This paper discusses a model that can optimize plant operation by considering transportation cost and metallurgical assays. All boundary conditions were set using ore samples taken from different locations in the same...
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This paper discusses a model that can optimize plant operation by considering transportation cost and metallurgical assays. All boundary conditions were set using ore samples taken from different locations in the same mine. Transportation from the mine to milling plants, dumping areas, and leaching units is also analyzed. Transportation from the mill to the smelter depends on assay results to ensure market readiness. We hypothesize that the grade of the material varies according to its source because milling plants are built to process different types of ore. Mines and processing units may be far apart, and therefore, it is critical to optimize production. Previous studies have discussed mine production optimization but did not consider metallurgical plants. The proposed model merges all units of a production system to determine the optimal planning solution. linear programming uses network formulations of planning problems to consider a combination of mining and metallurgy operations. This technology is useful for analyzing the combination of two major systems, and hence, the dual theory was used in formulating the problem. The final model was applied to minimize the costs of production and distribution. The study checked the optimality of the solution for accuracy and its use as an indicator for long-term planning.
The hybridization of energy systems is based on the combined integration of both renewable and non-renewable technologies and thermal energy storage. These hybrid installations improve cost effectiveness and energy ef...
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The hybridization of energy systems is based on the combined integration of both renewable and non-renewable technologies and thermal energy storage. These hybrid installations improve cost effectiveness and energy efficiency when they are correctly designed and the operation strategy is suitable. Despite the relevance of achieving the optimal configuration, sizing and control strategy of hybrid thermal systems, there is no simple and generic methodology which allows this type of installations to be optimized in the project phase. In response to this issue, in this work, a mixed integer linear programming-based simple model is carried out with the aim of obtaining the optimal design, sizing and operation of thermal energy systems in residential buildings. To do so, a superstructure is defined that includes the main technologies commercialized for thermal energy systems in buildings. Technical, economic, environmental and legal constraints are determined in the proposed generic model. In order to validate the method, it is applied to a central space heating and domestic hot water installation of a residential building located in a cold climate in Spain. Optimal solutions are obtained considering three different perspectives -economic, environmental and multicriteria- and are compared to the current installation. According to the results, the overall cost of the economic optimal configuration is reduced by 15%, whereas the greenhouse gas emissions decrease by 56% in the environmental optimal solution. It is thus demonstrated that the proposed generic and simple model is a useful tool for determining the optimal hybridization of the plant and for analysing the technical, economic and environmental feasibility of these systems in the project phase.
Road pavement costs expend a significant share of financial resources in road construction, and finding the optimal pavement thickness design with minimal cost remains a concern that can be determined using intelligen...
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Road pavement costs expend a significant share of financial resources in road construction, and finding the optimal pavement thickness design with minimal cost remains a concern that can be determined using intelligent search algorithms. This study aims to evaluate the performance of the particle swarm optimization (PSO) algorithm based on Iranian Highway Asphalt Paving (IHAP) Code 234. Thus, using the PSO algorithm, the problem of pavement thickness design was solved numerically, and a simulation-optimization technique was devised. Comparing PSO and the linear algorithms (LP) indicates that PSO is accounted as an optimal pavement thickness design regardless of different ranges of the equivalent single-axle loads (ESALs) and resilient moduli (Mr). Considering the four-layer and three-layer pavement designs revealed that the different bitumen-stabilized bases in the four-layer design are not costeffective. Moreover, the PSO design resulted in 22-29 percent cost savings at various ESALs and Mr;however, the three-layer design was 23-31 percent less expensive. (c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/ by-nc-nd/4.0/).
linear programming has had a tremendous impact in the modeling and solution of a great diversity of applied problems, especially in the efficient allocation of resources. As a result, this methodology forms the backbo...
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linear programming has had a tremendous impact in the modeling and solution of a great diversity of applied problems, especially in the efficient allocation of resources. As a result, this methodology forms the backbone of introductory courses in operations research. What students, and others, may not appreciate is that linear programming transcends its linear nomenclature and can be applied to an even wider range of important practical problems. The objective of this article is to present a selection, and just a selection, from this range of problems that at first blush do not seem amenable to linear programming formulation. The exposition focuses on the most basic models in these selected applications, with pointers to more elaborate formulations and extensions. Thus, our intent is to expand the modeling awareness of those first encountering linear programming. In addition, we hope this article will be of interest to those who teach linear programming and to seasoned academics and practitioners, alike.
The aim was to design culturally acceptable and healthy diets with reduced energetic share of ultra-processed foods (UPF%) at no cost increment and to evaluate the impact of the change in the UPF% on diet quality. Foo...
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The aim was to design culturally acceptable and healthy diets with reduced energetic share of ultra-processed foods (UPF%) at no cost increment and to evaluate the impact of the change in the UPF% on diet quality. Food consumption and price data were obtained from the Household Budget Survey (n 55 970 households) and National Dietary Survey (n 32 749 individuals). linear programming models were performed to design diets in which the mean population UPF% was reduced up to 5 % with no cost increment relative to the observed costs. The models were isoenergetic or allowed the energy content to vary according to the UPF%, and they were not constrained to nutritional goals (nutrient-free models) or maximised the compliance with dietary recommendations (nutrient-constrained models). Constraints regarding food preference were introduced in the models to obtain culturally acceptable diets. The mean population UPF% was 23 center dot 8 %. The lowest UPF% attained was approximately 10 %. The optimised diet cost was up to 20 % cheaper than the observed cost, depending on the model and the income level. In the optimised diets, the reduction in the UPF% was followed by an increase in fruits, vegetables, beans, tubers, dairy products, nuts, fibre, K, Mg, vitamin A and vitamin C in the nutrient-constrained models, compared with the observed consumption in the population. There was little variation in most nutrients across the UPF% reduction. The UPF% reduction in the nutrient-free models impacted only trans-fat and added sugar content. UPF% reduction and increase in diet quality are possible at no cost increment.
Optimized allocation of water resources in relatively water-scarce areas is the basis for rational development and utilization of regional water resources and the fundamental guarantee for sus-tainable utilization of ...
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Optimized allocation of water resources in relatively water-scarce areas is the basis for rational development and utilization of regional water resources and the fundamental guarantee for sus-tainable utilization of water resources. In this paper, game theory and linear programming from the aspect of non-engineering measures are used to construct a cooperative game mode (maximizing economic benefits of urban agglomerations in the watershed) and a non-cooperative game mode (maximizing economic benefits of individual cities in the watershed), and it is concluded that the water consumption benefit of the cooperative game mode in the techno garden scenario (ratio-nality of urban agglomerations in the Jiulong River Watershed) is 64.442 billion yuan higher than that of the non-cooperative game (rationality of each city). Moreover, under this scenario, water resource allocation is optimized and the water quality is also significantly improved. Therefore, the cooperative game is the optimum model for the development of urban agglomerations in the watershed, and more attention should be paid to the comprehensive management of water resources, ecological resources, and human activities from the aspect of the river catchment areas given the particularity of water resources.
Homomorphic Encryption (HE) is a set of powerful properties of certain cryptosystems that allow privacy-preserving operation over the encrypted text. Still, HE is not widespread due to limitations in terms of efficien...
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Homomorphic Encryption (HE) is a set of powerful properties of certain cryptosystems that allow privacy-preserving operation over the encrypted text. Still, HE is not widespread due to limitations in terms of efficiency and usability. Among the challenges of HE, scheme parametrization (i.e., the selection of appropriate parameters within the algorithms) is a relevant multi-faced problem. First, the parametrization needs to comply with a set of properties to guarantee the security of the underlying scheme. Second, parametrization requires a deep understanding of the low-level primitives since the parameters have a confronting impact on the scheme's precision, performance, and security. Finally, the circuit to be executed influences, and it is influenced by, the parametrization. Thus, there is no general optimal selection of parameters, and this selection depends on the circuit and the scenario of the application. Currently, most existing HE frameworks require cryptographers to address these considerations manually. It requires a minimum of expertise acquired through a steep learning curve. In this paper, we propose a unified solution for the aforementioned challenges. Concretely, we present an expert system combining Fuzzy Logic and linear programming. The Fuzzy Logic Modules receive a user selection of high-level priorities for the security, efficiency, and performance of the cryptosystem. Based on these preferences, the expert system generates a linear programming Model that obtains optimal combinations of parameters by considering those priorities while preserving a minimum level of security for the cryptosystem. We conduct an extended evaluation showing that an expert system generates optimal parameter selections that maintain user preferences without undergoing the inherent complexity of analyzing the circuit.
Since its inception, fuzzy linear programming (FLP) has proved to be a more powerful tool than classical linear programming to optimize real-life problems dealing with uncertainty. However, the proposed models are par...
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This paper presents a simplified form of dual simplex algorithm for solving linear programming problems with fuzzy and neutrosophic numbers which supplies some great benefits over phase 1 of traditional dual simplex a...
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Conflict-driven pseudo-Boolean solvers optimize 0-1 integer linear programs by extending the conflict-driven clause learning (CDCL) paradigm from SAT solving. Though pseudo-Boolean solvers have the potential to be exp...
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Conflict-driven pseudo-Boolean solvers optimize 0-1 integer linear programs by extending the conflict-driven clause learning (CDCL) paradigm from SAT solving. Though pseudo-Boolean solvers have the potential to be exponentially more efficient than CDCL solvers in theory, in practice they can sometimes get hopelessly stuck even when the linear programming (LP) relaxation is infeasible over the reals. Inspired by mixed integer programming (MIP), we address this problem by interleaving incremental LP solving with cut generation within the conflict-driven pseudo-Boolean search. This hybrid approach, which for the first time combines MIP techniques with full-blown conflict analysis operating directly on linear inequalities using the cutting planes method, significantly improves performance on a wide range of benchmarks, approaching a "best-of-both-worlds" scenario between SAT-style conflict-driven search and MIP-style branch-and-cut.
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