Previous works have shown that the best topology of an electrical network depends on the total demand and its distribution. Optimal Transmission Switching (OTS) identifies lines to be opened to minimize operating cost...
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Previous works have shown that the best topology of an electrical network depends on the total demand and its distribution. Optimal Transmission Switching (OTS) identifies lines to be opened to minimize operating costs. However, this cost-based criterion is not suitable for hydropower generation-based companies, where hydrology largely dictates the power generation. Moreover, no study about impact of OTS on voltage stability margins have been conducted in literature as it never been explicitly integrated in the problem formulation. Thus, OTS does not guarantee that proposed topologies are secure even if voltage bounds are respected. It also does not take full advantage of the real networks flexibility by including only line states and production distribution in the control vector. This leads to algorithms that are not suitable for weakly meshed (or mostly radial) networks. In this paper, we propose an extended OTS (E-OTS) that includes several improvements over current algorithms and overcomes the above-mentioned problems. We also propose two heuristics to reduce resolution time. Results on the PEGASE (1354 bus) and Polish (2236 bus) networks show that E-OTS identifies a topology improvement in less than 40 minutes, which is low enough to be implemented in a real energy management system. Tests have been made for three different criteria.
While a range of models have been proposed for the multiperiod blend scheduling problem (MBSP), solving even medium-size MBSP instances remains challenging due to the presence of bilinear terms and binary variables. T...
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While a range of models have been proposed for the multiperiod blend scheduling problem (MBSP), solving even medium-size MBSP instances remains challenging due to the presence of bilinear terms and binary variables. To address this challenge, we develop solution methods for MBSP focusing on the cost minimization objective. We develop a novel preprocessing algorithm to calculate lower bounds on stream flows. We define product dedicated flow variables to address product specific features involved in MBSP. Bounds on stream flows and new product dedicated flow variables are then used to generate tightening constraints which significantly improve the solution time of the mixed integer nonlinear programming models as well as models based on linear approximations.
To find the optimal sailing frequency and optimal ship type of container liner operation under low carbon environment, a mixed integer nonlinear programming model is proposed, in which the ship load capacity constrain...
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To find the optimal sailing frequency and optimal ship type of container liner operation under low carbon environment, a mixed integer nonlinear programming model is proposed, in which the ship load capacity constraint is considered. The objective of the proposed model is to minimize voyage total cost consisting of carbon dioxide emissions cost, voyage shipping cost(fuel costs, port charges, operating costs) as well as shipper's inventory cost in a certain operation period. It is shown from theoretical analysis that the proposed model can be solved by Kuhn-Tucker condition and Enumeration method. A numerical example is given to demonstrate the effectiveness of the proposed model, and analyze the impacts of carbon tax levels, fuel prices, ship size and the sailing frequency on total cost and environmental cost. The results show that as the size of the ship increases, the ship total cost decreases first and then increases, the percentage of carbon emission cost in total ship cost increases. As the sailing frequency increases, the ship total cost decreases first and then increases, the percentage of carbon emission cost in total ship cost decreases. The results indicate that the shipping companies can improve economic performance and environment performance simultaneously by employing reasonable sailing frequency and ship type decisions.(C) 2022 Elsevier B.V. All rights reserved.
Resource allocation in wireless networks, such as device-to-device (D2D) communications, is usually formulated as mixed integer nonlinear programming (MINLP) problems, which are generally NP-hard and difficult to get ...
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Resource allocation in wireless networks, such as device-to-device (D2D) communications, is usually formulated as mixed integer nonlinear programming (MINLP) problems, which are generally NP-hard and difficult to get the optimal solutions. Traditional methods to solve these MINLP problems are all based on mathematical optimization techniques, such as the branch-and-bound (B&B) algorithm that converges slowly and has forbidding complexity for real-time implementation. Therefore, machine leaning (ML) has been used recently to address the MINLP problems in wireless communications. In this paper, we use imitation learning method to accelerate the B&B algorithm. With invariant problem-independent features and appropriate problem-dependent feature selection for D2D communications, a good auxiliary prune policy can be learned in a supervised manner to speed up the most time-consuming branch process of the B&B algorithm. Moreover, we develop a mixed training strategy to further reinforce the generalization ability and a deep neural network (DNN) with a novel loss function to achieve better dynamic control over optimality and computational complexity. Extensive simulation demonstrates that the proposed method can achieve good optimality and reduce computational complexity simultaneously.
This paper presents a new All-In-One (AIO) implementation of an existing formulation to design adaptive structures through Total Energy Optimization (TEO). The method implemented in previous work is a nested optimizat...
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This paper presents a new All-In-One (AIO) implementation of an existing formulation to design adaptive structures through Total Energy Optimization (TEO). The method implemented in previous work is a nested optimization process, here named TEO-Nested. Numerical simulations and experimental testing have shown that the TEO-Nested method produces structures that embody and use significantly lower energy compared to passive designs. However, TEO-Nested does not guarantee solution optimality. The formulation presented in this paper is an ADD optimization based on mixed integer nonlinear programming (MINLP), here named TEO-MINLP. Element cross-section areas, internal forces, nodal displacements and control commands are treated as continuous variables while the actuator positions as binary variables. Stress and displacement limits are included in the optimization constraints. Case studies of reticular structures are employed to benchmark the solutions with those produced by the TEO-Nested method. Results have shown that both formulations produce similar solutions which are only marginally different in energy terms thus proving that the TEO-Nested method tends to converge to optimal (local) solutions. However, the computation time required by TEO-Nested is only a fraction of that required by TEO-MINLP, which makes the former more suitable for structures of complex layout that are made of many elements. (C) 2020 Elsevier Ltd. All rights reserved.
The continuous data-flow application in the IoT integrates the functions of fog, edge, and cloud computing. Its typical paradigm is the E-Health system. Like other IoT applications, the energy consumption optimization...
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The continuous data-flow application in the IoT integrates the functions of fog, edge, and cloud computing. Its typical paradigm is the E-Health system. Like other IoT applications, the energy consumption optimization of IoT devices in continuous data-flow applications is a challenging problem. Since the anomalous nodes in the network will cause the increase of energy consumption, it is necessary to make continuous data flows bypass these nodes as much as possible. At present, the existing research work related to the performance of continuous data-flow is often optimized from system architecture design and deployment. In this paper, a mathematical programming method is proposed for the first time to optimize the runtime performance of continuous data flow applications. A lightweight anomaly detection method is proposed to evaluate the reliability of nodes. Then the node reliability is input into the optimization algorithm to estimate the task latency. The latency-aware energy consumption optimization for continuous data-flow is modeled as a mixed integer nonlinear programming problem. A block coordinate descend-based max-flow algorithm is proposed to solve this problem. Based on the real-life datasets, the numerical simulation is carried out. The simulation results show that the proposed strategy has better performance than the benchmark strategy.
Upcoming active space debris removal missions will most likely attempt to remove several objects per mission. The design of such missions involves the selection of the objects to be removed, as well as the optimisatio...
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Upcoming active space debris removal missions will most likely attempt to remove several objects per mission. The design of such missions involves the selection of the objects to be removed, as well as the optimisation of the visit sequence and the orbital transfers interconnecting them. In this work a branch-and-bound-based algorithm is presented for the preliminary design of multi-target space debris removal missions. The proposed algorithm comprises two different levels. The upper level, modelled as an integer Linear programming problem, deals with the combinatorial complexity of the problem. The lower level, modelled as a mixed integer nonlinear programming problem, encapsulates the orbital dynamics. Throughout the problem resolution, the upper level selects promising subsets of a pool of candidate objects of space debris, so that a removed threat value is maximised. Each of these subsets is passed through to the lower level, which ensures that there is a feasible trajectory that allows to rendezvous, in a specific sequence, with each and every object in the subset, while prescribed mission duration and v constraints are fulfilled. This framework is able to exploit the structure of the problem so that instances with large pools of candidate objects can be efficiently solved while achieving the certificates of optimality that branch-and-bound methods provide.
We present an overview of the main algorithms in the literature for convex mixed integer nonlinear programming and discuss aspects of their implementation in a new open source computational package called Muriqui Opti...
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We present an overview of the main algorithms in the literature for convex mixed integer nonlinear programming and discuss aspects of their implementation in a new open source computational package called Muriqui Optimizer. We provide extensive computational results comparing the implementations of all approaches considered on a set of 343 benchmark test problems. Finally, we present to the technical and scientific community the new software Muriqui Optimizer.
In industry, the durations of reliability experiments are usually fixed in advanced, which could lead to suboptimal results. A constrained optimization problem is stated in order to determine the best inspection time ...
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In industry, the durations of reliability experiments are usually fixed in advanced, which could lead to suboptimal results. A constrained optimization problem is stated in order to determine the best inspection time for reliability testing. The decision criterion is based on Weibull failure counts, whereas the constraints are related to the reliability and risk levels imposed by the producer and the consumer. The optimal reliability sampling plan provides the best strategy to determine the acceptability of lots and production processes. Minimum-cost inspection times, as well as the required number of test units and the maximum number of failures allowed, are derived by solving mixed integer nonlinear programming problems. An approximation of the minimum feasible acceptance number is first provided in closed-form. An efficient step-by-step procedure is then proposed in order to find the optimal reliability test plan for lot sentencing. In most cases, only a few iterations are needed to reach the optimal solution. For illustrative purposes, the suggested methodology is applied to the manufacturing of turbine engine combustors, systems of components and lawn mower motors.
Tensegrity structures have been widely utilized as lightweight structures due to their high stiffness-to-mass and strength-to-mass ratios. Minimal mass design of tensegrity structures subject to external loads and spe...
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Tensegrity structures have been widely utilized as lightweight structures due to their high stiffness-to-mass and strength-to-mass ratios. Minimal mass design of tensegrity structures subject to external loads and specific constraints (e.g., member yielding and buckling) has been intensively studied. However, all the existing studies focus on passive tensegrity structures, i.e., the structural members cannot change their lengths actively and the structure has to passively resist external loads. An active tensegrity structure equipped with actuators can actively adapt its internal forces and nodal positions and thus can actively resist external loads. Therefore, it is expected that active tensegrity structures use less material compared to passive tensegrity structures thus leading to a smaller mass. Due to the integration of the active control system, the design of active tensegrity structures is different from passive tensegrity structures. This study proposes a general approach for the design of minimal mass active tensegrity structures based on a mixedintegerprogramming scheme, in which the member crosssectional areas, prestress, actuator layout and control strategies (i.e., actuator length changes) are designed simultaneously. The member cross-sectional areas, prestress level, and actuator control strategies are treated as continuous variables and the actuator layout is treated as a binary variable. The equilibrium condition, member yielding, cable slackness, strut buckling, and the limitations on the nodal displacements as well as other practical requirements are formulated as constraints. Three typical active tensegrity structures are designed through the proposed approach and the results are benchmarked with the equivalent minimal mass passive designs. It is illustrated that the active designs can significantly decrease the material consumption compared with the equivalent passive designs thus leading to more lightweight tensegrity structures.
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