Advances in Global Optimization

Mathematical programs with cardinality constraints are optimization problems with an additional constraint which requires the solution to be sparse in the sense that the number of nonzero elements, i.e. the cardinality, is bounded by a given constant. Such programs can be reformulated as a mixed-integer ones in which the sparsity is modeled with the use of complementarity-type constraints. It is shown that the standard relaxation of the integrality leads to a nonlinear optimization program of the striking property that its solutions (global minimizers) are the same as the solutions of the original program with cardinality constraints. Since the number of local minimizers of the relaxed program is typically larger than the number of local minimizers of the cardinality-constrained problem, the relationship between the local minimizers is also discussed in detail. Furthermore, we show under which assumptions the standard KKT conditions are necessary optimality conditions for the relaxed program. The main result obtained for such conditions is significantly different from the existing optimality conditions that are known for the somewhat related class of mathematical programs with complementarity constraints.

It is known that the minimal cone for the constraint system of a conic linear optimization problem is a key component in obtaining strong duality without any constraint qualification. In the particular case of semidefinite optimization, an explicit expression for the dual cone of the minimal cone allows for a dual program of polynomial size that satisfies strong duality. This is achieved due to the fact that we can express the orthogonal complement of a face of the cone of positive semidefinite matrices completely in terms of a system of semidefinite inequalities. In this paper, we extend this result to cones that are either faces of the cone of positive semidefinite matrices or the dual cones of faces of the cone of positive semidefinite matrices. The newly proved result was used in Zhang (4OR 9:403–416, 2011). However, a proof was not given in Zhang (4OR 9:403–416, 2011).

A bilevel optimization problem consists of minimizing an upper level objective function subject to the constraints that involve the solution mapping of the lower level optimization problem parameterized by the upper level decision variable. The global equivalence between a general bilevel programming problem and a multiobjective optimization problem with nonconvex ordering cone is established and optimality conditions of the bilevel problem are obtained using Mordukhovich extremal principles.

In this paper, we focus on deriving some sufficient conditions for global solutions to cubic minimization problems with box constraints. Our main tool is an extension of the global subdifferential, L-normal cone approach, developed by Jeyakumar et al. (J. Glob. Optim., 2007; Math. Program. Ser. A 110, 2007), and underestimator functions. By applying these tools to characteristic global solutions, we provide some sufficient conditions for cubic programming problem with box constraints. An example is given to demonstrate that the sufficient conditions can be used effectively for identifying global minimizers of certain cubic minimization problems with box constraints.

This article presents an outcome space branch-and-bound algorithm for globally solving a class of linear multiplicative programming problem. In this algorithm, the lower bound is found by solving a separable relaxation programming problem. A convex quadratic programming problem is constructed so as to improve the ability to set the upper bound. The convergence of the algorithm is proved. Numerical experiments are reported to show the feasibility and effectiveness of the proposed algorithm.

We present a cut-peak function method for finding a global minimizer of the bound constrained optimization problems. A simple cut-peak function and choice function are defined at a local minimizer. By minimizing the choice function, a global descent of the original objective function is assured. Since the pattern search method does not require the gradient of the choice function, smoothing technique is not employed. The new algorithm is simple to implement and numerical results indicate its efficiency.

We present a modified definition of the filled function for discrete nonlinear programming problem and give a filled function satisfying our definition. The properties of the proposed filled function and the method using this filled function to solve discrete nonlinear programming problem are discussed in this paper. The results of preliminary numerical experiments are also reported.

The aim of this paper is to propose a global algorithm model for continuous constrained nonlinear programming based on a new simple and exact penalty function. Under weak assumptions, we show that the optimizer obtained by the algorithm is converged to the global minimizer of the original problem.

In this paper, a discrete state transition algorithm is introduced to solve a multiobjective single machine job shop scheduling problem. In the proposed approach, a non-dominated sort technique is used to select the best from a candidate state set, and a Pareto archived strategy is adopted to keep all the non-dominated solutions. Compared with the enumeration and other heuristics, experimental results have demonstrated the effectiveness of the multiobjective state transition algorithm.

This paper addresses the model modification in scheduling of batch chemical processes, which is widely used in current literatures. In the modified model, the capacity, storage constraints are modified and the allocation, sequence constraints are simplified. It is shown that the modified model can lead to fewer decision variables, fewer constraints, resulting in low computational complexity. Experimental results with two classical examples are given to demonstrate the effectiveness of the proposed formulation and approach.

In this paper, we introduce a model of distributionally robust facility location problem (DRFLP) under moment constraints up to the second order. We show, via duality theory of moment problems, that the linear relaxation of the DRFLP is equivalent to that of the standard uncapacitated facility location problem (UFLP). Consequently, any LP-based approximation algorithm for the UFLP implies an approximation algorithm for the DRFLP with the same approximation ratio.

An edge-colored graph

G

is rainbow connected if any two vertices are connected by a path whose edges have distinct colors. The rainbow connection number of a connected graph

G

, denoted by

rc

(

G

), is the smallest number of colors that are needed in order to make

G

rainbow connected. In this paper, we investigate rainbow connection numbers of three subfamilies of undirected double-loop networks, denoted by

rc

(

G

(

n

; ±

s

1

, ±

s

2

)), where 1 ≤ 

s

1

 < 

s

2

 < 

n

∕2. We almost determine the precise value for the case that

s

1

=

1

,

s

2

=

2

$$s_{1} = 1,s_{2} = 2$$

. For the case that

n

=

k

s

,

s

1

=

1

,

s

2

=

s

$$n = ks,s_{1} = 1,s_{2} = s$$

, where

s

 ≥ 3 and

k

 ≥ 1 are integers, we derive that

r

c

(

G

(

k

s

;

±

1

,

±

s

)

)

≤

min

{

⌈

k

2

⌉

+

s

,

⌈

s

+

1

2

⌉

+

k

−

1

}

$$rc(G(ks;\pm 1,\pm s)) \leq \min \{\lceil \frac{k} {2} \rceil + s,\lceil \frac{s+1} {2} \rceil + k - 1\}$$

. For the case that

n

=

2

k

s

,

s

1

=

2

,

s

2

=

s

$$n = 2ks,s_{1} = 2,s_{2} = s$$

, where

k

 ≥ 1 are integers and

s

 ≥ 3 are odd integers, we have

r

c

(

G

(

n

;

±

s

1

,

±

s

2

)

)

≤

⌈

k

s

2

⌉

+

k

$$rc(G(n;\pm s_{1},\pm s_{2})) \leq \lceil \frac{ks} {2} \rceil + k$$

.

In a facility game one or more facilities are placed in a metric space to serve a set of selfish agents whose addresses are their private information. In a classical facility game, each agent wants to be as close to a facility as possible, and the cost of an agent can be defined as the distance between her location and the closest facility. In an obnoxious facility game, each agent wants to be far away from all facilities, and her utility is the distance from her location to the facility set. The objective of each agent is to minimize her cost or maximize her utility. An agent may lie if, by doing so, more benefit can be obtained. We are interested in social choice mechanisms that do not utilize payments. The game designer aims at a mechanism that is strategy-proof, in the sense that any agent cannot benefit by misreporting her address, or, even better, group strategy-proof, in the sense that any coalition of agents cannot all benefit by lying. Meanwhile, it is desirable to have the mechanism to be approximately optimal with respect to a chosen objective function. Several models for such approximation mechanism design without money for facility games have been proposed. In this paper we briefly review these models and related results for both deterministic and randomized mechanisms, and meanwhile we present a general framework for approximation mechanism design without money for facility games.

In this paper, we consider the robust facility location problem with penalties, aiming to serve only a specified fraction of the clients. We formulate this problem as an integer linear programming to identify which clients must be served. Based on the corresponding LP relaxation and dual program, we propose a primal-dual 3-approximation algorithm. Combining the greedy augmentation procedure, we further improve the above approximation ratio to 2.

Generalized traveling salesman problem (GTSP) is an extension of classical traveling salesman problem (TSP), which is a combinatorial optimization problem and an NP-hard problem. In this paper, an efficient discrete state transition algorithm (DSTA) for GTSP is proposed, where a new local search operator named

K-circle

, directed by neighborhood information in space, has been introduced to DSTA to shrink search space and strengthen search ability. A novel robust update mechanism, restore in probability and risk in probability (Double R-Probability), is used in our work to escape from local minima. The proposed algorithm is tested on a set of GTSP instances. Compared with other heuristics, experimental results have demonstrated the effectiveness and strong adaptability of DSTA and also show that DSTA has better search ability than its competitors.

In this paper, we study global optimal solutions of minimizing a nonconvex quadratic function subject to a sphere constraint. The main challenge is to solve the problem when it has multiple global solutions on the boundary of the sphere, which is called

hard case

. By canonical duality theory, a concave maximization problem is formulated, which is one-dimensional and without duality gaps to the primal problem. Then sufficient and necessary conditions are provided to identify whether the problem is in the hard case or not. A perturbation method and associated algorithms are proposed to solve hard-case problems. Theoretical results and methods are verified by numerical examples.

In this paper, we study general fixed point problem. We first rewrite the original problem in the canonical framework. Then, we proposed a canonical transformation of this problem, which leads to a convex differentiable dual problem and new iteration method. An illustrative example is presented.

The facility location problem is one of the most widely studied discrete location problems, whose applications arise in a variety of settings, such as routers or servers in a communication network, warehouses or distribution centres in a supply chain, hospitals or airports in a public service system. The problem involves locating a number of facilities to minimize the sum of the fixed setup costs and the variable costs of serving the market demand from these facilities. First a dual problem is developed for the facility location problem. Then general optimality conditions are also obtained, which generate sequences globally converging to a primal and dual solutions, respectively.

In this paper, we address the exact controllability problem for the hyperbolic electromagnetic equation, which plays an important role in the research of quantum mechanics. Typical techniques such as Hamiltonian induced Hilbert spaces and pseudodifferential operators are introduced. By choosing appropriate multipliers, we proved the observability inequality with sharp constants. In particular, a genuine compactness-uniqueness argument is applied to obtain the minimal time. In the final analysis, a suitable boundary control is constructed by the systematic Hilbert Uniqueness Method introduced by J.L. Lions. Compared with the micro-local discussion in Bardos et al. (SIAM J Control Optim 30:1024–1065, 1992), we do not require the coefficients belong to

C

∞

. Actually,

C

1

is already sufficient for the vector potential of the hyperbolic electromagnetic equation.

This paper intends to reveal the inter-duality nature of a system and the intrinsic structure of a general system. Furthermore, the inter-duality theory will be introduced and employed to analyse the nature of optimization and optimal behaviour relative to management systems.

This paper proposes a new design optimization method for structures subject to uncertainty. Interval model is used to account for uncertainties of uncertain-but-bounded parameters. It only requires the determination of lower and upper bounds of an uncertain parameter, without necessarily knowing its precise probability distribution. The interval uncertain optimization problem containing interval design variables and/or interval parameters will be formulated as a nested double-loop procedure, in which the outer loop optimization updates the midpoint of interval variables while the inner loop optimization calculates the bounds of objective and constraints. However, the nested double-loop optimization strategy will be computationally prohibitive, and it may be trapped into some local optimal solutions. To reduce the computational cost, the interval arithmetic is applied to the inner loop to directly evaluate the bounds of interval functions, so as to eliminate the optimization of the inner loop. The Taylor interval inclusion function is introduced to control the overestimation induced by the intrinsic wrapping effect of interval arithmetic. Since it is hard to evaluate the high-order coefficients in the Taylor inclusion function, a Chebyshev meta-model is proposed to approximate the Taylor inclusion function. Two numerical examples are used to demonstrate the effectiveness of the proposed method in the uncertain design optimization.

This paper proposes an alternative topology optimization approach for the design of the large displacement compliant mechanisms with geometrical non-linearity by using the Element-free Galerkin (EFG) Method. In this study, because of its non-negative and range-bounded properties, Shepard function method, as a density filter, is used to generate a non-local nodal density field with enriched smoothness over the design domain. Besides, the Shepard function method is employed to build a point-wise density interpolation, the numerical implementation to calculate the artificial densities at all Gauss points. The moving least squares (MLS) method is then used to construct shape functions with compactly supported weight functions, to assemble the meshless approximations of system state equations. A typical large deformation compliant mechanism is presented to demonstrate the effectiveness of the proposed method.

An extrudable structure is formed via the extrusion process in which materials are squeezed through a die with pre-specified shape. Then, we obtain the long and straight metal parts with the fixed cross sections. The key of implementing the extrusion process is that the cross sections are perpendicular to the specified direction should be kept constant. To this end, this paper proposes a level set based optimization method for 3D structures with the extrusion constraint. The compactly supported radial basis functions (CS-RBFs) are introduced to convert the conventional level set method to a parametric one. The cross section projection strategy is applied to reduce the design variables and satisfy the extrusion constraint. Several 3D numerical examples are also provided.

This paper proposes a topology optimization method based on an adaptive element-free Galerkin (EFG) method. Since there are a large number of discrete design variables in the meshless based topology optimization, the adaptive EFG method is included to improve the computational efficiency. The topology optimization problem is formulated as a mean compliance design of structures, in which the nodal densities are regarded as design variable. In the proposed method, the criteria of adding node, the scheme of adaptively inserting nodes, and the method of updating the variable for any new node are discussed. A typical example is used to demonstrate the effectiveness of the proposed topology optimization method using the adaptive EFG method.

This paper presents the multi-objective optimization problem of double suction centrifugal pump using Kriging meta-model. A set of double suction centrifugal pumps with various blade shapes were numerically simulated in the CFD software. Efficiency

η

and required net positive suction head (NPSHr) were investigated through these numerical simulations. Kriging meta-models were built to approximate the pump characteristic performance functions

η

and NPSHr using the design variables related to the blade geometrical shape. The objectives are to maximize

η

, as well as to minimize the NPSHr, which are two important indicates of centrifugal pump. Non-dominated Sorting Genetic Algorithm II (NSGA II) is used as the optimization algorithm. A tradeoff optimal point was selected in the Pareto-optimal solution set by means of robust design based on Monte Carlo simulations, and then the simulation result of the optimal solution was compared with experiment result, which shows that the proposed optimal solution coincides with the experiment well.

The material in the human proximal femur is considered as bi-modulus material and the density distribution is predicted by topology optimization method. To reduce the computational cost, the bi-modulus material is replaced with two isotropic materials in simulation. The selection of local material modulus is determined by the previous local stress state. Compared with density prediction results by traditional isotropic material in proximal femur, the bi-modulus material layouts are different obviously. The results also demonstrate that the bi-modulus material model is better than the isotropic material model in simulation of density prediction in femur bone.

Functionally graded materials (FGMs) are inhomogeneous composites which are characterized by gradual variation in their physical properties. This study proposes a computational approach based on the bi-directional evolutionary structural optimization (BESO) for topologically designing microstructures of such materials with multi-functional properties, e.g. bulk modulus and thermal conductivity. It is assumed that the base cells are composed of two constituents. The smooth transition between adjacent base cells is realized by considering three base cells at each stage of the optimization. Effectiveness and efficiency of the proposed approach has been demonstrated by several numerical examples.

For the class of nonautonomous evolution inclusions with pointwise pseudomonotone multi-valued mappings the dynamics as

t

 → +

∞

of all global weak solutions defined on [0, +

∞

) is studied. The existence of a compact uniform trajectory attractor is proved. The results obtained allow one to study the dynamics of solutions for new classes of evolution inclusions related to nonlinear mathematical models of controlled geophysical and socioeconomic processes and for fields with interaction functions of pseudomonotone type satisfying the condition of polynomial growth and the standard sign condition.

In this work, we consider a static frictional contact problem between a linearly elastic body and an obstacle, the so-called foundation. This contact is described by a normal compliance condition of such a type that the penetration is restricted with unilateral constraint. The friction is modeled with a nonmonotone law. In order to approximate the contact conditions, we consider a regularized problem wherein the contact is modeled by a standard normal compliance condition without finite penetration. Next, we present a convergence result between the solution of the regularized problem and the original problem. Finally, we provide a numerical validation of this convergence result. To this end we introduce a discrete scheme for the numerical approximation of the frictional contact problems.

We provide an existence result in the study of a new class of mixed variational problems. The problems are formulated on unbounded interval of time and involve history-dependent operators. The proof is based on generalized saddle point theory and various estimates, combined with fixed point arguments. Then, we consider a new mathematical model which describes the frictionless contact between a viscoelastic body and an obstacle. The process is quasistatic and the contact is modelled with a version of the normal compliance condition with unilateral constraint, which describes both the hardness and the softness of the foundation. We list the assumption on the data, derive a variational formulation of the problem, then we use our abstract result to prove its weak solvability.

We apply a sequential dual canonical transformation on the global optimization problem resulting from the reformulation of the Karush–Kuhn–Tucker conditions of affine quasi-variational inequalities (QVIs) using the Fischer-Burmeister complementarity function. Canonical duality is generally able to provide conditions for a critical point of the dual formulation to be the corresponding point of a global optimum of the original problem. By studying the new dual formulation it is possible to obtain properties that are not evident from the original one and that can be useful to develop new methods for the solution of (not necessarily affine) QVIs. The resulting formulation is canonically dual to the original in the sense that there is no duality gap between critical points of the original problem and those of the dual one.

We study a class of dynamic thermal sub-differential contact problems with friction, for long memory viscoelastic materials, without the clamped condition, which can be put into a general model of system defined by a second order evolution inequality, coupled with a first order evolution equation. After statement of an existence and uniqueness result, we present a fully discrete scheme for numerical approximations and analysis of error order estimate.

This paper presents a Newton-CG augmented Lagrangian method for solving convex quadratically constrained quadratic semidefinite programming (QCQSDP) problems. Based on the Robinson’s CQ, the strong second order sufficient condition, and the constraint nondegeneracy conditions, we analyze the global convergence of the proposed method. For the inner problems, we prove the equivalence between the positive definiteness of the generalized Hessian of the objective functions in those inner problems and the constraint nondegeneracy of the corresponding dual problems, which guarantees the superlinear convergence of the inexact semismooth Newton-CG method to solve the inner problem. Numerical experiments show that the proposed method is very efficient to solve the large-scale convex QCQSDP problems.

In this work, a novel hybrid population-based algorithm, named SP-QPSO has been introduced by combining Shuffled Complex Evolution with PCS (SP-UCI) and Quantum Particle Swarm Optimization (QPSO). The main purpose of this algorithm is to improve the efficiency of optimization task in both low and high dimensional problems. SP-QPSO is using the main strategy of SP-UCI by constructing complexes and monitoring their dimensionality, then evolving each complex based on QPSO. In this algorithm the initialization of point is done using Centroidal Voronoi Tessellations (CVT) to ensure that points visit the entire search space. Twelve popular benchmark functions are employed to evaluate the SP-QPSO performance in 2, 10, 50, 100, and 200 Dimensions. The results show that the proposed algorithm performed better in most functions.

A filter-genetic method for constrained optimization problems is presented. It uses the filter technique instead of a fitness function to determine the merits of individuals. The method not only ensures the optimization of the offspring, but also avoids selecting the penalty parameter of a penalty function, which often leads to computational instability. And the numerical results are listed in the end.

Singular nonlinear optimization problem has been the difficulty for optimization, which is frequently encountered in practical applications. People have been using numerical iteration methods to deal with the singular problem previously, but the numerical instability and large calculation amount have not been able to be resolved. Based on neural network, this paper puts forward a continuity solution with any rank defect, by using the Augmented Lagrangian method and Projection to form a stable model. By using LaSalle’s invariance principle, it is shown that the solution of the proposed network model is convergent. The numerical simulation also further confirmed the effectiveness of the method.

A new hybrid algorithm named teaching–learning-based Cuckoo Search (TLCS) is proposed for constrained optimization problems. The TLCS modifies the Cuckoo Search (CS) based on the teaching–learning-based Optimization (TLBO) and then is applied for constrained engineering design problems. Experimental results on several well-known constrained engineering design problems demonstrate the effectiveness, efficiency, and robustness of the proposed TLCS. Moreover, the TLCS obtains some solutions better than those previously reported in the literature.

A leader-following consensus problem of second-order multi-agent systems with switching topologies is considered in this thesis. The consensus problem of the multi-agent systems is converted to the stability problem of the error dynamical system here. By studying the stability properties of error switched systems consisting of both Hurwitz stable and unstable via the average dwell time approach, the necessary condition for the agents reaching leading-following consensus is obtained. It is found that if the average dwell time is chosen sufficiently large and the total activation time of unstable subsystems is relatively small compared with that of Hurwitz stable subsystems, the multi-agent systems can reach leader-following consensus. Finally, the effectiveness of the theoretical findings is demonstrated through some numerical examples.

A multigrid scheme is proposed for solving the Schur complement linear systems arising in each Newton iteration when the semi-smooth Newton method is applied to solve control-constrained elliptic optimal control problems. Numerical experiments are performed to illustrate the high efficiency of our proposed method. Computation simulation shows that the convergence rate is quite robust as the regularization parameter approaches to zero.

DIRECT is a popular deterministic algorithm for global optimization problems. It can find the basins of attraction for global or local optima efficiently, especially when dimension is small. Recently, we have proposed a class of modified DIRECT algorithms to eliminate the sensitivities of the original DIRECT to linear scaling of the objective function. In this paper, we devote to find a specific algorithm with best performance among this class. We compare the performance of the modified DIRECT algorithms on the GKLS test set. Numerical results show that DIRECT-median performs outstanding among this class. What is more, numerical results also show that DIRECT-median can find solutions with high accuracy much more efficiently than the original DIRECT.

Lean distribution networks have been facing an increased exposure to risk of unpredicted disruptions causing significant economic forfeitures. At the same time, the existing literature features very few studies which examine the impact of facility fortification for improving network reliability. In this paper, we present a reliable

P

-median problem (RPMP) for planning distribution networks against disruptions. We consider heterogeneous facility failure probabilities, one layer of supplier backup, and facility fortification within a finite budget. The RPMP is formulated as nonlinear integer programming model and we develop a Tabu search heuristic that is capable of solving large size problems efficiently.

We propose a stochastic approximation approach for implementing a class of random search-based optimization algorithms called the model-based methods. The approach makes efficient use of the past sampling information as the search progresses and can significantly reduce the number of function evaluations needed to obtain high quality solutions. We illustrate our approach through a specific algorithm called Model-based Annealing Random Search with Stochastic Averaging (MARS-SA), which maintains the per-iteration sample size at a small constant value. We present the global convergence property of MARS-SA and report on numerical results.

Uncertainties exist at all levels of the industrial design and manufacturing. Hitherto, all the studies that handle multi-parametric programming (mp-LP, mp-QP, mp-NLP, mp-MILP, and mp-MINLP) treat uncertainties to be independent of each other; while under some circumstances, there might exist some kinds of quantitative relationship among them. There is still a lack of research studies on the relationship between these uncertainties, which can help simply the complexity of multi-parametric optimization problems in terms of reducing the dimension of uncertainty space or the region of uncertainty space.

This paper presents multiple types of relationships among uncertainty parameters, which can be generalized into two categories: strong relationship and weak relationship. The strong relationship can be used to reduce the dimension of uncertainty space while the weak relationship can be used to reduce the region of uncertainty space. With the combination of the above relationships, different kinds of multi-parametric programming problems can be solved more efficiently and effectively toward global optimality.

This paper describes a simulation based methodology utilizing simulation optimization technique in measuring an accurate Malmqusit productivity index (MPI) score. Given the high level of stochastic data in real environment, a novel methodology known as DCBA-MPI has been developed. An example of the mode application is shown in banking institutions. In addition to the novel approach presented, this article provides a new insight into the application domain of MPI measurement as well as the way one conducts productivity study in stochastic environment.

Robust analysis is important for designing and analyzing algorithm for global optimization. In this paper, we introduced a new concept, robust constant, to quantitatively characterize robustness of measurable sets and measurable functions. The new concept is consistent with the robustness proposed in literature. This paper also showed that robust constant had significant value in the analysis of some random search algorithms for solving global optimization problem.

In this paper, we propose a closed-loop supply chain network equilibrium model with environmental indicators through variational inequality theory, which is composed by raw material suppliers, manufacturers, retailers, demand markets, and recovery centers. In view of sustainable development, the Ministry of Environmental Protection legislates by imposing emission penalties to prevent the manufacturers from violating laws and regulations but offering premiums to stimulate the recovery centers to recycle used products. The penalties and premiums should be greater than the corresponding shadow prices as environmental indicators determined by the model, which is constructive for decision making of the authorities. We describe their behavior, derive optimality conditions, and establish the variational inequality in accordance with the closed-loop supply chain network equilibrium conditions. Based on the existence of solution to the model under reasonable assumptions, a numerical example is provided for illustration.

Stock movement often shows inconformity with macroeconomic situation and waves more intensively than expected. In this paper, we mainly focus on the influence of social expectation and macroeconomic issue on Shanghai stock market. By establishing a vector error correction model (i.e., VECM), we are able to find out the relationship among them.

In the model, we assume that the macroeconomic situation can be fairly represented by the data of total retail sales of consumer goods and total fixed asset investment. As for social expectation, it can be indicated by the leading index in the economic climate index system. Also, the performance of Shanghai stock market can be regarded as the variation of the Shanghai composite index. Afterwards, we build a VECM connecting these four elements synthetically. Depending on the Granger causality tests and variance decomposition, we can figure out the dynamic impacts of another three factors on Shanghai composite index.

It turns out that expected effect (i.e., leading index) exerts more influence on stock fluctuation than macroeconomic factors. More importantly, all of those three elements fail to nicely explain the variation of Shanghai composite index, which shows that Shanghai stock market’s efficiency is really weak.

Supply chain financial services have been studied for years. However, the literatures have been silent on comparative research of every financial model, especially the model of third party logistics (3PL) firms as credit providers in Cash-strapped supply chains. This paper investigates an extended supply chain model with a Cash-strapped retailer, a supplier, a bank, and a 3PL firm, in which the retailer has insufficient initial budget and may borrow or obtain trade credit from bank, supplier, or a 3PL firm. Our analysis indicates that the 3PL firm model yields higher profits not only for the 3PL firm but also for the supplier, the retailer, and the entire supply chain.

Technologies, such as Atomic Force Microscopy (AFM), have proven to be one of the most versatile research equipments in the field of nanotechnology by providing physical access to the materials at nanoscale. Working principles of AFM involve physical interaction with the sample at nanometre scale to estimate the topography of the sample surface. Size of the cantilever tip, within the range of few nanometres diameter, and inherent elasticity of the cantilever allow it to bend in response to the changes in the sample surface leading to accurate estimation of the sample topography. Despite the capabilities of the AFM, there is a lack of intuitive user interfaces that could allow interaction with the materials at nanoscale, analogous to the way we are accustomed to at macro level. To bridge this gap of intuitive interface design and development, a haptics interface is designed in conjunction with Bruker Nanos AFM. Interaction with the materials at nanoscale is characterised by estimating the forces experienced by the cantilever tip employing geometric deformation principles. Estimated forces are reflected to the user, in a controlled manner, through haptics interface. Established mathematical framework for force estimation can be adopted for AFM operations in air as well as in liquid mediums.

With the development of applications of high power electronic device as a harmonic wave source in power system increase, and harmonic wave which is the most common interference signals in power system often leads to error, malfunction of electronic equipment. In this paper, a method to determine the wavelet threshold based on wavelet theory with particle swarm optimization is provided to remove the noises which have a good ability to adapt the voltage waveform distortion conditions and to improve the real-time performance of harmonic signal processing. Simulation results show that this method is feasible and effective.

This paper further investigates the problem of synchronization of hyperchaotic memristor-based Chua’s circuits. An active control method is employed to design a controller to achieve the global synchronization of two identical memristor-based systems. Based on Lyapunov stability theory, a sufficient condition is given to guarantee the stability of the synchronization error system.

Conflicts between resources in stockyards cause mining companies millions of dollars a year. An effective planning strategy needs to be established in order to reduce these operational conflicts. In this research a stockyard simulation model of a mining operation is proposed. The simulation uses discrete event and continuous strategies to create a high detail level of visualization and animation that closely resemble actual stockyard operation. The proposed simulation model is tightly integrated with a stockpile planner and it is used to evaluate the feasibility of a given production plan. The high detail visualization of the simulation model allows planner to determine the source of conflict, which can be used to guide the elimination of these conflicts.