With the ever growing number of technologies that interact with the grid, optimization and control strategies become critical to ensure the quality of service without disruption. This work analyses the application of ...
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A large part of the services provided on the Internet resides in virtualized environments of 'cloud' providers, whose IT infrastructures adopt, for the most part, the containers virtualization technique to hou...
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This paper presents two mixed integer linear programming (MILP) models that extend two basic Flow Shop Scheduling problems: Fm/prmu/Cmax and Fm/block/Cmax. This extension incorporates the concept of an overall demand ...
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This paper presents two mixed integer linear programming (MILP) models that extend two basic Flow Shop Scheduling problems: Fm/prmu/Cmax and Fm/block/Cmax. This extension incorporates the concept of an overall demand plan for types of jobs or products. After using an example to illustrate the new problems under study, we evaluated the new models and analyzed their behaviors when applied to instances found in the literature and industrial instances of a case study from Nissan's plant in Barcelona. CPLEX solver was used as a solution tool and obtained acceptable results, allowing us to conclude that MILP can be used as a method for solving Flow Shop Scheduling problems with an overall demand plan.
The Netherlands, with over 90% of homes heated by natural gas is currently in the early phases of the heat transition to find alternative solutions towards 2050. According to ambitions of the Dutch government up to 50...
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The Netherlands, with over 90% of homes heated by natural gas is currently in the early phases of the heat transition to find alternative solutions towards 2050. According to ambitions of the Dutch government up to 50% of future implemented heating systems in the built environment will use District Heat Networks. The investment-heavy nature for District Heating System (DHS) makes it challenging to establish viable business- cases as supply-side parties require security of demand. However, resident participation is lacking as there is no integral estimate for the cost of heat over the lifetime in the early planning phase. This paper proposes a network integral techno-economic optimization with minimal a-priori assumptions. An integral network cost optimization enables to achieve a considerably more reliable cost of heat in the early planning phase. Both the investments and operational strategy are optimized with a mixed integer linear programming approach that captures the physics as well as the financial choices. linearization of the physics are chosen to have a conservative impact on the costs estimates. The end scenario network is designed where the placement and size of sources, storage components and pipes are optimized together with the operational strategy, e.g. thermal allocation time-series, for all assets. The workflow was applied to a greenfield network for the Dutch municipality of Rijswijk. It was shown that a 18% reduction in Total Cost of Ownership in the primary grid could be achieved by introducing decentralized sources, decentralized storages and seasonal storage.
Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all...
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Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixedlinearprogramming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which w
Practitioners in construction management primarily focus on two key indicators of project success: total cost and completion time. Heavy equipment and machinery play pivotal role in determining these measures, represe...
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Practitioners in construction management primarily focus on two key indicators of project success: total cost and completion time. Heavy equipment and machinery play pivotal role in determining these measures, representing significant cost elements in various heavy construction projects such as road construction. Consequently, there is a pressing need for an efficient approach to determining the optimal scheduling of these heavy resources to minimize costs and shorten completion times. This paper proposes an innovative approach to address this challenge by introducing a mixed integer linear programming (MILP) model. The aim is to identify the optimal configuration for heavy equipment in earthmoving operations. The dynamic nature of the configuration process is adopted, enabling daily updates to the schedule based on the contractor's available resources. Moreover, environmental considerations are integrated into the decision-making process, ensuring a comprehensive approach to project optimization. To demonstrate the superiority of the developed model, three case projects from the literature have been solved. The proposed model led to a significant improvement in project cost, with an average enhancement of 25%, and in completion time, with an average improvement of 50% compared with the literature case studies. This paper presents a novel MILP model designed to optimize earthmoving operations, focusing on dynamic fleet configurations and emission costs. Unlike existing models, this approach provides daily fleet setups for multiple cut and fill sites, considering the contractor's available resources. It calculates optimal soil quantities to be moved, monitors soil levels at sites, and estimates daily trips between them. In the realm of project bidding and management, this model offers valuable insights and practical applications. It empowers project managers with a robust tool for optimizing fleet configurations during bid preparation, enabling contractors to determi
China has become the world second largest energy consumer, and IEA predicted that its primary energy demand will double from 2025 to 2030. Offshore wind power construction is difficult and carries high risk. It is ext...
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ISBN:
(纸本)9780791885932
China has become the world second largest energy consumer, and IEA predicted that its primary energy demand will double from 2025 to 2030. Offshore wind power construction is difficult and carries high risk. It is extremely difficult to control the cost, of which the construction period is the biggest variable. As to meet the requirements of cost reduction and efficiency increase in the coming era of flat rate, the construction procedures, primary vessels, and equipment need to be properly planned prior to the project startup. For this purpose, an algorithm based on mixed integer linear programming (MILP) for accurately calculating weather window and construction efficiency of offshore wind power was firstly proposed in this study, which comprehensively takes the complex construction procedures, duration of each link and environmental restrictions into account. Focusing on the offshore seas of Fujian province, utilizing the 1-hour hindcast wind and wave data respectively from the validated reanalysis wind field and SWAN model with a time span of 25 years (1996-2020), the weather window and construction efficiency for monopile foundation construction and offshore wind turbine installation were evaluated both on spatial and temporal scope. With respects to the specific wind farms that will be constructed, the detailed analysis and construction suggestions were provided.
mixed integer linear programming (MILP) is known as a type of programming that can combine continuous variables, integer variables, and (0-1) variables in the same algorithm and generate fiffing results for the data. ...
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mixed integer linear programming (MILP) is known as a type of programming that can combine continuous variables, integer variables, and (0-1) variables in the same algorithm and generate fiffing results for the data. Using this technique, it is possible to model and solve complex problems in many different fields such as economics, biology, engineering, etc. In the present study, a regional planning model was developed using MILP technique for the conversion of manure from dairy and beef cattle into biogas and electrical energy. For this regional planning study, considering the locations of future facilities, data on dairy and beef cattle in the Isparta province of TOrkiye were used. According to the model written and solution outputs, to utilize all manure obtained from dairy and beef cattles in Isparta, 5 biogas plants with a total manure processing capacity of approximately 522,000 tons should be built in different districts. It is possible to produce a total of approximately 21,000,000 m 3 of biogas and 38,500 MW of electricity per year in these biogas plants. This electrical energy obtained can meet 3.83% of the annual electricity consumption of Isparta province.
We present a mixed integer linear programming (MILP) approach in order to model the non-linear problem of minimizing the tire noise function. In a recent work, we proposed an exact solution for the Tire Noise Optimiza...
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We present a mixed integer linear programming (MILP) approach in order to model the non-linear problem of minimizing the tire noise function. In a recent work, we proposed an exact solution for the Tire Noise Optimization Problem, dealing with an APproximation of the noise (TNOP-AP). Here we study the original non-linear problem modeling the EXact- or real-noise (TNOP-EX) and propose a new scheme to obtain a solution for the TNOP-EX. Relying on the solution for the TNOP-AP, we use a Branch&Cut framework and develop an exact algorithm to solve the TNOP-EX. We also take more industrial constraints into account. Finally, we compare our experimental results with those obtained by other methods.
This paper formulates a mixed integer linear programming (MILP) model to optimize a system of electric vehicle (EV) charging stations. Our methodology introduces a two-stage framework that integrates the first-stage s...
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This paper formulates a mixed integer linear programming (MILP) model to optimize a system of electric vehicle (EV) charging stations. Our methodology introduces a two-stage framework that integrates the first-stage system design problem with a second-stage control problem of the EV charging stations and develops a design and analysis of computer experiments (DACE) based system design optimization solution method. Our DACE approach generates a metamodel to predict revenue from the control problem using multivariate adaptive regression splines (MARS), fit over a binned Latin hypercube (LH) experimental design. Comparing the DACE based approach to using a commercial solver on the MILP, it yields near optimal solutions, provides interpretable profit functions, and significantly reduces computational time for practical application.
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