一种新的为实现IP Over WDM设计的光波长路由器

将IP与WDM相结合是一个极具吸引力的研究方向。与以往点到点的方式不同,提出了一种新的实现IP over WDM的方案,称为DPDP（缺省波长和专用波长,default path and dedicated path）,给出了详细的节点结构图,并解释了其工作流程。最后对相关问题进行了讨论。     

Optimality conditions and solution procedures for nondegenerate dual-response systems

This paper investigates the dual-response problem in the case where the response functions are nonconvex (nonconcave) quadratics and the independent variables satisfy a radial bound. Sufficient conditions for a global optimum are established and shown to generalize to the multi-response case. It is then demonstrated that the sufficient conditions may not hold if the problem is 'degenerate'. However, if the problem is nondegenerate, it is shown that the sufficient conditions are necessarily satisfied by some stationary point. In this case, a specialized algorithm (DRSALG) is shown to locate the global optimum in a finite number of steps. DRSALG will also identify the degenerate case and pinpoint the location where degeneracy occurs. The algorithm is easy to implement from the standpoint of code development, and we illustrate our elementary version on a well-studied dual-response example from quality control.     

An enhanced genetic algorithm for structural topology optimization

Genetic algorithms (GAs) have become a popular optimization tool for many areas of research and topology optimization an effective design tool for obtaining efficient and lighter structures. In this paper, a versatile, robust and enhanced GA is proposed for structural topology optimization by using problem‐specific knowledge. The original discrete black‐and‐white (0–1) problem is directly solved by using a bit‐array representation method. To address the related pronounced connectivity issue effectively, the four‐neighbourhood connectivity is used to suppress the occurrence of checkerboard patterns. A simpler version of the perimeter control approach is developed to obtain a well‐posed problem and the total number of hinges of each individual is explicitly penalized to achieve a hinge‐free design. To handle the problem of representation degeneracy effectively, a recessive gene technique is applied to viable topologies while unusable topologies are penalized in a hierarchical manner. An efficient FEM‐based function evaluation method is developed to reduce the computational cost. A dynamic penalty method is presented for the GA to convert the constrained optimization problem into an unconstrained problem without the possible degeneracy. With all these enhancements and appropriate choice of the GA operators, the present GA can achieve significant improvements in evolving into near‐optimum solutions and viable topologies with checkerboard free, mesh independent and hinge‐free characteristics. Numerical results show that the present GA can be more efficient and robust than the conventional GAs in solving the structural topology optimization problems of minimum compliance design, minimum weight design and optimal compliant mechanisms design. It is suggested that the present enhanced GA using problem‐specific knowledge can be a powerful global search tool for structural topology optimization. Copyright © 2005 John Wiley & Sons, Ltd.     

A deficit scaling algorithm for the minimum flow problem

In this paper, we develop a new preflow algorithm for the minimum flow problem, called deficit scaling algorithm. This is a special implementation of the generic preflow algorithm for the minimum flow problem developed by Ciurea and Ciupală earlier. The bottleneck operation in the generic preflow algorithm is the number of noncancelling pulls. Using the scaling technique (i.e. selecting the active nodes with sufficiently large deficits), we reduce the number of noncancelling pulls to O(n ² log-c) and obtain an O(nm +n ² log-c) algorithm.     

Ab initio phasing of X-ray powder diffraction patterns by charge flipping

Determining crystal structures from powder X-ray diffraction data remains a challenging problem in materials science. By embedding a Le-Bail-like procedure within the recently discovered charge-flipping phasing algorithm, an extremely simple, fast and effective ab initio method has been developed to determine phases directly from indexed powder diffraction patterns. The algorithm solves the degeneracy problem by applying spherical averaging for overlapping Bragg reflections, while solving the phase problem by using the Oszlányi-Süto charge-flipping algorithm. The processes of peak decomposition and phasing are integrated within the same iteration, and a dynamic support is used. The Fienup hybrid input-output algorithm is also incorporated to minimize stagnation. The ability of the algorithm to find structure-factor phases rapidly is found to assist with the fundamental problem of degeneracy (overlapping reflections) which is intrinsic to powder diffraction data. Space-group and chemical-composition information are not needed as inputs, and can be determined from the result. The method is illustrated using several experimental powder patterns of indifferent quality.     

A highly optimised tolerance-based approach for multi-stage,multi-product supply chain network design

In this paper we propose an algorithm called Highly Optimised Tolerance (HOT) for solving a multi-stage, multi-product supply chain network design problem. HOT is based on power law and control theory. The proposed approach takes its traits from the local incremental algorithm (LIA), which was initially employed to maximise the design parameter (i.e. yield), particularly in the percolation model. The LIA is somewhat analogous to the evolution by natural selection schema. The proposed methodology explores a wide search space and is computationally viable. The HOT algorithm tries to make the system more robust at each step of the optimisation. The objective of this paper is to reduce the total cost of supply chain distribution by selecting the optimum number of facilities in the network. To examine the effectiveness of the HOT algorithm we compare the results with those obtained by applying simulated annealing on a supply chain network design problem with different problem sizes and the same data sets.     

An optimal and a heuristic algorithm for the single-item retrieval problem in puzzle-based storage systems with multiple escorts

Puzzle-based storage systems consist of densely stored unit loads on a square grid. The problem addressed in this paper is to retrieve a stored unit load from a puzzle-based storage using the minimum number of item moves. While previous research contributed optimal algorithms for only up to two empty locations (escorts), our approach solves configurations where multiple empty locations are arbitrarily positioned in the grid. The problem is formulated as a state space problem and solved to optimality using an exact search algorithm. To reduce the search space, we derive bounds on the number of eligible empty locations and develop several search-guiding estimate functions. Furthermore, we present a heuristic variant of the search algorithm to solve larger problem instances. We evaluate both solution algorithms on a large set of problem instances. Our computational results show that the algorithms clearly outperform existing approaches where they are applicate and solve more general configurations, which could not be solved to optimality before. The heuristic variant efficiently yields high-quality solutions for significantly larger instances of practically relevant size.     

Tool selection for optimal part production: a Lagrangian relaxation approach

This paper extends previous work on implementation problems associated with a flexible system that produces flat sheet-metal parts with interior holes. The paper makes four main contributions. First, we formulate the problem of selecting tooling and design standards to minimize the cost of producing parts as an optimization model. Second, we develop a projected subgradient algorithm for the Lagrangian relaxation of the problem by using the model's special structure to develop relationships between the Lagrangian multipliers. Third, we demonstrate that the algorithm produces close to optimal solutions (duality gap less than 2%) very quickly on a number of problems derived using a substantial data set obtained from a Chicago area firm. Fourth, an important variant of the traditional repair kit problem is shown to be a special case of the tool selection problem.     

Bi-parental product algorithm for coded waveform design in radar

The problem of obtaining long sequences with finite alphabet and peaky aperiodic auto-correlation is important in the context of radar, sonar and system identification and is called the coded waveform design problem, or simply the signal design problem in this limited context. It is good to remember that there are other signal design problems in coding theory and digital communication. It is viewed as a problem of optimization. An algorithm based on two operational ideas is developed. From the earlier experience of using the eugenic algorithm for the problem of waveform design, it was realised that rather than random but multiple mutations, all the first-order mutations should be examined to pick up the best one. This is called Hamming scan, which has the advantage of being locally complete, rather than random. The conventional genetic algorithm for non-local optimization leaves out the anabolic role of chemistry of allowing quick growth of complexity. Here, the Hamming scan is made to operate on the Kronecker or Chinese product of two sequences with best-known discrimination values, so that one can go to large lengths and yet get good results in affordable time. The details of the ternary pulse compression sequences obtained are given. They suggest the superiority of the ternary sequences.     

Conceptual design optimization of rectilinear building frames: A knapsack problem approach

This article presents an automated technique for preliminary layout (conceptual design) optimization of rectilinear, orthogonal building frames in which the shape of the building plan, the number of bays and the size of unsupported spans are variables. It adopts the knapsack problem as the applied combinatorial optimization problem, and describes how the conceptual design optimization problem can be generally modelled as the unbounded multi-constraint multiple knapsack problem. It discusses some special cases, which can be modelled more efficiently as the single knapsack problem, the multiple-choice knapsack problem or the multiple knapsack problem. A knapsack contains sub-rectangles that define the floor plan and the location of columns. Particular conditions or preferences for the conceptual design can be incorporated as constraints on the knapsacks and/or sub-rectangles. A bi-objective knapsack problem is defined with the aim of obtaining a conceptual design having minimum cost and maximum plan regularity (minimum structural eccentricity). A multi-objective ant colony algorithm is formulated to solve the combinatorial optimization problem. A numerical example is included to demonstrate the application of the present method and the robustness of the algorithm.     

A general corridor method-based approach for capacitated facility location

The Capacitated Facility Location Problem (CFLP) is a well-known optimisation problem with applications in a number of fields, such as distribution system planning, telecommunication network design, and supply chain design. The goal of this paper is to present a matheuristic algorithm based on the corridor method, to develop a general algorithm for a number of variants of the CFLP. The algorithm exploits solutions obtained via Lagrangean relaxation and builds corridors around such solutions via the introduction of constraints around the incumbent solution, used to limit the size of the solution space explored at each iteration. A thorough exploration of the neighbourhoods induced by the corridors is carried out using a mixed integer programming (MIP) solver. More precisely, we solve to (near) optimality over 500 benchmark instances, using the single-source as well as the multi-source formulations, both in the nominal variant, i.e. the deterministic version of the problem, and the robust variant, i.e. the version obtained when using robust optimisation to model the uncertainty of the problem parameters. The performance of the algorithm is highly competitive when compared with the best approaches proposed in the literature for each variant of the CFLP, especially considering that the algorithm has not been designed with a specific CFLP formulation in mind.     

Solving the heterogeneous fixed fleet open vehicle routing problem by a combined metaheuristic algorithm

The vehicle routing problem (VRP) is a well-known combinatorial optimisation problem and holds a central place in logistics management. Many exact, heuristic and metaheuristic approaches have been proposed to solve VRP. An important variant of the VRP arises when a ?eet of vehicles is fixed and characterised by different capacities for distribution activities. The problem is known as the heterogeneous fixed fleet VRP (HFFVRP). The HFFVRP is a natural generalisation of the VRP with several vehicle types, each type being defined by a capacity, a fixed cost and a cost per distance unit, and can cover more practical situations in transportation. This problem consists of determining a set of vehicle trips of minimum total length in which a set of customers is to be satisfied in the demand constraints using identical vehicles with limited capacity. If open routes instead of closed ones are considered in the HFFVRP, the problem becomes a heterogeneous fixed fleet Open VRP (HFFOVRP) which has numerous applications in industrial and service problems. In this paper, a bone route algorithm which uses the tabu search as an improved procedure is utilised to solve the HFFOVRP. The proposed algorithm was tested empirically on a 24 of generated VRPs, and compared with elite ant system and ant colony system. In all cases, the proposed algorithm finds the best-known solutions within a reasonable time.     

Lot sizing problems with strong set-up interactions

We address the problem of coordinated replenishment of products when the products can be produced only in fixed proportion to each other. Such problems commonly arise in the manufacture of sheet/plate metal parts or die-cast parts. The problem is a variant of the well-known Joint Replenishment Problem. We call this problem the Strong Interaction Problem (SIP). After giving a mathematical formulation of the problem, we show that the general problem is NP-hard. An important variant of the problem, in which products are unique to a family, is shown to be polynomially solvable. We present several lower bounds, an exact algorithm and a heuristic for the problem. Computational testing on randomly generated problems suggests that our exact algorithm performs very well when compared with a commercially available integer programming solver. The heuristic method also gives good solutions.     

An extensive simplex method for mapping global feasibility

Understanding the global feasibility of engineering decision-making problems is fundamental to the synthesis of rational engineering decisions. An Extensive Simplex Method is presented to solve the global feasibility for a linear decision model relating multiple decision variables to multiple performance measures, and constrained by corresponding limits. The developed algorithm effectively traverses all extreme points in the feasible space and establishes the graph structure reflecting the active constraints and their connectivity. The algorithm demarcates basic and nonbasic variables at each extreme point, which is exploited to traverse the active constraints and merge the degenerate extreme points. Finally, a random model generator is presented with the capability to control the matrix sparseness and the model degeneracy for an arbitrary number of decision variables and performance measures. The results indicate that all these model properties are significant factors which affect the total number of extreme points, their connected graph, and the global feasibility.     

Tool design problems in a punch press flexible manufacturing system

In this paper, we study tool design problems encountered in using a punch press Flexible Manufacturing System (FMS) for producing flat sheet-metal parts. We consider the problem of designing the minimum number of tools needed to punch a given set of holes in the parts. Holes described by a single attribute as well as two attributes are considered. We model the tool design problems as graph theoretic problems. Such an approach is believed to be new for the problem studied. We have made the following major contributions: First, we show that the two-attribute tool design problem is equivalent to the minimum clique cover problem on the intersection graph of rectangles, which is a well known NP-complete problem. Second, we develop a fast algorithm to construct a set-covering formulation from the underlying graph model. In addition, we show that our approach has applications beyond the tool design problem (e.g., location problems).     

Design of code division multiple access filters based on sampled fiber Bragg grating by using global optimization algorithms

In this article, we focus on the design of code division multiple access filters (used in data transmission) composed of a particular optical fiber called sampled fiber Bragg grating (SFBG). More precisely, we consider an inverse problem that consists in determining the effective refractive index profile of an SFBG that produces a given reflected spectrum. In order to solve this problem, we use an original multi-layers semi-deterministic global optimization method based on the search of suitable initial conditions for a given optimization algorithm. The results obtained with our optimization algorithms are compared, in term of complexity and final design, with those given by an hybrid genetic algorithm (the method generally considered in the literature for designing SFBGs).     

STRUCTURAL OPTIMIZATION USING A NEW LOCAL APPROXIMATION METHOD

A new method for solving structural optimization problems using a local function approximation algorithm is proposed. This new algorithm, called the Generalized Convex Approximation (GCA), uses the design sensitivity information from the current and previous design points to generate a sequence of convex, separable subproblems. The paper contains the derivation of the parameters associated with the approximation and the formulation of the approximated problem. Numerical results from standard test problems solved using this method are presented. It is observed that this algorithm generates local approximations which lead to faster convergence for structural optimization problems.     

On the conventional boundary integral equation formulation for piezoelectric solids with defects or of thin shapes

In this paper, the conventional boundary integral equation (BIE) formulation for piezoelectric solids is revisited and the related issues are examined. The key relations employed in deriving the piezoelectric BIE, such as the generalized Green's identity (reciprocal work theorem) and integral identities for the piezoelectric fundamental solution, are established rigorously. A weakly singular form of the piezoelectric BIE is derived for the first time using the identities for the fundamental solution, which eliminates the calculation of any singular integrals in the piezoelectric boundary element method (BEM). The crucial question of whether or not the piezoelectric BIE will degenerate when applied to crack and thin shell-like problems is addressed. It is shown analytically that the conventional BIE for piezoelectricity does degenerate for crack problems, but does not degenerate for thin piezoelectric shells. The latter has significant implications in applications of the piezoelectric BIE to the analysis of thin piezoelectric films used widely as sensors and actuators. Numerical tests to show the degeneracy of the piezoelectric BIE for crack problems are presented and one remedy to this degeneracy by using the multi-domain BEM is also demonstrated.     

Finding minimum flow time cyclic schedules for non-identical, multistage jobs

Finding a minimum flow time cyclic schedule for a single, multistage job with a serial, re-entrant routing is known to be NP-hard. This paper addresses the problem of scheduling multiple, non-identical jobs in a cyclic fashion, where the job routings may be arbitrary partial orders as well as re-entrant. Given a fixed cycle length, our goal is to minimize a weighted sum of the job flow times. We present a general schedule construction algorithm for implementing a cyclic version of priority dispatch rules that accepts any user-defined tie-breaking function and naturally yields a feasible cyclic schedule. We also describe a pair of easily solvable subproblems that may be used to tighten existing cyclic schedules, as well as an iterative schedule improvement algorithm based on a technique called compression. A numerical study suggests that our schedule construction algorithm, called Cyclic PDR, outperforms its traditional noncyclic priority dispatch rule counterpart, as well as a previously proposed single-pass algorithm. The Cyclic PDR algorithm is shown to be particularly effective when used in conjunction with a least work remaining tie-breaking function. Taken together, our schedule construction and improvement techniques provide an effective solution approach for producing minimum flow time cyclic schedules.     

Effective Form Error Assessment Using Improved Particle Swarm Optimization

This paper presents an improved variant of particle swarm optimization (MPSO) algorithm for the form error evaluation, from a set of coordinate measurement data points. In classical particle swarm optimization (PSO), new solution is updated by the existing one without really comparing which one is better. This behaviour is considered to be caused by lack in exploitation ability in the search space. The proposed algorithm generates new swarm position and fitness solution employing an improved and modified search equation. In this step, the swarm searches in proximity of the best solution of previous iteration to improve the exploitation behaviour. The particle swarm employs greedy selection procedure to choose the best candidate solution. A non-linear minimum zone objective function is formulated mathematically for each form error and consequently optimized using proposed MPSO algorithm. Five benchmark functions are used to prove the efficiency of the proposed MPSO algorithm, by comparing the proposed algorithm with established PSO and genetic algorithm. Finally, the results of the proposed MPSO algorithm are compared with previous literature and with other nature inspired algorithms on the same problem. The results validate that proposed MPSO algorithm is more efficient and accurate as compared to other conventional methods and is well suited for effective form error evaluation using CMMs.