With the increasing penetration of renewable energy sources,transmission maintenance scheduling(TMS)will have a larger impact on the accommodation of wind ***,the more flexible transmission network topology owing to t...
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With the increasing penetration of renewable energy sources,transmission maintenance scheduling(TMS)will have a larger impact on the accommodation of wind ***,the more flexible transmission network topology owing to the network topology optimization(NTO)technique can ensure the secure and economic operation of power *** paper proposes a TMS model considering NTO to decrease the wind curtailment without adding control *** problem is formulated as a two-stage stochastic mixed-integerprogramming *** first stage arranges the maintenance periods of transmission *** second stage optimizes the transmission network topology to minimize the maintenance cost and system operation in different wind speed *** proposed model cannot be solved efficiently with off-theshelf solvers due to the binary variables in both ***,the progressive hedging algorithm is *** results on the modified IEEE RTS-79 system show that the proposed method can reduce the negative impact of transmission maintenance on wind accommodation by 65.49%,which proves its effectiveness.
This paper focuses on the state estimation of a specific class of state-intermittent dynamical systems evolving in a high-dimensional state-space. As the dimension is large, a classical estimator such as a Kalman filt...
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ISBN:
(纸本)9798350373981;9798350373974
This paper focuses on the state estimation of a specific class of state-intermittent dynamical systems evolving in a high-dimensional state-space. As the dimension is large, a classical estimator such as a Kalman filter would require a large observation horizon to converge. To overcome this limitation, a mixed-integer formulation is proposed and solved by using a Genetic Algorithm. We also propose a reduced-order state estimation as a variant of this approach under some mild assumptions, which reduces the observation window size even more. In particular, we show, through a case study in finance, that the reduced model and its associated genetic algorithm can reduce the observation window size needed by almost 75% and that this method is faster than the non-reduced mixedinteger optimization problem. We also show that the reduced-order state estimation approach can be used to discover a mutual fund portfolio composition at each date of a given period.
The railway timetable rescheduling problem is regarded as an efficient way to handle disturbances. Typically, it is tackled using a mixedintegerlinearprogramming (MILP) formulation. In this paper, an algorithm that...
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The railway timetable rescheduling problem is regarded as an efficient way to handle disturbances. Typically, it is tackled using a mixedintegerlinearprogramming (MILP) formulation. In this paper, an algorithm that combines both reinforcement learning and optimization is proposed to solve the railway timetable rescheduling problem. Specifically, a value-based reinforcement learning algorithm is implemented to determine the independent integer variables of the MILP problem. Then, the values of all the integer variables can be derived from these independent integer variables. With the solution for the integer variables, the MILP problem can be transformed into a linearprogramming problem, which can be solved efficiently. The simulation results show that the proposed method can reduce passenger delays compared with the baseline, while also reducing the solution time. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)
Assembly line balancing usually presupposes binary task-station assignments. Some authors have previously described efficiency increases due to fractional task allocations or work-sharing. However, the internal storag...
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Assembly line balancing usually presupposes binary task-station assignments. Some authors have previously described efficiency increases due to fractional task allocations or work-sharing. However, the internal storage requirements for such efficiency increases have not been analytically described. This paper defines the Fractional Allocation Assembly Line Balancing Problem and presents mixed-integer linear programming models to bridge that gap. The main opportunity afforded by the studied flexibility is increased throughput, which is associated to higher internal storage costs. Worst-case analyses define mathematical expressions for these costs both for paced (line length) and unpaced lines (buffers). A screening on a 1050-instance dataset is conducted. Results suggest that fractional allocations can often allow better resource utilisation with relatively low costs: the higher space requirement costs are often one-time investments, while lower cycle time represents fundamentally continuous gains. Lastly, the proposed formulation was adapted and applied to industrial data. This mixed-model assembly line case study suggests that fractional allocations can also lead to more robust balancing regarding demand uncertainty.
As regards distributed hybrid flow shop scheduling with sequence-dependent setup times (DHFSP-SDST), three novel mixed-integer linear programming (MILP) models and a constraint programming (CP) model are formulated fo...
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As regards distributed hybrid flow shop scheduling with sequence-dependent setup times (DHFSP-SDST), three novel mixed-integer linear programming (MILP) models and a constraint programming (CP) model are formulated for the same-factory and different-factory environments. The three novel MILP models are based on two different modeling ideas. The existing MILP model and the three proposed MILP models are compared in detail from several aspects, such as binary decision variables, continuous decision variables, constraints, solution performance and solution time. By solving the benchmarks in existing studies, the effectiveness and superiority of the proposed MILP and CP models are proved. Experimental results show that the MILP model of sequence-based modeling idea performs best, the MILP model of adjacent sequence-based modeling idea takes the second place and the existing MILP model of position-based modeling idea performs worst. The CP model is more efficient and effective than MILP models. In addition, compared with the existing meta-heuristic algorithms (e.g., DABC and IABC), the proposed MILP models prove the optimal solutions of 37 instances and improve 17 current best solutions. The CP model solves all the 45 instances to optimality and improves 19 current best solutions for benchmarks in the existing studies
As technologies advance rapidly, the urgency of remanufacturing is escalating. Efficient disassembly processes are crucial at the outset of remanufacturing. This work proposes an innovative disassembly scheme aimed at...
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ISBN:
(纸本)9798350358513;9798350358520
As technologies advance rapidly, the urgency of remanufacturing is escalating. Efficient disassembly processes are crucial at the outset of remanufacturing. This work proposes an innovative disassembly scheme aimed at amplifying efficiency through the utilization of parallelization and human-robot collaboration in the context of a hybrid disassembly line. A single-objective mixed-integerprogramming model is developed to optimize disassembly profit. A discrete salp swarm algorithm is proposed to solve it since it is NP-hard. We have proposed and integrated uniform variation and two-point crossover operators in this algorithm. After comparing its results with those of the exact solver and other intelligent optimization methods, we conclude its competitive performance in both solution quality and efficiency.
This paper introduces a novel approach to optimizing the layout and deployment of mobile shelter hospitals, a concept that has gained prominence due to the increasing need for rapid medical response in diverse scenari...
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This paper presents an approach to joint wireless and computing resource management in slice-enabled metaverse networks, addressing the challenges of inter-slice and intra-slice resource allocation in the presence of ...
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The compactness of routes in distribution planning is a criterion that has been under explored in logistics literature, despite its notable impact on the practical implementation of routing plans, such as in solid was...
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This paper presents a new method for optimal selection of conductor's sizes in radial distribution networks using mixedinteger quadratic programming technique. The objective function to be minimized is the capita...
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