Covering a convex 3D polytope by a minimal number of congruent spheres

The problem of covering a convex 3D polytope by the minimal number of congruent spheres is reduced to a sequence of problems of minimising sphere radius when fixing the number of the spheres. We form a mathematical model of the problem using the Voronoi polytopes. Characteristics of the model are investigated. Extrema are attained at the vertices of the Voronoi polytopes constructed for sphere centres. To search for local minima, a modification of the Zoutendijk feasible directions method in a combination with random search is developed. Some numerical results for a cube and a non-regular octahedron are obtained.     

Convergence of memory gradient methods

In this paper we present a new class of memory gradient methods for unconstrained optimization problems and develop some useful global convergence properties under some mild conditions. In the new algorithms, trust region approach is used to guarantee the global convergence. Numerical results show that some memory gradient methods are stable and efficient in practical computation. In particular, some memory gradient methods can be reduced to the BB method in some special cases.     

Valve stiction estimation using global optimisation

Valve stiction is the most frequent cause of loop oscillations. Thus, detecting and quantifying this valve problem is essential to ensuring plant profitability. In this work, a novel one-stage procedure to estimate stiction parameters is proposed using a two-parameter stiction model. The optimisation problem is computed using a global optimisation algorithm. These two propositions make the stiction computation more efficient and computationally faster than the currently available method. The applicability of the proposed approach is illustrated using a large number of simulated and industrial valves. Moreover, to isolate the impact of each proposition, the novel method is compared with the currently available technique, which is based on a two-stage scheme. The results show that the global optimisation algorithm is more efficient than the direct search and genetic algorithms, as previously proposed by Jelali (2008). The two-stage procedure provides a better estimate of the apparent stiction, whereas the one-stage procedure provides a better slipjump value.     

Nonlinear Performance Index (npi): A Tool for Manipulator Dynamics Improvement

The precise control of manipulators depends nonlinearly on the velocity of the motion as well as on manipulator configuration and commanded acceleration requiring complex control strategies. This paper presents a useful tool for identifying and quantifying nonlinear effects appearing during the motion of any manipulator, the Nonlinear Performance Index (npi). The npi takes into account not only the geometrical parameters defining the manipulator but also its structural dynamics through the use of inertial parameters like mass, inertia, centre of mass... The npi can be used in the design stage for analysing and reducing these undesirable nonlinear effects in any general motion or in the trajectory planning looking for paths along which more precise control is expected. The last part of the paper shows how this design optimisation and path planning has been applied to the Agribot, a fruit picking robot designed at the IAI.     

Analysing stability and investments in railway networks using advanced evolutionary algorithms

We consider a network of periodically running railway lines. Investments are possible to increase the speed and to improve the synchronisation of trains. The model also includes random delays of trains and the propagation of delays across the network. We derive a cost‐benefit analysis of investments, where the benefit is measured in reduced waiting time for passengers changing lines. We also estimate the actual mean waiting time simulating the train delays. This allows us to analyse the impact that an increasing synchronisation of the timetable has on its stability. Simulation is based on an analytical model obtained from queueing theory. We use sophisticated adaptive evolutionary algorithms, which send off avant‐garde solutions from time to time to speed up the optimisation process. As there is a high correlation between scheduled and estimated waiting times for badly synchronised timetables, we are even able to include the time consuming simulation into our optimisation runs.     

用改进的信赖域方法求解二次插值模型

一种改进的信赖域方法被用来解无约束最优化问题,当目标函数的导数信息不可利用或者求解目标函数的导数代价太大。通常,考虑用二次插值模型来逼近目标函数,并且用传统的信赖域方法求解这个二次模型。传统的信赖域方法将被改进,并且形成两个改进的信赖域子问题。改进的信赖域方法的创新点在于:求解二次模型在一个参数化的信赖域中,修改这个模型在另一个参数化的信赖域当中。在这两个新的信赖域中,可以分别很快地找到一个好的下降方向和一个具有均衡性的插值点。这个改进的方法不但节省了函数值计算次数而且提高了解的精度。实验结果表明,针对测试问题,提出的方法的确是优于传统的信赖域方法的。     

A particle swarm optimisation for the no-wait flow shop problem with due date constraints

This paper considers the no-wait flow shop scheduling problem with due date constraints. In the no-wait flow shop problem, waiting time is not allowed between successive operations of jobs. Moreover, a due date is associated with the completion of each job. The considered objective function is makespan. This problem is proved to be strongly NP-Hard. In this paper, a particle swarm optimisation (PSO) is developed to deal with the problem. Moreover, the effect of some dispatching rules for generating initial solutions are studied. A Taguchi-based design of experience approach has been followed to determine the effect of the different values of the parameters on the performance of the algorithm. To evaluate the performance of the proposed PSO, a large number of benchmark problems are selected from the literature and solved with different due date and penalty settings. Computational results confirm that the proposed PSO is efficient and competitive; the developed framework is able to improve many of the best-known solutions of the test problems available in the literature.     

A hybrid nested partitions algorithm for scheduling flexible resource in flow shop problem

Production scheduling with flexible resources is critical and challenging in many modern manufacturing firms. This paper applies the nested partitions (NP) framework to solve the flexible resource flow shop scheduling (FRFS) problem using an efficient hybrid NP algorithm. By considering the domain knowledge, the ordinal optimisation principle and the NEH heuristics are integrated into the partitioning scheme to search the feasible region. An efficient resource-allocation procedure is built into the sampling scheme for the FRFS problem. A large number of benchmark examples with flexible resources are tested. The test results show that the hybrid NP algorithm is more efficient than either generic NP or heuristics alone. The algorithm developed in this study is capable of selecting the most promising region for a manufacturing system with a high degree of accuracy. The algorithm is efficient and scalable for large-scale problems.     

A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition

This paper proposes a multi-objective hybrid artificial bee colony (MOHABC) algorithm for service composition and optimal selection (SCOS) in cloud manufacturing, in which both the quality of service and the energy consumption are considered from the perspectives of economy and environment that are two pillars of sustainable manufacturing. The MOHABC uses the concept of Pareto dominance to direct the searching of a bee swarm, and maintains non-dominated solution found in an external archive. In order to achieve good distribution of solutions along the Pareto front, cuckoo search with Levy flight is introduced in the employed bee search to maintain diversity of population. Furthermore, to ensure the balance of exploitation and exploration capabilities for MOHABC, the comprehensive learning strategy is designed in the onlooker search so that every bee learns from the external archive elite, itself and other onlookers. Experiments are carried out to verify the effect of the improvement strategies and parameters’ impacts on the proposed algorithm and comparative study of the MOHABC with typical multi-objective algorithms for SCOS problems are addressed. The results show that the proposed approach obtains very promising solutions that significantly surpass the other considered algorithms.