A Heuristic Algorithm for QoS Multicast Routing

In recent years, QoS multicast routing has continued to be a very important research topic in the areas of networks. This paper presents a heuristic algorithm for the QoS multicast routing (HAQMR). This heuristic algorithm deals with delay and bandwidth constraints and has low cost. The HAQMR attempts to significantly reduce the overhead for constructing a multicast tree, the proof for correctness of the HAQMR is given, and the performance of the HAQMR is evaluated by simulations. The study shows that HAQMR provides an available approach to QoS multicast routing.     

ALLOCATING REPAIRABLE SYSTEM＇S RELIABILITY SUBJECT TO MINIMAL TOTAL COST - AN INTEGER PROGRAMMING APPROACH

Reliability allocation problem is commonly treated using a closed-form expression relating the cost to reliability. A recent approach has introduced the use of discrete integer technique for un-repairable systems. This research addresses the allocation problem for repairable systems. It presents an integer formulation for finding the optimum selection of components based on the integer values of their Mean Time to Failure （MTTF） and Mean Time to Repair （MTTR）. The objective is to minimize the total cost under a system reliability constraint, in addition to other physical constraints. Although, a closed-form expression relating the cost to reliability may not be a linear; however, in this research, the objective function will always be linear regardless of the shape of the equivalent continuous closed-form function. An example is solved using the proposed method and compared with the solution of the continuous closed-form version. The formulation for all possible system configurations, components and subsystems are also considered.     

A Lagrangian relaxation-based algorithm for the allocation of yard cranes for yard activities with different priorities

This paper proposes a mixed integer programming model for the allocation of rail mounted gantry cranes for four basic yard activities with different priorities.The model pays special attention to the typical features of this kind of gantry cranes,such as a restricted traveling range and a limited number of adjustments during loading and discharging operations.In contrast to most of the literature dealing with these four yard activities individually,this paper models them into an integrated problem,whose computational complexity is proved to be NP-hard.We are therefore motivated to develop a Lagrangian relaxation-based heuristic to solve the problem.We compare the proposed heuristic with the branch-and-bound method that uses commercial software packages.Extensive computational results show that the proposed heuristic achieves competitive solution qualities for solving the tested problems.     

A LAGRANGIAN RELAXATION APPROACH FOR SUPPLY CHAIN PLANNING WITH ORDER／SETUP COSTS AND CAPACITY CONSTRAINTS

A heuristic approach is developed for supply chain planning modeled as multi-item multi-levelcapacitated lot sizing problems. The heuristic combines Lagrangian relaxation(LR) with local search.Different from existing LR approaches that relax capacity constraints and/or inventory balanceconstraints, our approach only relaxes the technical constraints that each 0-1 setup variable must takevalue 1 if its corresponding continuous variable is positive. The relaxed problem is approximatelysolved by using the simplex algorithm for linear programming, while Lagrange multipliers are updatedby using a surrogate subgradient method that ensures the convergence of the dual problem in case ofthe approximate resolution of the relaxed problem. At each iteration, a feasible solution of the originalproblem is constructed from the solution of the relaxed problem. The feasible solution is furtherimproved by a local search that changes the values of two setup variables at each time. By taking theadvantages of a special stru     

Task scheduling for multi-electro-magnetic detection satellite with a combined algorithm

Task scheduling for electro-magnetic detection satellite is a typical combinatorial optimization problem. The count of constraints that need to be taken into account is of large scale. An algorithm combined integer programming with constraint programming is presented. This algorithm is deployed in this problem through two steps. The first step is to decompose the original problem into master and sub-problem using the logic-based Benders decomposition; then a circus combines master and sub-problem solving process together, and the connection between them is general Benders cut. This hybrid algorithm is tested by a set of derived experiments. The result is compared with corresponding outcomes generated by the strength Pareto evolutionary algorithm and the pure constraint programming solver--GECODE, which is an open source software. These tests and comparisons yield promising effect.     

A branch-and-cut approach to portfolio selection with marginal risk control in a linear conic programming framework

Marginal risk represents the risk contribution of an individual asset to the risk of the entire portfolio In this paper, we investigate the portfolio selection problem with direct marginal risk control in a linear conic programming framework. ＇The optimization model involved is a nonconvex quadratically constrained quadratic programming （QCQP） problem. We first transform the QCQP problem into a linear conic programming problem, and then approximate the problem by semidefinite programming （SDP） relaxation problems over some subrectangles. In order to improve the lower bounds obtained from the SDP relaxation problems, linear and quadratic polar cuts are introduced for designing a branch-and-cut algorithm, that may yield an e -optimal global solution （with respect to feasibility and optimality） in a finite number of iterations. By exploring the special structure of the SDP relaxation problems, an adaptive branch-and-cut rule is employed to speed up the computation. The proposed algorithm is tested and compared with a known method in the literature for portfolio selection problems with hundreds of assets and tens of marginal risk control constraints.     

Fault detection for networked systems subject to access constraints and packet dropouts

This paper addresses the problem of fault detection(FD) for networked systems with access constraints and packet dropouts.Two independent Markov chains are used to describe the sequences of channels which are available for communication at an instant and the packet dropout process,respectively.Performance indexes H∞ and H_ are introduced to describe the robustness of residual against external disturbances and sensitivity of residual to faults,respectively.By using a mode-dependent fault detection filter(FDF) as residual generator,the addressed FD problem is converted into an auxiliary filter design problem with the above index constraints.A sufficient condition for the existence of the FDF is derived in terms of certain linear matrix inequalities(LMIs).When these LMIs are feasible,the explicit expression of the desired FDF can also be characterized.A numerical example is exploited to show the usefulness of the proposed results.     

A TABU SEARCH APPROACH TOWARDS CONGESTION AND TOTAL FLOW MINIMIZATION IN OPTICAL NETWORKS

This paper considers rearrangeable multihop lightwave networks whereby each network node is equipped with a number p of transmitters and receivers, and a spectrum of wavelengths is accessible by, and shared among, all nodes by using the Wavelength Division Multiplexing (WDM). Depending on input traffic flow, nodal transmitters and receivers can be re-tuned to create virtual connectivity best suited with respect to a given optimization criterion. We present an efficient heuristic algorithm that combines two criteria for optimization: throughput maximization, as well as total flow minimization. Throughput maximization criterion is equivalent to congestion minimization, while minimizing total flow under the assumption of having links with equal lengths implies minimization of the average number of hops. Taking into account lengths of the links (i.e. link costs proportional with distances), the total flow minimization becomes equivalent to the total delay minimization. Tabu search is implemented as a two-ph     

An improved branch-and-bound algorithm to minimize the weighted flowtime on identical parallel machines with family setup times

This article investigates identical parallel machines scheduling with family setup times. The objective function being the weighted sum of completion times, the problem is known to be strongly NP-hard. We propose a constructive heuristic algorithm and three complementary lower bounds. Two of these bounds proceed by elimination of setup times or by distributing each of them to jobs of the corresponding family, while the third one is based on a lagrangian relaxation. The bounds and the heuristic are incorporated into a branch-and-bound algorithm. Experimental results obtained outperform those of the methods presented in previous works, in term of size of solved problems.     

带振荡策略的启发式算法求解一类新型分配问题

提出了一种新型的分配问题,该问题来源于钢铁企业中的板坯优化管理.与一般分配问题相比,该问题在将物品分配给背包时,除了需满足背包的容量限制外,还需满足流向限制.此问题可归结为 一般分配问题,因此为NP难问题.针对该问题,提出了带有振荡策略和长期表的启发式算法求解.振荡策略使局部搜索算法在可行区域和不可行区域间振荡,以获得更好的近优解;其次,在算法中引入了禁忌搜索的长期表,根据频率鼓励物品的多样性移动,提高算法的分散搜索能力.为验证算法有效性, 对随机产生的23种规模的数据进行了实验.实验结果表明：对于小规模数据,算法结果与最优解的最大偏差为0.55{\%};在大规模情况下,算法能在快速的时间内获得问题的近优解.     

多星联合任务规划的迭代修复求解技术

对地观测卫星任务规划问题需要考虑侧视、星上能量、数据容量和数据传输等多种约束,是一类复杂的组合优化问题.现有研究大多对问题进行了不同程度的简化.面向多种类型卫星的联合任务规划问题,考虑上述多种约束,建立数学规划模型,引入迭代修复方法对问题进行求解,并提出了基于成像任务分布的插入选择和撤销选择启发式准则.实验结果表明,迭代修复技术在多星联合任务规划领域是可行有效的.     

一种求解资源约束条件下运输优化问题的启发式方法

介绍了一种求解资源约束条件下的大规模组合优化运输问题的启发式方法。由于现实生活中的运输系统的复杂性，与总运输时间相关的目标函数无法用解析方法给出，在这种条件下它需要通过仿真运行得到，同时运输资源(主要指道路和中转站等)的限制又增加了优化的难度，传统的求解这种瓶颈运输问题的网络流方法无法处理。本文介绍的启发式方法充分利用了仿真模型对于系统的直观描述特性，将资源约束的求解反馈到优化过程中，取得了较好的效果。     

SET COVERING-BASED TOPSIS METHOD FOR SLOVING SUP-T EQUATION CONSTRAINED MULTI-OBJECTIVE OPTIMIZATION PROBLEMS

This paper considers solving a multi-objective optimization problem with sup-r equation constraints.A set covering-based technique for order of preference by similarity to the ideal solution is proposed for solving such a problem.It is shown that a compromise solution of the sup-r equation constrained multi-objective optimization problem can be obtained by solving an associated set covering problem.A surrogate heuristic is then applied to solve the resulting optimization problem.Numerical experiments on solving randomly generated multi-objective optimization problems with sup-T equation constraints are included.Our computational results confirm the efficiency of the proposed method and show its potential for solving large scale sup-T equation constrained multi-objective optimization problems.     

非线性约束最短路问题的启发式算法

多约束QoS路由优化是当前网络研究中的一个重要课题,而受限最短路问题(RSP)是QoS路由的一个基本问题。它是NP-完全的,并有许多具有多项式时间和伪多项式时间的启发式求解算法。然而这些方法只能求解一些带有线性约束的RSP。对一些非线性的约束(比如丢失率约束)大都用数学方法转化成线性约束来求解,这增加了问题的复杂性。本文提出了一种新的具有伪多项式时间的启发式算法来求解这类带非线性约束的RSP。主要思想是将非线性约束作为检验条件来使用。当每得到一个解时,检查解是否满足非线性约束。如满足,则得到最终解;否则在原问题中添加一个线性约束。该新约束将去除已经找到的解,从而使原问题的解空间进一步缩小,直到得到最终解。仿真算例说明了算法的有效性。     

多资源约束的网络计划的启发式优化方法

多资源约束的网络计划的启发式优化方法白思俊（西北工业大学管理学院，西安７１００７２）ＨｅｕｒｉｓｔｉｃＭｅｔｈｏｄｆｏｒＭｕｌｔｉｐｌｅＲｅｓｏｕｒｃｅ－ＣｏｎｓｔｒａｉｎｅｄｉｎＰＥＲＴ／ＣＰＭＮｅｔｗｏｒｋＢａｉＳｉｊｕｎ（ＭａｎａｇｅｍｅｎｔＳ...     

Solving the Observing and Downloading Integrated Scheduling Problem of Earth Observation Satellite with a Quantum Genetic Algorithm

This paper addresses the integrated Earth observation satellite scheduling problem. It is a complicated problem because observing and downloading operations are both involved. We use an acyclic directed graph model to describe the observing and downloading integrated scheduling problem.Based on the model which considering energy constraints and storage capacity constraints, we develop an efficient solving method using a novel quantum genetic algorithm. We design a new encoding and decoding scheme that can generate feasible solution and increase the diversity of the population.The results of the simulation experiments show that the proposed method solves the integrated Earth observation satellite scheduling problem with good performance and outperforms the genetic algorithm and greedy algorithm on all instances.     

动态加强CPT解job-shop调度约束满足优化问题

带有相同到达期与交货期的job-shop调度问题(JSSP)作为多种实际生产调度问题简化模型,是一类典型强NP-hard问题.对优化目标是最小化最大完工时间的JSSP问题,建立了约束满足优化问题模型(JSSC-SOP).利用弧一致约束传播算法和深度优先启发式构造活动调度,逐步加入新约束,实现活动调度集的部分列举与寻优.提出3种动态加强约束传播技术(CPT),嵌入搜索过程,提高求解效率.最后通过随机生成的实例,验证了各方法可行性与有效性.     

A case study for advanced planning and scheduling (APS)

This paper presents a case study for the advanced planning and scheduling (APS) problem encountered in a light source manufacturer. The APS problem explicitly considers due dates of products, operation sequences among items, and capacity constraints of the manufacturing system. The objective of the problem is to seek the minimum cost of both production idle time and tardiness or earliness penalty of an order. An intelligent heuristic is applied to the problem, and the results demonstrate that significant production performances can be achieved while ensuring customer satisfaction as opposed to normal practices followed in the company relying on human expertise.