Type II robotic assembly line balancing problem: An evolution strategies algorithm for a multi-objective model

In this paper a different type II robotic assembly line balancing problem (RALB-II) is considered. One of the two main differences with the existing literature is objective function which is a multi-objective one. The aim is to minimize the cycle time, robot setup costs and robot costs. The second difference is on the procedure proposed to solve the problem. In addition, a new mixed-integer linear programming model is developed. Since the problem is NP-hard, three versions of multi-objective evolution strategies (MOES) are employed. Numerical results show that the proposed hybrid MOES is more efficient.     

Heuristics for determining a patrol path of an unmanned combat vehicle

We consider a problem of finding a path of an unmanned combat vehicle that patrols a given area by visiting a given set of checkpoints with the objective of minimizing possibility of enemy’s infiltration. In this study, we focus on a situation in which the possibility of enemy’s infiltration at (through) each checkpoint is increased nonlinearly as time passes and the checkpoint may be patrolled multiple times during a planning horizon. We develop two-phase heuristics in which an initial path is constructed in the first phase and then it is improved in the second phase. For evaluation of the performance of the proposed heuristics, computational experiments are performed on randomly generated problem instances, and results show that the heuristics give good solutions in a reasonably short time.     

Optimization of oval–round pass design using genetic algorithm

The primary purpose of this paper is to propose a computer aided optimal design system to support a generalized oval–round pass design, which is widely used as both intermediate and final passes in the process of rod rolling. This system, which is based on a hybrid model and the genetic algorithm, is developed to improve the efficiency, to reduce the manufacturing errors, as well as to extend the useful life of rolls through uniform wear design. Generalized parametric equations are established for geometrical modeling, graphic plotting of oval–round passes, as well as calculation of the cross section area, contact area and the lengths of contact arcs along the cross section of round groove in the MATLAB programming environment. Moreover, these equations can also realize the parametric transformation between roll profile and mathematical models for the oval–round pass design and optimization. The genetic algorithm is employed for the optimal design of oval–round passes in this paper. The objective functions are formulated for minimization of power consumption in the rolling process, variances between ideal dimensions and design dimensions, as well as variances between the lengths of contact arcs. To reduce the complexity and computational burden of the system, some reliable empirical formulas for the calculations of contact area and contact arc length are applied. Finally, the proposed approach is applied to an oval–round pass design. Through simulation and comparison of results against experimental data acquired from literature, it is found that this system is reliable, effective and easier to use.     

Finite-horizon neuro-optimal tracking control for a class of discrete-time nonlinear systems using adaptive dynamic programming approach

In this paper, a finite-horizon neuro-optimal tracking control strategy for a class of discrete-time nonlinear systems is proposed. Through system transformation, the optimal tracking problem is converted into designing a finite-horizon optimal regulator for the tracking error dynamics. Then, with convergence analysis in terms of cost function and control law, the iterative adaptive dynamic programming (ADP) algorithm via heuristic dynamic programming (HDP) technique is introduced to obtain the finite-horizon optimal tracking controller which makes the cost function close to its optimal value within an ?-error bound. Three neural networks are used as parametric structures to implement the algorithm, which aims at approximating the cost function, the control law, and the error dynamics, respectively. Two simulation examples are included to complement the theoretical discussions.     

An optimal sampling approach to modelling whole-body vibration exposure in all-terrain vehicle driving

Abstract

Exposure to whole-body vibration (WBV) presents an occupational health risk and several safety standards obligate to measure WBV. The high cost of direct measurements in large epidemiological studies raises the question of the optimal sampling for estimating WBV exposures given by a large variation in exposure levels in real worksites. This paper presents a new approach to addressing this problem. A daily exposure to WBV was recorded for 9–24 days among 48 all-terrain vehicle drivers. Four data-sets based on root mean squared recordings were obtained from the measurement. The data were modelled using semi-variogram with spectrum analysis and the optimal sampling scheme was derived. The optimum sampling period was 140 min apart. The result was verified and validated in terms of its accuracy and statistical power. Recordings of two to three hours are probably needed to get a sufficiently unbiased daily WBV exposure estimate in real worksites. The developed model is general enough that is applicable to other cumulative exposures or biosignals.

Practitioner Summary: Exposure to whole-body vibration (WBV) presents an occupational health risk and safety standards obligate to measure WBV. However, direct measurements can be expensive. This paper presents a new approach to addressing this problem. The developed model is general enough that is applicable to other cumulative exposures or biosignals.     

Optimal error estimates of the penalty method for the linearized viscoelastic flows

In this article, optimal error estimates of the penalty method for the linearized viscoelastic flows equations arising in the Oldroyd model are derived. Furthermore, error estimates for the backward Euler time discretization scheme in L ² and H ¹-norms are obtained.     

Modified alternating direction-implicit iteration method for linear systems from the incompressible Navier–Stokes equations

In order to solve the large sparse systems of linear equations arising from numerical solutions of two-dimensional steady incompressible viscous flow problems in primitive variable formulation, Ran and Yuan [On modified block SSOR iteration methods for linear systems from steady incompressible viscous flow problems, Appl. Math. Comput. 217 (2010), pp. 3050–3068] presented the block symmetric successive over-relaxation (BSSOR) and the modified BSSOR iteration methods based on the special structures of the coefficient matrices. In this study, we present the modified alternating direction-implicit (MADI) iteration method for solving the linear systems. Under suitable conditions, we establish convergence theorems for the MADI iteration method. In addition, the optimal parameter involved in the MADI iteration method is estimated in detail. Numerical experiments show that the MADI iteration method is a feasible and effective iterative solver.     

The complexity for partitioning graphs by monochromatic trees,cycles and paths

Let G be an edge-coloured graph. We show in this paper that it is NP-hard to find the minimum number of vertex disjoint monochromatic trees which cover the vertices of the graph G. We also show that there is no constant factor approximation algorithm for the problem unless P?=?NP. The same results hold for the problem of finding the minimum number of vertex disjoint monochromatic cycles (paths, respectively) which cover the vertices of the graph.