Given a role-based access control (RBAC), resiliency checking problem (RCP) aims at determining whether every permission is executed by a user and all authorization constraints are satisfied when some users become abs...
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Given a role-based access control (RBAC), resiliency checking problem (RCP) aims at determining whether every permission is executed by a user and all authorization constraints are satisfied when some users become absent. Although the problem is computationally hard, desirable solutions are still expected so as to guarantee the continuity of access control. In this article, we solve RCP for RBAC based on constraint enforcement and mathematical programming. We use Petri nets (PNs) to formalize RBAC. It is shown that each separation of duty constraint imposed on a PN modeling of RBAC can be enforced by a maximally permissive PN-based control structure. After implementing such control structure on the PN modeling of RBAC, we can obtain an admissible RBAC. We show that RCP of RBAC can be transformed into another problem, which determines whether each permission can be executed by a user in the admissible RBAC against the absence of some users. An integer linear programming-based approach is presented to accomplish such verification. The comparison between our approach and the existing one is given to illustrate the effectiveness and efficiency of ours.
This work presents a mathematical programming model for the optimal decision making on the supply chain of lithium and lithium compounds in a macroscopic system. The model considers the exploitation of the economicall...
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This work presents a mathematical programming model for the optimal decision making on the supply chain of lithium and lithium compounds in a macroscopic system. The model considers the exploitation of the economically feasible natural sources;it also considers the processing and purification stages as well as the transportation costs needed to satisfy the demand of the several lithium applications, such as Li-ion batteries. Given the significance of circular economy, a discussion on the remanufacturing and recycling of the battery components is also included. Currently, there is no exploitation and production of lithium in Mexico;however, since Mexico is the 7th vehicle manufacturer in the world, information from the potential Lithium deposits in Mexico and its corresponding demands are used to explore the potential and the techno-economic decision making required within such a supply chain.
The method of Almon reduces multicollinearity in some degree in distributed lag model, however multicollinearity may not be recovered since Almon estimator depends on the use of ordinary least squares technique. In th...
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The method of Almon reduces multicollinearity in some degree in distributed lag model, however multicollinearity may not be recovered since Almon estimator depends on the use of ordinary least squares technique. In this context, Almon ridge estimator including one biasing parameter is commonly preferred in this model. Based on recent advances, biased estimators that have more than one biasing parameter are stated as advantageous to one biasing parameter estimators. One of two-parameter estimators is Almon two-parameter ridge estimator of ozbay (Iran J Sci Tech Trans Sci 43: 1819-1828, 2019) which regulates the multicollinearity with its first biasing parameter and improves the quality of fit of regression with its second biasing parameter. As for another method to eliminate multicollinearity, exact linear restrictions are employed for the Almon two-parameter ridge estimator and restricted Almon two-parameter ridge estimator was introduced by ozbay and Toker (Considering linear constraints for Almon two parameter ridge estimation. 11th International Statistics Congress (ISC 2019), Mugla, Turkey, 2019). In this article, the issue of selecting the biasing parameters of the restricted and unrestricted Almon two-parameter ridge estimators is handled with the approach of mathematical programming instead of traditional selection methods. Different scenarios in which mean square error is minimized or coefficient of multiple determination is maximized are constituted by this mathematical programming approach. In real-life data analysis, we focus on global warming as a trend topic to demonstrate the effect of mathematical programming approach on the mentioned estimators. The dataset in question comprises carbon dioxide emission that has adverse effects on global warming via increasing average global temperature.
This work proposes a mathematical programming (MP) representation of discrete event simulation of timed Petri nets (TPN). Currently, mathematical programming techniques are not widely applied to optimize discrete even...
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This work proposes a mathematical programming (MP) representation of discrete event simulation of timed Petri nets (TPN). Currently, mathematical programming techniques are not widely applied to optimize discrete event systems due to the difficulty of formulating models capable to correctly represent the system dynamics. This work connects the two fruitful research fields, i.e., mathematical programming and Timed Petri Nets. In the MP formalism, the decision variables of the model correspond to the transition firing times and the markings of the TPN, whereas the constraints represent the state transition logic and temporal sequences among events. The MP model and a simulation run of the TPN are then totally equivalent, and this equivalence has been validated through an application in the queuing network field. Using a TPN model as input, the MP model can be routinely generated and used as a white box for further tasks such as sensitivity analysis, cut generation in optimization procedures, and proof of formal properties.
This article is devoted to the study of mathematical programming problems with vanishing constraints on Hadamard manifolds (in short, MPVC-HM). We present the Abadie constraint qualification (in short, ACQ) and (MPVC-...
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This article is devoted to the study of mathematical programming problems with vanishing constraints on Hadamard manifolds (in short, MPVC-HM). We present the Abadie constraint qualification (in short, ACQ) and (MPVC-HM)-tailored ACQ for MPVC-HM and provide some necessary conditions for the satisfaction of ACQ for MPVC-HM. Moreover, we demonstrate that the Guignard constraint qualification (in short, GCQ) is satisfied for MPVC-HM under certain mild restrictions. We introduce several (MPVC-HM)-tailored constraint qualifications in the framework of Hadamard manifolds that ensure satisfaction of GCQ. Moreover, we refine our analysis and present some modified sufficient conditions which guarantee that GCQ is satisfied. Several non-trivial examples are incorporated to illustrate the significance of the derived results. To the best of our knowledge, constraint qualifications for mathematical programming problems with vanishing constraints in manifold setting have not been explored before.
Production scheduling in underground mines is one of the most important and complex processes in maximising the economic value of an operation over the life of the mine. Due to increasing energy, transportation, proce...
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Production scheduling in underground mines is one of the most important and complex processes in maximising the economic value of an operation over the life of the mine. Due to increasing energy, transportation, processing and smelting costs and reducing water availability, the extraction of ore deposits through underground operations has become more expensive and challenging. A potential productive and efficient solution to mitigate these issues is to integrate pre-concentration systems into the existing mining system to increase ore grades through a preliminary process of rejecting some waste before sending the ore to the energy intensive mineral processing plant. Further benefit of such systems includes the utilisation of the waste and low-grade material in backfilling of open stopes to reduce the transportation costs and surface environmental impact such as reduction of CO2 emissions and waste transportation. The objective of this paper is to present a new mathematical model to optimise the production schedule of underground metalliferous mining operations in the presence of a pre-concentration system. The proposed model optimises the production schedule and sequencing of stopes within a sublevel stoping operation. The results of this study illustrate the potential economic benefits of using a pre-concentration system prior to a mineral processing operation as a result of increased net present value.
Decreasing greenhouse gas emissions plays a crucial role in the Europe energy transition and the production of hydrogen using electricity from renewable sources (green H2) con-tributes significantly to this objective....
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Decreasing greenhouse gas emissions plays a crucial role in the Europe energy transition and the production of hydrogen using electricity from renewable sources (green H2) con-tributes significantly to this objective. As the renewable energy production capacity grows, the impact of green H2 is likely to increase. In the case of the Iberian Electricity Market (MIBEL), no quantitative studies have so far tested the viability of the H2 growth plans. This paper fills this gap through a novel mathematical programming model, which integrates H2 generation into the MIBEL. The model reveals a mismatch between the sustainability goals and the expansion plans of renewable and green H2 for Spain and Portugal.(c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Flexible Process Planning (FPP) is one of the key intelligent manufacturing techniques. The FPP problem is exactly and concisely formulated using 0-1 mathematical programming. Compared with the existing models, the ne...
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Flexible Process Planning (FPP) is one of the key intelligent manufacturing techniques. The FPP problem is exactly and concisely formulated using 0-1 mathematical programming. Compared with the existing models, the new formulation simultaneously considers alternative operation selection and sequencing and operational method assignment under two optimization criteria. The new formulation does not need to plot the common AND/OR-network that often depicts partial possible processing routes. Distinctively, the important operational precedence constraint is beforehand transformed into the possible successor set of each operation and the possible immediate successor set. Three methods are creatively proposed to prohibit from generating a cycle in sequencing. The complicated criteria involving the machine, tool and setup changeover identification are linearly expressed. The experimental results indicate that the proposed 0-1 linear programming models are able to quickly obtain the optimal solution of the small-scale problems and stably find a satisfactory solution of the large-scale problems within acceptable time. Compared with the existing mathematical programming models for process planning, the proposed linear models have lower complexity and better performance in solving benchmark instances. In two groups of comparative experiments, the number of constraints of the proposed linear models dramatically reduces by 99.6% and 70%, respectively. Moreover, all benchmark instances are exactly solved by the Cplex solver using the proposed linear models within one hour.& COPY;2022 Elsevier B.V. All rights reserved.
Operations optimization in an organic Rankine cycle (ORC) based combined system is important while computationally difficult by using mechanistic models due to complex nonlinearities and constraints. In this study, a ...
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Operations optimization in an organic Rankine cycle (ORC) based combined system is important while computationally difficult by using mechanistic models due to complex nonlinearities and constraints. In this study, a hybrid framework integrating machine learning and mathematical programming has been proposed to optimize the operations of the system for the best exergy performance. The combined system is first decomposed into two single ORCs for reducing computational complexity. Classification models and regression models based on artificial neural network (ANN) and linear regression are developed using simulation data, where classifications can be employed for high-throughput screening feasible inputs which meet the mechanistic constraints in ORC. The results demonstrate high performances of machine learning with at least 99% accuracies for classifications and with mean relative errors of less than 1% for regressions. These data-driven models and the relation of two ORCs were then embedded with mathematical programming for optimization and maximum net exergy of 28.66 MW is obtained. By linear expansion of ReLU operators in ANN, mixed-integer linear programming (MILP) based on machine learning models achieve high efficiency with similar to 0.1 s required for optimization compared to mixed-integer nonlinear programming (MINLP) (>1000 s) and heuristic optimization based on mechanistic models (>10 h).
This paper considers the hybrid flow shop scheduling problem, where jobs are processed in $m$ stages with the same route of the stage. Each stage has identical parallel machines for processing jobs. Some mathematical ...
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This paper considers the hybrid flow shop scheduling problem, where jobs are processed in $m$ stages with the same route of the stage. Each stage has identical parallel machines for processing jobs. Some mathematical programming formulations and lower bound calculations have been proposed in the literature for such cases. Nevertheless, there is a lack of complete comparisons of these mathematical programming formulations and lower bounds in the hybrid flow shop literature. This paper proposes a new mixed integer programming model and two new lower bounds based on the bin-packing concept for the considered problem. To evaluate the proposed model, two sets of small and small-to-medium problems are used to compare our model with the existing models. Moreover, two propositions are discussed for lower bounds. The experimental results show that the proposed mixed integer programming model efficiently found optimal solutions because it needs a smaller number of binary variables and a smaller number of constraints, and the proposed lower bound can also serve as a strong indicator to evaluate the distances between the solutions obtained by heuristic algorithms and the optimal solution.
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