The growing demand for flexible production systems is driven by product diversity and fluctuating order volumes. Seasonal variations can lead to imbalances between available machines and order demands, making efficien...
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The growing demand for flexible production systems is driven by product diversity and fluctuating order volumes. Seasonal variations can lead to imbalances between available machines and order demands, making efficient resource configuration critical before production begins. This paper addresses the optimization of machine configuration and scheduling in the hybrid flow shop, incorporating the order delivery time window. Most previous studies have focused on fixed machine numbers, while this study considers uncertain machine availability. This paper presents the mixed-integer linearprogramming model of the problem, and a linearprogramming (LP)-driven variable strategies evolutionary approach. The proposed approach combines an evolutionary algorithm with LP-driven neighborhood search for sequence optimization. Three strategies are constructed by narrowing down the search scope, which effectively reduces the algorithm's stagnation time and speeds up the convergence. To evaluate the effectiveness of the approach, the scales of application of the three LP strategies are first tested. Then ablation and comparison experiments are conducted, which show that the proposed approach generally agrees with the MILP results on small-scale problems and with smaller time resources. Experiments on 60 sets of large-scale problems show that the proposed approach has significant advantages over 5 state-of-the-art evolutionary algorithms and the MILP model. Additionally, the experiments show that the LPdriven strategy can improve the algorithm efficiency by about 13.32 % compared with the conventional strategy. These results demonstrate the potential of the LP-driven evolutionary approach for solving the complex scheduling problem.
In this study, the problem of optimal synthesis of pre-equalisation factors for a multi-carrier code division multiple-access wireless network is studied under different quality of service (QoS) restrictions and perfo...
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In this study, the problem of optimal synthesis of pre-equalisation factors for a multi-carrier code division multiple-access wireless network is studied under different quality of service (QoS) restrictions and performance objectives. The downlink is particularly addressed, where the QoS is evaluated by the signal-to-interference-noise ratio (SINR) after signal detection by matched filtering. First, the minimum energy formulation under inequality QoS constraints is analysed, and it was concluded that the optimal performance is always achieved at the lower bounds. Next, the minimisation of the peak magnitude of the pre-equalisation factors was addressed by an optimal synthesis scheme through the infinity norm, where a linear programming strategy is proposed by using an upper bound. Finally, by combining the minimum energy and minimum peak strategies, the minimisation of an upper bound on the peak-to-average-power ratio (PAPR) is suggested, which can be solved through quadratic programming by incorporating the QoS constraints. The new synthesis schemes take into account two important transmitter-based properties: peak magnitude and PAPR. These ideas were extensively validated in simulation, where the synthesis schemes were compared by Monte Carlo tests at different load and noise levels, and objective SINRs.
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