For solving the singularity problem arising in the control of manipulators, an efficient way is to maximize its manipulability. However, it is challenging to optimize manipulability effectively ...because it is a nonconvex function to the joint angles of a robotic arm. In addition, the involvement of an inversion operation in the expression of manipulability makes it even hard for timely optimization due to the intensively computational burden for matrix inversion. In this paper, we make progress on real-time manipulability optimization by establishing a dynamic neural network for recurrent calculation of manipulability-maximal control actions for redundant manipulators under physical constraints in an inverse-free manner. By expressing position tracking and matrix inversion as equality constraints, physical limits as inequality constraints, and velocity-level manipulability measure, which is affine to the joint velocities, as the objective function, the manipulability optimization scheme is further formulated as a constrained quadratic program. Then, a dynamic neural network with rigorously provable convergence is constructed to solve such a problem online. Computer simulations are conducted and show that, compared to the existing methods, the proposed scheme can raise the manipulability almost 40% on average, which substantiates the efficacy, accuracy, and superiority of the proposed manipulability optimization scheme.
•Surrogate models embedded within Real-Time Optimization (RTO) framework.•A trust region filter (TRF) optimization strategy is applied.•TRF integrates with an FCC truth model and the Aspen RTO ...optimizer.•Demonstrated on three real-world scenarios.
Since its initial development in the 1980′s, Real-Time Optimization (RTO) has been widely appreciated as an efficient way to optimize process decision variables and improve economic performance of refineries. RTOs consist of nonlinear optimization models with hundreds of thousands of equations, which are built within equation-oriented (EO) modeling platforms. With increasing size and complexity of RTO applications, there is increased demand for improved optimization strategies. To address this demand, surrogate models for complex refinery units have been embedded within the general EO framework for RTO. Moreover, the recent trust region filter (TRF) optimization strategy allows great flexibility in the choice of surrogates, while ensuring convergence to the optimum of the rigorous RTO model. This study considers this approach for a real-world refinery. The Petrobras S.A. RECAP unit in Mauá, Brazil runs an RTO refinery model with an Aspen RTO optimizer to maximize the profit within two hour cycles. To reduce the computational burden, we embed a reduced model (RM) to replace the detailed (truth) model for the residue fluid catalytic cracking (RFCC) unit, and implement a TRF optimization strategy. The TRF driver is written in Python and integrates with the RFCC truth model, the Aspen-EO RECAP model, and the Aspen RTO optimizer. The approach is illustrated on three real-world scenarios in order to demonstrate the effectiveness and efficiency of this RM-based optimization strategy.
We investigate the train timetabling problem in suburban rail transit lines by considering (1) the traditional stopping mode (TSM), in which all trains stop at each station, and (2) the express/local ...stopping mode (ELM), in which express trains can skip certain low–demand stations. We first propose two mixed–integer linear programming models for the train timetabling problem under the TSM with and without capacity constraints. Next, we develop two mixed–integer nonlinear programming models under the ELM with and without “overtaking”; thus, a total of four optimization models are proposed. The objective is to minimize the passenger travel time (PTT). Owing to the NP–hardness of the studied problem, we propose an adaptive genetic algorithm (A–GA) that can efficiently solve the four proposed models. The A–GA is customized to solve the train timetabling problem with train capacity, overtaking, and other operational constraints, reducing the PTT. To evaluate the performance of the proposed algorithm, we conduct numerical experiments on 60 randomly generated realistic instances and a real–world case study based on Shanghai Metro Line 16. The computational results for the realistic instances indicate that our A–GA can obtain near–optimal solutions with significantly less computation time than an established commercial solver. The computational results from the real-world case study quantify the benefits of considering the combination of the ELM and overtaking strategies in train timetabling. Furthermore, we perform a sensitivity analysis on key parameters of our mathematical formulations. The results provide insights to railway managers on how to set key parameters when applying the proposed formulations and solution methodology in practice.
•We propose mixed–integer programming formulations for train timetabling in suburban transit lines.•We consider passengers being left behind under limited train capacity and express/local stopping mode.•We accurately calculate passenger waiting times under oversaturated traffic conditions.•We present an adaptive genetic algorithm for optimizing train timetables with overtaking possibility.•The proposed algorithm yields good quality solutions in a short computation time.
The key of reactive power planning (RPP), or Var planning, is the optimal allocation of reactive power sources considering location and size. Traditionally, the locations for placing new Var sources ...were either simply estimated or directly assumed. Recent research works have presented some rigorous optimization-based methods in RPP. This paper will first review various objectives of RPP. The objectives may consider many cost functions such as variable Var cost, fixed Var cost, real power losses, and fuel cost. Also considered may be the deviation of a given voltage schedule, voltage stability margin, or even a combination of different objectives as a multi-objective model. Secondly, different constraints in RPP are discussed. These different constraints are the key of various optimization models, identified as optimal power flow (OPF) model, security-constrained OPF (SCOPF) model, and SCOPF with voltage-stability consideration. Thirdly, the optimization-based models will be categorized as conventional algorithms, intelligent searches, and fuzzy set applications. The conventional algorithms include linear programming, nonlinear programming, mixed-integer nonlinear programming, etc. The intelligent searches include simulated annealing, evolutionary algorithms, and tabu search. The fuzzy set applications in RPP address the uncertainties in objectives and constraints. Finally, this paper will conclude the discussion with a summary matrix for different objectives, models, and algorithms.
•A new optimization model is developed for scheduling projects under uncertain activity costs.•Our approached provides a vehicle to obtain exact solutions to the addressed problem.•A hybrid heuristic ...and genetic algorithm solves medium to large instances efficiently.•Project deadline, budget and variation of cost significantly impact optimal solutions.
The multi-mode resource-constrained project scheduling problem under uncertain activity cost (MRCPSP-UAC) has a wide range of applications in production planning and project management. We first build a new mixed-integer nonlinear programming (MINLP) model with the objective of minimizing the risk of project cost overrun, which provides a vehicle to obtain optimal solutions. To overcome the computational challenge of exact method for solving large instances, we devise a construction heuristic (CH) with a multi-pass greedy improvement procedure to obtain a feasible solution efficiently. To further improve solution quality, a hybrid CH and genetic algorithm (CH-GA) is developed with a custom fitness function to properly calibrate the quality of an individual. A comprehensive computational study is performed to examine the impact of various problem parameters on the optimal solutions, and the performance of our algorithms. Our hybrid CH-GA performs well for large instances with significantly less computational time than the exact method.
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•Multiple properties of steel were predicted by nonlinear programming-based model.•One model could predict multiple properties simultaneously.•The potential boundaries of three ...properties were calculated.
Extracting the valuable information about the connections between the overall properties and the related factors from the industrial big data of materials is of significant interest to the materials engineering. At present, most data-driven approaches focus on building a relation model for a single property of the materials, where it may ignore the restrictive boundaries of other properties. In this paper, we propose a machine-learning-based method using nonlinear programming for multiple properties of the materials, and solve the problem by using the Interior Point Algorithm. The key idea is to take the mapping functions corresponding to the properties of the materials as the constraints of the nonlinear programming problem, thus it is capable of processing the restrictions of these properties. Moreover, with our method, the possible boundaries of these properties under certain conditions can be calculated. Experiments results on steel production data demonstrate the rationality and reliability of the proposed method.
Cooperation between the fog and the cloud in mobile cloud computing environments could offer improved offloading services to smart mobile user equipment (UE) with computation intensive tasks. In this ...paper, we tackle the computation offloading problem in a mixed fog/cloud system by jointly optimizing the offloading decisions and the allocation of computation resource, transmit power, and radio bandwidth while guaranteeing user fairness and maximum tolerable delay. This optimization problem is formulated to minimize the maximal weighted cost of delay and energy consumption (EC) among all UEs, which is a mixed-integer non-linear programming problem. Due to the NP-hardness of the problem, we propose a low-complexity suboptimal algorithm to solve it, where the offloading decisions are obtained via semidefinite relaxation and randomization, and the resource allocation is obtained using fractional programming theory and Lagrangian dual decomposition. Simulation results are presented to verify the convergence performance of our proposed algorithms and their achieved fairness among UEs, and the performance gains in terms of delay, EC, and the number of beneficial UEs over existing algorithms.
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