Model predictive thrust control (MPTC) is one of the most effective approaches for linear induction motor (LIM) drive system. It can achieve the optimization of multiple objectives. However, the process of tuning the weighting factors in the objective function is the main drawback of MPTC. It greatly increases the computation burden. In this paper, the tuning process of weighting factors is regarded as a random sampling process. Then, a novel weighting factor optimization method based on the stochastic programming technique is proposed to select the suitable control action for LIM. It will optimize with flux and thrust together to avoid the adjustment of weighting factor. In this paper, the optimal model can be solved by Monte Carlo simulation. At last, the simulation results have shown better dynamic and steady state performance of the proposed method.
This article introduces a solution approach for the Stochastic Capacitated Vehicle Routing Problem (SCVRP) with uncertain demands, called Robust Simulation-Based (RoSi) approach. RoSi aims at designing route plans that can be more or less robust based on a decision-maker weight, i.e. solutions that resist demand changes with marginal additional (recourse) cost. For that, RoSi combines simulation with heuristics. It transforms a complex SCVRP into a set of deterministic ones, where well-known heuristics can be applied, computing a set of feasible solutions. These solutions are assessed by Monte Carlo simulation, and the one that deals better with demand fluctuation is selected as the final solution. The efficiency of RoSi is compared with those of three methods in the literature: Integer Linear Programming (ILP) model, Stochastic Programming with Recourse (SPR) model, and Robust Bi-Objective (RoBi) approach through numerical experiments. The results show that RoSi outperforms these meth