benefits both road users and relevant stakeholders. The objective of this study is to propose a Life Cycle Cost Analysis (LCCA) model for sustainable highway projects in Malaysia which considers certain cost optimisation measures throughout the stages of concept, design, construction, and operation & maintenance. The proposed LCCA model intends to act as a cost optimisation tool that provides sustainability recommendations for toll systems, highway alignments, pavement maintenance and rehabilitation, existing policies, contract and project type, material, equipment, time-cost factor, etc. Additionally, a relationship between the financial efficiency of toll systems and the affect it has on the overall cost of highway projects was established. The significance of cost pertaining to highway infrastructure components and the perception of toll systems was evaluated via a survey questionnaire; distributed to a select group of senior and principal engineers. The survey utilised a 5-point Likert scale which assisted in forming a regression analysis along with determining a correlation between toll systems and the overall cost of highway projects. Secondary data obtained from a reputable consultancy aided in understanding highway components that could potentially undergo further cost optimisation. Lastly, the sustainable and cost optimised LCCA model consists of recommendations and measures intended for a new age of sustainable highway projects in Malaysia.
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Chiteka, K., R. Arora, S. N. Sridhara, and C. C. Enweremadu. "Cleaning cycle optimisation in non-tracking ground mounted solar PV systems using Particle Swarm Optimisation." International Journal for Simulation and Multidisciplinary Design Optimization 11 (2020): 9. http://dx.doi.org/10.1051/smdo/2020004.
Abstract:
The effect of installation azimuth angle in the optimization of the cleaning cycle of a solar photovoltaic plant was experimentally investigated in this study. The optimum cleaning cycle was determined using Particle Swarm Optimization algorithm cognizance of the fact that different orientations have different soiling rates. Soiling rates on three different azimuth configurations were experimentally investigated and an exponential soiling loss model was developed for each configuration for use in the optimization problem. Azimuth angle differences of ±12.5% were found to have a significant influence on soiling of as much as 28.29% for the selected location. The North of North West configuration was found to be optimal as opposed to the generally accepted North configuration for maximum energy generation at a minimum cost of energy. This configuration generated 0.87% more energy at unit energy cost of $0.093 compared to the North configuration which had a minimum cost of $0.113. The optimized cleaning cycles were 35 days for the optimal configuration while the North configuration had an optimized cleaning cycle of 28 days. A 17.7% difference in the cost of energy was recorded due the influence of soiling. The study revealed that for minimizing the unit energy cost, it is necessary to take into effect the influence of soiling.
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Iaria, Davide, Homam Nipkey, Jafar Al Zaili, Abdulnaser Sayma, and Mohsen Assadi. "Development and Validation of a Thermo-Economic Model for Design Optimisation and Off-Design Performance Evaluation of a Pure Solar Microturbine." Energies 11, no. 11 (November 18, 2018): 3199. http://dx.doi.org/10.3390/en11113199.
Abstract:
The aim of this paper is to present a thermo-economic model of a microturbine for solar dish applications, which demonstrates the applicability and accuracy of the model for off-design performance evaluation and techno-economic optimisation purposes. The model is built using an object-oriented programming approach. Each component is represented using a class made of functions that perform a one-dimensional physical design, off-design performance analysis and the component cost evaluation. Compressor, recuperator, receiver and turbine models are presented and validated against experimental data available in literature, and each demonstrated good accuracy for a wide range of operating conditions. A 7-kWe microturbine and solar irradiation data available for Rome between 2004 and 2005 were considered as a case study, and the thermo-economic analysis of the plant was performed to estimate the levelised cost of electricity based on the annual performance of the plant. The overall energy produced by the plant is 10,682 kWh, the capital cost has been estimated to be EUR 27,051 and, consequently, the specific cost of the plant, defined as the ratio between the cost of components and output power in design condition, has been estimated to be around EUR 3980/kWe. Results from the levelised cost of electricity (LCOE) analysis demonstrate a levelised cost of electricity of EUR 22.81/kWh considering a plant lifetime of 25 years. The results of the present case study have been compared with the results from IPSEpro 7 where the same component characteristic maps and operational strategy were considered. This comparison was aimed to verify the component matching procedure adopted for the present model. A plant sizing optimisation was then performed to determine the plant size which minimises the levelised cost of electricity. The design space of the optimisation variable is limited to the values 0.07–0.16 kg/s. Results of the optimisation demonstrate a minimum LCOE of 21.5 [EUR/kWh] for a design point mass flow rate of about 0.11 kg/s. This corresponds to an overall cost of the plant of around EUR 32,600, with a dish diameter of 9.4 m and an annual electricity production of 13,700 [kWh].
30
Bálint, Roland, and Attila Magyar. "Refrigerator Optimal Scheduling to Minimise the Cost of Operation." Hungarian Journal of Industry and Chemistry 44, no. 2 (December 1, 2016): 99–104. http://dx.doi.org/10.1515/hjic-2016-0012.
Abstract:
Abstract The cost optimal scheduling of a household refrigerator is presented in this work. The fundamental approach is the model predictive control methodology applied to the piecewise affine model of the refrigerator. The optimisation could not be solved using off-the-shelf tools, e.g. Multi-Parametric Toolbox, so a binary treebased optimal scheduling algorithm has been developed for this problem.
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Chen, Hao, Chihua Lu, Zhien Liu, Cunrui Shen, and Menglei Sun. "Multi-Response Optimisation of Automotive Door Using Grey Relational Analysis with Entropy Weights." Materials 15, no. 15 (August 3, 2022): 5339. http://dx.doi.org/10.3390/ma15155339.
Abstract:
Tail-welded blanks (TWBs) are widely used in automotive bodies to improve the structural performance and reduce weight. The stiffness and modal lightweight design optimisation of TWBs for automotive doors was performed in this study. The finite element model was validated through physical experiments. An L27 (312) Taguchi orthogonal array was used to collect the sample points. The multi-objective optimisation problem was transformed into a single-objective optimisation problem based on the grey relational degree. The optimal combination of structural design parameters was obtained for a tail-welded door using the proposed method, and the weight of the door structure was reduced by 2.83 kg. The proposed optimisation method has fewer iterations and a lower computational cost, enabling the design of lightweight TWBs.
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Csordas, Helga. "Calendars in Time-Cost Trade-Off." Periodica Polytechnica Architecture 50, no. 1 (April 16, 2019): 63–66. http://dx.doi.org/10.3311/ppar.13257.
Abstract:
In project management, there are two main operation problems. Scheduling and cost optimisation. These are interrelated and have mathematically proven solutions for the basics. However, in case of applying arbitrary calendars, there may be generated such effects in scheduling that make the known time-cost trade-off model unusable. In consideration of these effects, this paper aims to apply known algorithms that have been successful for other problems.
33
McCoy, M. S. "System of systems force structure optimisation." Aeronautical Journal 110, no. 1109 (July 2006): 457–62. http://dx.doi.org/10.1017/s0001924000001354.
Abstract:
Abstract A system of systems study plan was developed and a prototype was executed to optimise a recommended military force structure. This methodology defined the optimal force structure, using constrained optimisation to reflect budget limitations and desired mission performance. The force structure included surface and air assets, a command, control, communications, computers, intelligence, surveillance and reconnaissance (C4ISR) architecture, and a recommended logistics infrastructure. A second aspect of the study plan defined the total acquisition strategy, which accounted for: retiring legacy assets, extending the service life of existing assets until new replacements became available, and acquiring new assets for deployment, within the budget allocation. This methodology combined various modeling and simulation techniques to meet three study objectives. First, a nonlinear mixed integer programming model maximised performance, subject to cost constraints, cost as an independent variable (CAIV). Second, a dynamic programming model scheduled the transition from the legacy force structure to the future force, defined by the previous modeling technique. Third, a process simulation model simulated performance, over a one-year time period, for 25 areas of responsibility and five missions. This model verified performance estimates generated by the previous models.
34
Echtermeyer, Alexander, Yehia Amar, Jacek Zakrzewski, and Alexei Lapkin. "Self-optimisation and model-based design of experiments for developing a C–H activation flow process." Beilstein Journal of Organic Chemistry 13 (January 24, 2017): 150–63. http://dx.doi.org/10.3762/bjoc.13.18.
Abstract:
A recently described C(sp3)–H activation reaction to synthesise aziridines was used as a model reaction to demonstrate the methodology of developing a process model using model-based design of experiments (MBDoE) and self-optimisation approaches in flow. The two approaches are compared in terms of experimental efficiency. The self-optimisation approach required the least number of experiments to reach the specified objectives of cost and product yield, whereas the MBDoE approach enabled a rapid generation of a process model.
35
De Gussem, K., T. Wambecq, J. Roels, A. Fenu, G. De Gueldre, and B. Van De Steene. "Cost optimisation and minimisation of the environmental impact through life cycle analysis of the waste water treatment plant of Bree (Belgium)." Water Science and Technology 63, no. 1 (January 1, 2011): 164–70. http://dx.doi.org/10.2166/wst.2011.027.
Abstract:
An ASM2da model of the full-scale waste water plant of Bree (Belgium) has been made. It showed very good correlation with reference operational data. This basic model has been extended to include an accurate calculation of environmental footprint and operational costs (energy consumption, dosing of chemicals and sludge treatment). Two optimisation strategies were compared: lowest cost meeting the effluent consent versus lowest environmental footprint. Six optimisation scenarios have been studied, namely (i) implementation of an online control system based on ammonium and nitrate sensors, (ii) implementation of a control on MLSS concentration, (iii) evaluation of internal recirculation flow, (iv) oxygen set point, (v) installation of mixing in the aeration tank, and (vi) evaluation of nitrate setpoint for post denitrification. Both an environmental impact or Life Cycle Assessment (LCA) based approach for optimisation are able to significantly lower the cost and environmental footprint. However, the LCA approach has some advantages over cost minimisation of an existing full-scale plant. LCA tends to chose control settings that are more logic: it results in a safer operation of the plant with less risks regarding the consents. It results in a better effluent at a slightly increased cost.
36
Sun, Liang, and Bing Wang. "An Inverse Robust Optimisation Approach for a Class of Vehicle Routing Problems under Uncertainty." Discrete Dynamics in Nature and Society 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2804525.
Abstract:
There is a trade-off between the total penalty paid to customers (TPC) and the total transportation cost (TTC) in depot for vehicle routing problems under uncertainty (VRPU). The trade-off refers to the fact that the TTC in depot inevitably increases when the TPC decreases andvice versa. With respect to this issue, the vehicle routing problem (VRP) with uncertain customer demand and travel time was studied to optimise the TPC and the TTC in depot. In addition, an inverse robust optimisation approach was proposed to solve this kind of VRPU by combining the ideas of inverse optimisation and robust optimisation so as to improve both the TPC and the TTC in depot. The method aimed to improve the corresponding TTC of the robust optimisation solution under the minimum TPC through minimising the adjustment of benchmark road transportation cost. According to the characteristics of the inverse robust optimisation model, a genetic algorithm (GA) and column generation algorithm are combined to solve the problem. Moreover, 39 test problems are solved by using an inverse robust optimisation approach: the results show that both the TPC and TTC obtained by using the inverse robust optimisation approach are less than those calculated using a robust optimisation approach.
37
Zhao, Jie, Mingcheng Zhang, Biao Zhao, Xiao Du, Huaixun Zhang, Lei Shang, and Chenhao Wang. "Integrated Reactive Power Optimisation for Power Grids Containing Large-Scale Wind Power Based on Improved HHO Algorithm." Sustainability 15, no. 17 (August 28, 2023): 12962. http://dx.doi.org/10.3390/su151712962.
Abstract:
Large-scale wind power grid integration will greatly change the system current distribution, making it difficult for the reactive power regulator to adjust to the optimal state. In this paper, an integrated reactive power optimisation method based on the improved Harris Hawk (HHO) algorithm is proposed. Firstly, a reactive power regulation model is constructed to solve the reactive power regulation interval of wind turbines, and the reactive power margin of wind turbines is used to participate in the system’s reactive power optimisation. Finally, a reactive power compensation capacity allocation optimisation model considering nodal voltage deviation, line loss and equipment investment cost, is established, and a reactive power optimisation scheme is obtained using the Harris Hawk optimisation algorithm on the basis of considering the constraints of the wind turbine reactive power output interval. The improved HHO algorithm is used to solve the reactive power optimisation scheme considering the constraints of tidal power, machine end voltage, a conventional generator and wind farm reactive power. In the simulation, the effects of the improved Harris Hawk optimisation algorithm and the particle swarm optimisation algorithm are compared, and the experimental results prove that compared to the particle swarm algorithm, the optimisation result of the improved Harris Hawk optimisation algorithm reduces the average loss of the system by 42.6% and reduces the average voltage deviation by 30.3%, which confirms that the improved Harris Hawk intelligent optimisation algorithm is effective in proving its superiority and solving the multi-objective model for reactive power optimisation.
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Rotua Ignasia Saragih and Tri Andri Hutapea. "Optimasi Biaya Pengendalian Persediaan Alat Suntik (Spuit) Dengan Metode Optimisasi Robust Menggunakan Aplikasi Python Di RSUD Dr. Pirngadi." JURNAL RISET RUMPUN MATEMATIKA DAN ILMU PENGETAHUAN ALAM 2, no. 2 (July 5, 2023): 72–86. http://dx.doi.org/10.55606/jurrimipa.v2i2.1348.
Abstract:
Inventory control is very important for companies because without proper inventory control the company will experience problems in meeting consumer needs both in the form of goods and services produced by the company. RSUD Dr. Pirngadi is one of the companies that uses conventional methods to calculate the total cost of inventory so that the costs incurred both for ordering syringes and storage costs are still high, therefore it is necessary to control the inventory of syringes which aims to minimise inventory costs so that company goals can be achieved. In solving the problem of controlling inventory costs to minimise total inventory costs, namely using robust optimisation. Robust optimisation is an optimisation model that contains uncertainty data to obtain the right solution using linear program solving. The results of research at the RSUD Dr. Pirngadi obtained the total cost of inventory according to company policy for the use of 3 mL Terumo syringes is Rp.426,104,665 while the robust optimisation method is Rp.319,647,106 so it can be concluded that by applying the robust optimisation method to the company can save inventory costs of Rp.106,987,599 or 25%. Furthermore, the total inventory cost of using a 5 mL Terumo syringe according to company policy is Rp.208,402,454 while the robust optimisation method is Rp.166,608,139 so it can be concluded that by applying the robust optimisation method to the company can save inventory costs of Rp.41,794,315 or 20%.
39
Akram, Junaid, Arsalan Tahir, Hafiz Suliman Munawar, Awais Akram, Abbas Z. Kouzani, and M. A. Parvez Mahmud. "Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation." Sensors 21, no. 23 (November 25, 2021): 7846. http://dx.doi.org/10.3390/s21237846.
Abstract:
The smart grid (SG) is a contemporary electrical network that enhances the network’s performance, reliability, stability, and energy efficiency. The integration of cloud and fog computing with SG can increase its efficiency. The combination of SG with cloud computing enhances resource allocation. To minimise the burden on the Cloud and optimise resource allocation, the concept of fog computing integration with cloud computing is presented. Fog has three essential functionalities: location awareness, low latency, and mobility. We offer a cloud and fog-based architecture for information management in this study. By allocating virtual machines using a load-balancing mechanism, fog computing makes the system more efficient (VMs). We proposed a novel approach based on binary particle swarm optimisation with inertia weight adjusted using simulated annealing. The technique is named BPSOSA. Inertia weight is an important factor in BPSOSA which adjusts the size of the search space for finding the optimal solution. The BPSOSA technique is compared against the round robin, odds algorithm, and ant colony optimisation. In terms of response time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 53.99 ms, 82.08 ms, and 81.58 ms, respectively. In terms of processing time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 52.94 ms, 81.20 ms, and 80.56 ms, respectively. Compared to BPSOSA, ant colony optimisation has slightly better cost efficiency, however, the difference is insignificant.
40
Guo, S. Z., X. M. Zheng, H. S. Ang, and H. M. Cai. "Cruise missile head shape optimisation using an adaptive sampling surrogate model." Aeronautical Journal 122, no. 1253 (May 8, 2018): 1145–62. http://dx.doi.org/10.1017/aer.2018.40.
Abstract:
ABSTRACTHigh-precision response of the surrogate model is desired in the process of optimisation. An excessive number of sampling points will increase the cost of the calculation. The appropriate number of sampling points cannot only guarantee the accuracy of the surrogate model but also save the calculation cost. The purpose of this research is to demonstrate the eventuality of using an adaptive surrogate model for optimisation problems. The adaptive surrogate model is built on an adaptive sampling approach and an extended radial basis function (ERBF). The adaptive sampling is an approach that new sampling points are placed in the blank area and all the sampling points are uniformly distributed in the design region using Multi-Island GA. The precision of the ERBF surrogate model is checked using standard error measure to determine whether the surrogate model should be updated or not. This adaptive surrogate model is used to optimise a cruise missile head shape. Aerodynamic and stealthy performance of the cruise missile head shape are considered in this research. Different global objective function and different weight factor are used to research the aerodynamic and stealthy performance in this optimisation process. The results show that the drag is reduced with a slender head shape and the radar-cross section (RCS) value is reduced with a short head shape.
41
Andrawus, Jesse A., John Watson, Mohammed Kishk, and Heather Gordon. "Optimisation of Wind Turbine Inspection Intervals." Wind Engineering 32, no. 5 (October 2008): 477–90. http://dx.doi.org/10.1260/030952408786411921.
Abstract:
The choice of correct inspection intervals poses a serious challenge to industries that utilise physical assets. Too short an interval increases operational cost and waste production time while too long an interval increases the likelihood of unexpected asset failures. Failure Modes and Effect Criticality Analysis (FMECA) is a technique that permits qualitative evaluation of assets' functions to predict critical failure modes and the resultant consequences to determine appropriate maintenance tasks for the assets. The Delay-Time Maintenance Model (DTMM) is a quantitative maintenance optimisation technique that examines equipment failure patterns by taking into account failure consequences, inspection time and cost in order to determine optimum inspection interval. In this paper, a hybrid of FMECA and DTMM is used to assess the failure characteristics of a selected subsystems of a chosen wind turbine. Optimal inspection intervals for critical subsystems of the wind turbine are determined to minimise its total life-cycle cost.
42
Purnomo, Muhammad Ridwan Andi, Dzuraidah Abdul Wahab, and Ade Rizqy Anugerah. "Optimisation of the Single-Vendor Single-Buyer Supply Chain System under Fuzzy Demand Using Optimisation–Simulation Closed Loop Technique." Mathematical Modelling of Engineering Problems 9, no. 5 (December 13, 2022): 1343–51. http://dx.doi.org/10.18280/mmep.090524.
Abstract:
This research aims to model and optimise the most applicable supply chain system, which is a single-vendor single-buyer system with fuzzy buyer demand. An optimisation model of the supply chain system under consideration is built by formulating the objective function, which is minimising the joint total cost between a buyer and a vendor. The model is developed on the basis of a simulation system, and optimisation is carried out by utilising a Genetic Algorithm that has been embedded in the simulation system. This technique is called optimisation–simulation closed loop. The vendor actual condition, which deals with uncertain demands from the main buyer and other small buyers, is considered. To analyse timely supply chain events, a simulation system is developed. A new optimisation model for the single-vendor single-buyer supply chain system with fuzzy demand is developed on the basis of the simulation system. The use of optimisation–simulation closed loop is also a new finding. In this study, the optimisation model of the supply chain under consideration is developed by taking into account a specific condition in which the vendor receives demands from the main buyer and other small buyers. Naturally, buyer demand is uncertain and has been modelled using a fuzzy set. The use of optimisation–simulation closed loop enables the supply chain to make the optimum decision when at the steady state condition.
43
Liu, Zheyu, Craig West, Barry Lennox, and Farshad Arvin. "Local Bearing Estimation for a Swarm of Low-Cost Miniature Robots." Sensors 20, no. 11 (June 10, 2020): 3308. http://dx.doi.org/10.3390/s20113308.
Abstract:
Swarm robotics focuses on decentralised control of large numbers of simple robots with limited capabilities. Decentralised control in a swarm system requires a reliable communication link between the individuals that is able to provide linear and angular distances between the individuals—Range & Bearing. This study presents the development of an open-source, low-cost communication module which can be attached to miniature sized robots; e.g., Mona. In this study, we only focused on bearing estimation to mathematically model the bearings of neighbouring robots through systematic experiments using real robots. In addition, the model parameters were optimised using a genetic algorithm to provide a reliable and precise model that can be applied for all robots in a swarm. For further investigation and improvement of the system, an additional layer of optimisation on the hardware layout was implemented. The results from the optimisation suggested a new arrangement of the sensors with slight angular displacements on the developed board. The precision of bearing was significantly improved by optimising in both software level and re-arrangement of the sensors’ positions on the hardware layout.
44
Zhang, Han, Jibin Yang, Jiye Zhang, Pengyun Song, and Ming Li. "Optimal energy management of a fuel cell-battery-supercapacitor-powered hybrid tramway using a multi-objective approach." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 234, no. 5 (May 15, 2019): 511–23. http://dx.doi.org/10.1177/0954409719849804.
Abstract:
Achieving an optimal operating cost is a challenge for the development of hybrid tramways. In the past few years, in addition to fuel costs, the lifespan of the power source is being increasingly considered as an important factor that influences the operating cost of a tramway. In this work, an optimal energy management strategy based on a multi-mode strategy and optimisation algorithm is described for a high-power fuel cell hybrid tramway. The objective of optimisation is to decrease the operating costs under the conditions of guaranteeing tramway performance. Besides the fuel costs, the replacement cost and initial investment of all power units are also considered in the cost model, which is expressed in economic terms. Using two optimisation algorithms, a multi-population genetic algorithm and an artificial fish swarm algorithm, the hybrid system's power targets for the energy management strategy were acquired using the multi-objective optimisation. The selected case study includes a low-floor light rail vehicle, and experimental validations were performed using a hardware-in-the-loop workbench. The results testify that an optimised energy management strategy can fulfil the operational requirements, reduce the daily operation costs and improve the efficiency of the fuel cell system for a hybrid tramway.
45
Akparibo, Awingot Richard, and Erwin Normanyo. "Application of resistance energy model to optimising electric power consumption of a belt conveyor system." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (June 1, 2020): 2861. http://dx.doi.org/10.11591/ijece.v10i3.pp2861-2873.
Abstract:
Driven by constantly increasing energy demands, prices, environmental impact caused by carbon dioxide emissions and global warming, efficient use of energy is gaining grounds in both public and private enterprises. The energy consumption of belt conveyors can be lowered using energy modelling techniques. In this research,
a resistance-based mathematical energy model was utilised in
the electrical energy efficiency optimisation of the troughed, inclined belt conveyor system taking into account indentation rolling resistance, bulk solid flexure resistance and secondary resistance as they together contribute 89% resistance to motion. An optimisation problem was formulated to optimise the electrical energy efficiency of the belt conveyor system and subsequently solved using
the “fmincon” solver and interior point algorithm of the MATLAB optimisation toolbox. Analysis of simulation results showed that for the same given operating capacities, an average energy saving of about 7.42% and an annual total cost savings of Gh¢ 5, 852, 669.00 (USD 1, 083, 827.59) for a 2592-hour operation can be achieved when the used model and optimisation technique are employed over the constant speed operation.
46
Rouleau, Lucie, Boris Lossouarn, and Jean-François Deü. "Optimal viscoelastic properties for passive damping treatments." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 268, no. 3 (November 30, 2023): 5816–23. http://dx.doi.org/10.3397/in_2023_0831.
Abstract:
In the context of noise and vibration control, constrained viscoelastic materials can be used to reduce structure-borne noise of vibrating structure. This passive damping treatment finds many applications due to its robustness and low cost. While several approaches are proposed in the literature to optimise the placement of constrained viscoelastic patches, few studies has been dedicated to the optimisation of the viscoelastic material's properties. The difficulty stems from their frequency-dependency. In this work, a fractional derivative model is used to describe the complex frequency-dependent viscoelastic properties. This study illustrates the effectiveness of using surrogate modeling to predict the damping performance of viscoelastic patches. A multi-model reduction method combined with a modal parameter identification method is used to build a surrogate model at a reasonable computational cost. Once trained, this metamodel is explored to optimise the parameters of the fractional derivative model, thus allowing the design optimisation of viscoelastic properties. This approach could be extended to optimise the material properties of viscoelastic foams in the low frequency range.
47
Wang, Bangjia, Xuanyu Jie, and Jiayu Chen. "Research On Replenishment Strategy Of Vegetable Commodities Based On Linear Programming And Multi-Objective Particle Swarm Optimisation Algorithm." Journal of Education, Humanities and Social Sciences 25 (January 26, 2024): 198–205. http://dx.doi.org/10.54097/7rkc4637.
Abstract:
Because vegetable commodities are characterised by shorter freshness periods and diminishing character, they need to be replenished and priced on a daily basis based on historical sales and demand. In order to determine the optimal order quantity and sales price of vegetable commodities, this paper uses the least squares method to establish a regression model to obtain the relationship between sales volume and cost-plus pricing equation and verify the fitting effect, and then the total sales volume of the category obtained from the regression model and the cost-plus pricing equation combined with the derivation of the profit, the establishment of the optimisation model, the solution to obtain the replenishment volume and pricing strategy that will maximise the profitability of the superstore in the coming week. Considering the restricted situation of commodity categories, this paper chooses 0-1 integer planning,and adopts the multi-objective particle swarm optimisation algorithm to solve the optimisation model, and then iteratively calculates to get the global optimal solution, so as to find out the daily replenishment quantity of a single product and pricing strategy which will make the supercommercial supermarket have the largest revenue on 1 July.
48
Qiuyue Zhang, Guoping Zhang,. "Building Engineering Cost Prediction Based On Deep Learning: Model Construction and Real - Time Optimization." Journal of Electrical Systems 20, no. 5s (April 13, 2024): 151–64. http://dx.doi.org/10.52783/jes.1887.
Abstract:
Effective project planning, risk mitigation, and stakeholder satisfaction in the construction business are greatly impacted by accurate cost projection. Overspending, setbacks, and ruined projects are all possible results of imprecise cost estimates. For this reason, it is critical to guarantee the viability and success of a project by increasing the precision of cost predictions. Construction project complexity, a myriad of cost variables, and uncertainty are the obstacles that building engineering cost prediction must overcome. Predictions made using traditional approaches are commonly inaccurate because they fail to fully account for the complex interplay between project factors and expenses. Advanced modelling techniques that can handle complicated data and changeable project contexts are necessary to overcome these obstacles. An approach based on deep learning called Deep CostNet for Building Engineering Technique (DCN-BET) Cost Prediction is presented in this research. Its purpose is to solve the problems associated with building engineering cost prediction. The approach uses deep neural networks to extract intricate patterns from massive amounts of project data collected over time. Improved prediction accuracy and real-time optimisation during project execution are made possible by DCN-BET, which captures the nonlinear correlations between project characteristics and costs. Risk assessment and management, cost forecasting for resource allocation, and project budget estimation and planning are among the few of the many construction industry uses for DCN-BET. The effectiveness of DCN-BET is assessed by conducting thorough simulation analyses in contrast to more conventional cost prediction approaches. Training and testing the model with real-world building engineering datasets allows us to evaluate its accuracy and efficacy in project cost prediction. The results show that DCN-BET has the capacity to support real-time optimisation and significantly improved the accuracy of cost predictions, which improved the overall success and efficiency of the project.
49
Beckers, Jarl, Diederik Coppitters, Ward De Paepe, Francesco Contino, Joeri Van Mierlo, and Björn Verrelst. "Multi-Fidelity Design Optimisation of a Solenoid-Driven Linear Compressor." Actuators 9, no. 2 (May 11, 2020): 38. http://dx.doi.org/10.3390/act9020038.
Abstract:
Improved management and impermeability of refrigerants is a leading solution to reverse global warming. Therefore, crank-driven reciprocating refrigerator compressors are gradually replaced by more efficient, oil-free and hermetic linear compressors. However, the design and operation of an electromagnetic actuator, fitted on the compression requirements of a reciprocating linear compressor, received limited attention. Current research mainly focuses on the optimisation of short stroke linear compressors, while long stroke compressors benefit from higher isentropic and volumetric efficiencies. Moreover, designing such a system focuses mainly on the trade-off between number of copper windings and the current required, due to the large computational cost of performing a full geometric design optimisation based on a Finite Element Method. Therefore, in this paper, a computationally-efficient, multi-objective design optimisation for six geometric design parameters has been applied on a solenoid driven linear compressor with a stroke of 44.2 mm. The proposed multi-fidelity optimisation approach takes advantage of established models for actuator optimisation in mechatronic applications, combined with analytical equations established for a solenoid actuator to increase the overall computational efficiency. This paper consists of the multi-fidelity optimisation algorithm, the analytic model and Finite Element Method of a solenoid and the optimised designs obtained for optimised power and copper volume, which dominates the actuator cost. The optimisation results illustrate a trade-off between minimising the peak power and minimising the volume of copper windings. Considering this trade-off, an intermediate design is highlighted, which requires 33.3% less power, at the expense of an increased copper volume by 5.3% as opposed to the design achieving the minimum copper volume. Despite that the effect of the number of windings on the input current remains a dominant design characteristic, adapting the geometric parameters reduces the actuator power requirements significantly as well. Finally, the multi-fidelity optimisation algorithm achieves a 74% reduction in computational cost as opposed to an entire Finite Element Method optimisation. Future work focuses on a similar optimisation approach for a permanent magnet linear actuator.
50
Wang, Zhiliang, Xianan Li, Kunmin Liu, and Long Zhao. "Optimisation of regional energy demand networks based on flexible load model." Journal of Physics: Conference Series 2310, no. 1 (October 1, 2022): 012012. http://dx.doi.org/10.1088/1742-6596/2310/1/012012.
Abstract:
Abstract The existing literature on integrated energy station-grid planning studies focuses on configuration planning. Therefore, a multi-objective hybrid particle swarm algorithm is used in this paper to solve a multi-objective optimisation model and combines a multi-indicator evaluation method based on evidence-based reasoning to select the solution with the highest evaluation value from multiple candidates (Pareto solution set) as the optimal solution. The optimal planning solution is selected from multiple candidates (Pareto solution set) by a multi-indicator evaluation method based on evidence-based reasoning, and the effectiveness of the proposed model is verified utilizing an example. The customer participation demand response optimises the demand curve by adjusting the flexible load, reduces the system operation cost and investment construction cost, and maximises the economic, environmental and reliability benefits of the integrated regional energy station grid.