In this paper we propose and evaluate some idea to improve an existing exact algorithm for Input-Output Scheduling Problem (IOSP) in automated warehouses. The existing algorithm is based on LP relaxation of IOSP, which is solved by the column generation method allowing relaxed columns (routes). Our idea is, expecting to enhance LP solution, to impliment the column generation using only exact routes, and to reduce consequently increasing calculation cost by dropping (pegging) unusable edges. The pegging test is done in the preprocessing phase by solving Lagrangian relaxation of IOSP formulated in node cover decision variables. The results of computational experiments show that the proposed algorithm can solve slightly large sized instances in less execution time than existing one.
This paper is concerned with modeling and theoretical analysis on nonlinear dynamic systems subject to nonholonomic affine constraints. First, we give some definitions and concepts on the affine constraints. Next, nonholonomic dynamic systems with affine constraints (NDSAC) are derived, and their normal forms and linear approximated systems are also shown. We then analyze the NDSAC from the viewpoint of nonlinear control theory, and especially focus on accessibility, controllability and stabilizability. Finally, some physical examples are illustrated to verify our results.
This paper considers the state estimation problem for nonlinear systems based on the quantized outputs. This problem plays an important role in achieving higher control performance when we use low-resolution sensors or networked control systems. First, the problem is formulated in a general setting, which could deal with a broad class of nonlinear systems in the presence of non-Gaussian noises. Second, it is proposed to apply the particle filter, which does not depend on linearity of the target systems nor Gauss noises, for the state estimation subject to quantized outputs. Numerical examples are given to demonstrate its effectiveness, where it is also shown how to deal with a class of uncertainty of the target systems.