A procedure for solving general integer programming problems

Recently, Misra [Microelectron. Reliab. 31, 285–294 (1991)] introduced a procedure for solving a variety of reliability optimization problems. In the present paper, the authors demonstrate that this procedure can also be used for solving a general class of integer programming problems, which are usually encountered in many allocation problems. System reliability design is only one of the applications. The algorithm, while being simple, has been found to be an economical and exact solution to the integer programming problem. The algorithm solves a very wide variety of the problems which otherwise cannot be easily solved through any of the existing search methods. Several illustrations are provided to establish the superiority of the approach.     

Self-Organizing Migrating Strategies Applied to Reliability-Redundancy Optimization of Systems

The reliability-redundancy allocation problem is a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, mixed-integer non-linear programming, heuristics, and meta-heuristics. Meanwhile, the development of meta-heuristics has been an active research area in optimizing system reliability wherein the redundancy, the component reliability, or both are to be determined. In recent years, a broad class of stochastic algorithms, such as simulated annealing, evolutionary computation, and swarm intelligence algorithms, has been developed for reliability-redundancy optimization of systems. Recently, a new class of stochastic optimization algorithm called SOMA (Self-Organizing Migrating Algorithm) has emerged. SOMA works on a population of potential solutions called specimen, and is based on the self-organizing behavior of groups of individuals in a "social environment". This paper introduces a modified SOMA approach based on a Gaussian operator to solve reliability-redundancy optimization problems. In this context, three examples of mixed integer programming in reliability-redundancy design problems are evaluated. In this application domain, SOMA was found to outperform the previously best-known solutions available.     

Neural network optimization for redundancy allocation

System reliability optimization problems such as redundancy allocation are hard to solve exactly. Neural networks offer an alternative computational model for obtaining good approximate solutions for such problems. In this paper we present a neural network for solving the redundancy allocation problem for a n-stage parallel redundant system with separable objective function and constraints. The problem is formulated as a 0–1 integer programming problem and solved using the network. The performance of the network compare favourably with that of the best fit algorithm. The number of iterations taken by the network increases very slowly with increase in number of variables. Hence the network can easily solve large problems.     

一种基于网络优化的动态信道分配策略

该文提出了一种在CDMA网络中基于网络优化的动态信道分配策略,它针对恒定比特率(CBR)业务建立了一种整数规划的数学模型,它不是每个时隙进行一次信道分配而是若干个时隙根据当前所有用户的状态统筹做一次调度,目的是使得用户QoS和系统性能得到折中考虑。通过仿真,相对于传统话音优先和按照优先级排队的分配算法,这种优化算法在保证业务的QoS的同时使得信道利用率得到提高。     

A new dynamic programming method for reliability & redundancy allocation in a parallel-series system

Reliability & redundancy allocation is one of the most frequently encountered problems in system design. This problem is subject to constraints related to the design, such as required structural, physical, and technical characteristics; and the components available in the market. This last constraint implies that system components, and their reliability, must belong to a finite set. For a parallel-series system, we show that the problem can be modeled as an integer linear program, and solved by a decomposition approach. The problem is decomposed into as many sub-problems as subsystems, one sub-problem for each subsystem. The sub-problem for a given subsystem consists of determining the number of components of each type in order to reach a given reliability target with a minimum cost. The global problem consists of determining the reliability target of subsystems. We show that the sub-problems are equivalent to one-dimensional knapsack problems which can be solved in pseudopolynomial time with a dynamic programming approach. We show that the global problem can also be solved by a dynamic programming technique. We also show that the obtained method YCC converges toward an optimal solution.     

A nature-inspired firefly algorithm based approach for nanoscale leakage optimal RTL structure

Optimization of leakage power is essential for nanoscale CMOS (nano-CMOS) technology based integrated circuits for numerous reasons, including improving battery life of the system in which they are used as well as enhancing reliability. Leakage optimization at an early stage of the design cycle such as the register-transfer level (RTL) or architectural level provides more degrees of freedom to design engineers and ensures that the design is optimized at higher levels before proceeding to the next and more detailed phases of the design cycle. In this paper, an RTL optimization approach is presented that targets leakage-power optimization while performing simultaneous scheduling, allocation and binding. The optimization approach uses a nature-inspired firefly algorithm so that large digital integrated circuits can be effectively handled without convergence issues. The firefly algorithm optimizes the cost of leakage delay product (LDP) under various resource constraints. As a specific example, gate-oxide leakage is optimized using a 45 nm CMOS dual-oxide based pre-characterized datapath library. Experimental results over various architectural level benchmark integrated circuits show that average leakage optimization of 90% can be obtained. For a comparative perspective, an integer linear programming (ILP) based algorithm is also presented and it is observed that the firefly algorithm is as accurate as ILP while converging much faster. To the best of the authors׳ knowledge, this is the first ever paper that applies firefly based algorithms for RTL optimization.     

Optimal Reliability Allocation by Branch-and-Bound Technique

The paper presents an efficient method for finding the exact optimal solutions of reliability allocation problems that are formulated as an integer nonlinear programming problem generalized to handle nonlinear constraints and nonseparable problems. The method is based on branch-and-bound and developed by considering separation and relaxation techniques.     

Joint energy efficiency and spectral efficiency optimization algorithm for UDN under the restriction of interference threshold and backhaul capacity

Aiming at the scenarios which consider the constraint of backhaul capacity restriction and interference threshold in ultra-dense networks (UDN),an integer linear programming (ILP) and Lagrangian dual decomposition (LDD) based joint optimization algorithm of energy efficiency and spectrum efficiency was proposed.In the proposed algorithms,the user association problem with the constraint of limited backhaul capacity was modelled as an ILP problem and then finished the connection between the user and the base station of microcell by solving this problem with dynamic programming method.Therefor,Lagrangian dual decomposition (LDD) was applied in an iteration algorithm for spectrum resource allocation and power allocation.The simulation results show that compared with traditional schemes,the proposed algorithm can significantly improve the energy efficiency and spectrum efficiency of system and use the microcell’s load capacity more efficiently.     

A new partial bound enumeration technique for solving reliability redundancy optimization

A new partial bound enumeration technique is introduced to solve reliability redundancy optimization problems. The algorithm is based on the studies of the characters of the problems and the bound dynamic programming and the Misra integer programming. Some examples show that the proposed algorithm can obtain economically and effectively exact solutions of reliability redundancy optimization problems in many cases, especially when the numbers of the dimension size or the feasible region of the problem are very large.     

Interactive Optimization of System Reliability Under Multiple Objectives

This paper considers a multiobjective reliability allocation problem for a series system with time-dependent reliability. The method determines the most preferable reliability allocation and preventive maintenance schedule. The problem is multiobjective nonlinear mixed-integer. The decision making procedure is based on interactive optimization and a nonlinear programming algorithm. The method is illustrated by a numerical example.     

一种改进的可靠性分配方法及其应用验证

可靠性分配是系统设计中的重要工作,常用的可靠性分配方法如AGREE分配法、评分分配法对影响可靠性指标的因素考虑不全,并且都是针对串联系统的。利用贝叶斯网络在不确定性概率推理方面的优势,将传统的故障树转化为贝叶斯网络求解最小割集和底事件重要度。建立了改进的可靠性分配模型,首先将顶层可靠性指标在最小割集之间分配,然后根据底事件重要度和评分将最小割集对应的可靠性指标分配到底事件。以某型飞机电传操纵系统可靠性指标分配为例,仿真结果表明,贝叶斯网络相对于故障树分析计算底事件重要度更为简单、精度更高。改进的分配方法能够考虑影响可靠性指标的主要因素,实现指标的科学分配。     

Optimal Reliability Allocation Under Preventive Maintenance Schedule

This paper considers a series system of components with time-dependent reliability and gives a new formulation of an optimal reliability allocation problem where an optimal preventive maintenance (PM) schedule is determined simultaneously. The importance of this formulation is shown in comparison with a conventional formulation where PM schedule is not taken into account. The optimization problem becomes a nonlinear mixed-integer programming problem. A simple approximate solution algorithm is given on the basis of a nonlinear programming (NLP) algorithm. The procedure is illustrated by use of a numerical example. Though we restrict our attention to the case where a preventive replacement is adopted as a PM policy, a similar discussion is possible for the as-good-as-new repair.     

A New Technique to Optimize System Reliability

This paper describes an algorithm for solving reliability optimization problems formulated as nonlinear binary programming problems with multiple-choice constraints. These constraints stand for restrictions in which only one variable is assigned to each subset making up the set; thus, they are expressed by equations whose r.h.s. is unity. Different types of methods for achieving high reliability (an increase in component reliability, parallel redundancy, standby redundancy, etc.) can be easily used simultaneously as design alternatives for each subsystem. In order to solve the problem effectively, the Lawler & Bell algorithm is improved by introducing a new lexicographic enumeration order which always satisfies the multiple-choice constraints. The function for obtaining feasible solutions which give first ~ L-th minimum values of the objective function is added to the algorithm in order to make it more useful for decision making. After a numerical example assists in understanding the algorithm, the computational efficiency is compared with that of the Lawler & Bell algorithm.     

A bandwidth allocation and energy-optimal transmission rate scheduling scheme in multi-services wireless networks

The energy-saving of mobile devices during their application offloading process has always been the research hotspot in the field of mobile cloud computing (MCC). In this paper, we focus on the scenario where multiple mobile devices with MCC and non-MCC services coexist. A bandwidth allocation and the corresponding transmission rate scheduling schemes are proposed with the objectives of simultaneously maximizing the overall system throughput and minimizing the energy consumption of individual mobile device with MCC service. To allocate the bandwidth to all mobile devices, two different algorithms are proposed, i.e., 0–1 integer programming algorithm and Lagrange dual algorithm. The transmission rate scheduling scheme for mobile device with MCC service is presented based on reverse order iteration method. The numerical results suggest that energy consumed by individual mobile device with MCC service can be remarkably saved while the overall system throughput can also be maximized. Moreover, the results show that 0–1 integer programming algorithm can get greater system throughput but has higher computational complexity, which means the algorithm is more suitable for small-scale systems, whereas Lagrange dual algorithm can achieve a good balance between the performance and computational complexity.     

Resource allocation algorithm in LTE uplink SC-FDMA system for time-varying channel with imperfect channel state information

In long term evolution (LTE) uplink single carrier frequency division multiple access (SC-FDMA) system, the restriction that multiple resource blocks (RBs) allocated to a user should be adjacent, makes the resource allocation problem hard to solve. Moreover, with the practical constraint that perfect channel state information (CSI) cannot be obtained in time-varying channel, the resource allocation problem will become more difficult. In this paper, an efficient resource allocation algorithm is proposed in LTE uplink SC-FDMA system with imperfect CSI assumption. Firstly, the resource allocation problem is formulated as a mixed integer programming problem. Then an efficient algorithm based on discrete stochastic optimization is proposed to solve the problem. Finally, simulation results show that the proposed algorithm has desirable system performance.     

An Elastic Sub-carrier and Power Allocation Algorithm Enabling Wireless Network Virtualization

Wireless mobile network virtualization enables physical mobile network operators (PMNO) to partition their network resources into smaller slices and assign each slice to an individual virtual mobile network operator and then manages these virtual networks in a more dynamic and cost-effective fashion. How a PMNO allocates resources to individual slices while ensuring resource elasticity is a key issue. This paper presents a resource allocation algorithm in such a network virtualization scenario where resource considered here includes both sub-carriers and transmission power. The overall algorithm involves the following two major processes: firstly to virtualize a physical wireless network into multiple slices each representing a virtual network, where resources are allocated elastically based on traffic loads and channel state information during virtualization; secondly, to carry out physical resource allocation within each virtual network (or slice). In particular the paper adopts orthogonal frequency division multiplexing as its physical layer to achieve more efficient resource utilization. A multi-step dynamic optimization approach is proposed to achieve sub-carrier allocation using binary integer programming and power allocation using nonlinear programming. The aim is to achieve the following design goals: virtual network isolation, and resource efficiency. The simulation results show that the above goals have been achieved.     

导弹惯导系统可靠性分配的Vague集模型

系统可靠性分配是型号总体设计的一个重要环节,传统的用于解决可靠性分配问题的方法,如AGREE分配法、比例分配法等,具有不同的优缺点.针对导弹惯导系统可靠性分配问题,基于Vague集及Fuzzy集的有关理论,引入加权模糊判断矩阵,构造了Vague集模糊熵,建立了导弹惯导系统可靠性分配的Vague集模型.最后用实例说明模型的具体运用过程.     

QoS保证的多信道混合接入CRN频谱共享策略设计

根据既不对主用户(Primary Radio user,PR)产生干扰,又对认知无线电用户(Cognitive Radio user,CR)提供QoS保证的原则,将认知无线电网络(Cognitive Radio Network,CRN)中多信道接入频谱共享问题建模为混合整数非线性规划。采用多级功率限制保证PR不被干扰,以CR物理层能够提供的速率作为信道和功率分配的依据,提出将能够为CR提供最大传输速率的信道分配给该CR的分配策略,将混合整数非线性规划转化为整数线性规划,并给出集中式启发算法和基于功率速率比(Power Rate Ratio,PRR)最小的分布式启发算法求解该整数线性规划。仿真结果表明,文中给出的算法能够提供较高的CRN吞吐量和较好的CR用户QoS保证。     

基于联合备份的服务功能链可靠性保障的部署方法

在网络功能虚拟化(NFV)环境中,针对服务功能链(SFC)部署时的可靠性问题,该文提出对备份虚拟网络功能选择、备份实例放置和服务功能链部署的联合优化方法。首先,定义一个单位开销可靠性提高值的虚拟网络功能衡量标准,改进备份虚拟网络功能选择方法;其次,采用联合备份的方式调整相邻备份实例之间的放置策略,以降低带宽资源开销;最后,将整个服务功能链可靠性保障的部署问题构建成整数线性规划模型,并提出一种基于最短路径的启发式算法,克服整数线性规划求解的复杂性。仿真结果表明,该方法在优先满足网络服务可靠性需求的同时,优化资源配置,提高了请求接受率。     

信息优先级保护的动态频谱分配算法

为提高海上无人艇编队无线网络频谱利用率,同时满足不同优先级信息的传输需求,提出了一种信息优先级保护的动态频谱分配算法。算法采用完全信息动态博弈模型,引入异步分布式定价（Asynchronous Distributed Pricing,ADP）算法设计效用函数。鉴于传统ADP算法在有较多通信余量时干扰价格定价过高,改进干扰价格定义。为体现优先级对分配的影响,在效用函数中加入信息权重。对效用函数成本部分进一步更改,可在实现高优先级信息优先传输的同时,依据通信速率需求合理分配信道通信容量。经过仿真验证,所提算法在吞吐量和可靠性方面优于基于节点优先级的分配算法。