Approach by localization and multiobjective evolutionaryoptimization for flexible job-shop scheduling problems

Traditionally, assignment and scheduling decisions are made separately at different levels of the production management framework. The combining of such decisions presents additional complexity and new problems. We present two new approaches to solve jointly the assignment and job-shop scheduling problems (with total or partial flexibility). The first one is the approach by localization (AL). It makes it possible to solve the problem of resource allocation and build an ideal assignment model (assignments schemata). The second one is an evolutionary approach controlled by the assignment model (generated by the first approach). In such an approach, we apply advanced genetic manipulations in order to enhance the solution quality. We also explain some of the practical and theoretical considerations in the construction of a more robust encoding that will enable us to solve the flexible job-shop problem by applying the genetic algorithms (GAs). Two examples are presented to show the efficiency of the two suggested methodologies     

Optimal dynamic spectrum allocation-assisted latency minimization for multiuser mobile edge computing

Mobile Edge Computing (MEC) has been envisioned as an efficient solution to provide computation-intensive yet latency-sensitive services for wireless devices. In this paper, we investigate the optimal dynamic spectrum allocation-assisted multiuser computation offloading in MEC for overall latency minimization. Specifically, we first focus on a static multiuser computation offloading scenario and jointly optimize users' offloading decisions, transmission durations, and Edge Servers' (ESs) resource allocations. Owing to the nonconvexity of our joint optimization problem, we identify its layered structure and decompose it into two problems: a subproblem and a top problem. For the subproblem, we propose a bisection search-based algorithm to efficiently find the optimal users' offloading decisions and ESs’ resource allocations under a given transmission duration. Second, we use a linear search-based algorithm for solving the top problem to obtain the optimal transmission duration based on the result of the subproblem. Further, after solving the static scenario, we consider a dynamic scenario of multiuser computation offloading with time-varying channels and workload. To efficiently address this dynamic scenario, we propose a deep reinforcement learning-based online algorithm to determine the near-optimal transmission duration in a real-time manner. Numerical results are provided to validate our proposed algorithms for minimizing the overall latency in both static and dynamic offloading scenarios. We also demonstrate the advantages of our proposed algorithms compared to the conventional multiuser computation offloading schemes.     

Evolutionary algorithm for data association and IMM-based target tracking in IR image sequences

Simultaneous tracking of multiple maneuvering and non-maneuvering targets in the presence of dense clutter and in the absence of any a priori information about target dynamics is a challenging problem. A successful solution to this problem is to assign an observation to track for state update known as data association. In this paper, we have investigated tracking algorithms based on interacting multiple model to track an arbitrary trajectory in the presence of dense clutter. The novelty of the proposed tracking algorithms is the use of genetic algorithm for data association, i.e., observation to track fusion. For data association, we examined two novel approaches: (i) first approach was based on nearest neighbor approach and (ii) second approach used all observations to update target state by calculating the assignment weights for each validated observation and for a given target. Munkres’ optimal data association, most widely used algorithm, is based on nearest neighbor approach. First approach provides an alternative to Munkres’ optimal data association method with much reduced computational complexity while second one overcomes the uncertainty about an observation’s source. Extensive simulation results demonstrate the effectiveness of the proposed approaches for real-time tracking in infrared image sequences.     

An optimal polygonal boundary encoding scheme in the ratedistortion sense

In this paper, we present fast and efficient methods for the lossy encoding of object boundaries that are given as eight-connect chain codes. We approximate the boundary by a polygon, and consider the problem of finding the polygon which leads to the smallest distortion for a given number of bits. We also address the dual problem of finding the polygon which leads to the smallest bit rate for a given distortion. We consider two different classes of distortion measures. The first class is based on the maximum operator and the second class is based on the summation operator. For the first class, we derive a fast and optimal scheme that is based on a shortest path algorithm for a weighted directed acyclic graph. For the second class we propose a solution approach that is based on the Lagrange multiplier method, which uses the above-mentioned shortest path algorithm. Since the Lagrange multiplier method can only find solutions on the convex hull of the operational rate distortion function, we also propose a tree-pruning-based algorithm that can find all the optimal solutions. Finally, we present results of the proposed schemes using objects from the Miss America sequence.     

Topological optimization for spare-sharing-based wavelength-routed all-optical networks

In this paper, we propose a two-level fault tolerance strategy for wavelength-routed all-optical networks. The first-level strategy is applied to handle the large-scale disaster induced failures while the second-level strategy protects the network against regular single-link failures. The first-level fault tolerance is achieved by solving a topological optimization problem to re-regulate the traffic away from the disaster-affected area with minimum resource cost. Shared lightpath protection is applied in the second-level fault tolerance design to reduce resource allocation. First, by comparing with a simple greedy approach that we develop, we show that the traditional Routing and Wavelength Assignment (RWA) method, in which the routing and wavelength assignment are considered in a separate fashion, cannot lead to satisfying performance. Next, in order to obtain better performance, based on drawback analysis of the greedy approach, we propose a two-phase heuristic algorithm, in which the first phase is designed to generate an initial feasible solution and the second phase iteratively perfects the initial solution until no improvement can be made. For the design of the first phase, two variations are proposed featuring different types of initial solution generation. The numerical results show that, combined with perfection phase, both design variations can lead to considerable performance improvement over the greedy solutions. Finally, we propose a Performance Indicator (PI) that provides insight into the reason for performance difference among algorithms.     

Fixed channel assignment in cellular radio networks using amodified genetic algorithm

With the limited frequency spectrum and an increasing demand for cellular communication services, the problem of channel assignment becomes increasingly important. However, finding a conflict-free channel assignment with the minimum channel span is NP hard. Therefore, we formulate the problem by assuming a given channel span. Our objective is to obtain a conflict-free channel assignment among the cells, which satisfies both the electromagnetic compatibility (EMC) constraints and traffic demand requirements. We propose an approach based on a modified genetic algorithm (GA). The approach consists of a genetic-fix algorithm that generates and manipulates individuals with fixed size (i.e., in binary representation, the number of ones is fixed) and a minimum-separation encoding scheme that eliminates redundant zeros in the solution representation. Using these two strategies, the search space can be reduced substantially. Simulations on the first four benchmark problems showed that this algorithm could achieve at least 80%, if not 100%, convergence to solutions within reasonable time. In the fifth benchmark problem, our algorithm found better solutions with shorter channel span than any existing algorithms. Such significant results indicate that our approach is indeed a good method for solving the channel-assignment problem     

一种新型的动态路由和波长分配算法

本文讨论了WDM光网中，在动态业务流量和有限范围波长变换情况下的动态路由和波长分配（RWA）问题，基于Moone-Dijkstra算法，考虑到动态波长变换的可能和限制，提出了一种新型的、可实现动态最小代价路由和最佳虚波长通道的综合启发式算法（DMC－OVMP）。该算法对路由子问题和波长分配子问题既相互独立，又相互结合，优化了RWA，保证了网络信息传输的安全性。对中国教育和科研计算机网（CERNET）基于本算法进行了计算机仿真，实现了低的网络阻塞率。     

基于图论的无线传感器网络并行禁忌搜索信道分配方法

Wireless sensor networks are suffering from serious frequency interference. In this paper, we propose a channel assignment algorithm based on graph theory in wireless sensor networks. We first model the conflict infection graph for channel assignment with the goal of global optimization minimizing the total interferences in wireless sensor networks. The channel assignment problem is equivalent to the generalized graph coloring problem which is a NP complete problem. We further present a meta heuristic Wireless Sensor Network Parallel Tabu Search (WSN PTS) algorithm, which can optimize global networks with small numbers of iterations. The results from a simulation experiment reveal that the novel algorithm can effectively solve the channel assignment problem.     

Routing and wavelength assignment of scheduled lightpath demands

We present algorithms that compute the routing and wavelength assignment (RWA) for scheduled lightpath demands in a wavelength-switching mesh network without wavelength conversion functionality. Scheduled lightpath demands are connection demands for which the setup and teardown times are known in advance. We formulate separately the routing problem and the wavelength assignment problem as spatio-temporal combinatorial optimization problems. For the former, we propose a branch and bound algorithm for exact resolution and an alternative tabu search algorithm for approximate resolution. A generalized graph coloring approach is used to solve the wavelength assignment problem. We compared the proposed algorithms to an RWA algorithm that sequentially computes the route and wavelength assignment for the scheduled lightpath demands.     

Allocation of Splitting Nodes in All-Optical Wavelength-Routed Networks

In this paper, we introduce the splitter placement problem in wavelength-routed networks (SP-WRN). Given a network topology, a set of multicast sessions, and a fixed number of multicast-capable cross-connects, the SP-WRN problem entails the placement of the multicast-capable cross-connects so that the blocking probability is minimized. The SP-WRN problem is NP-complete as it includes as a subproblem the routing and wavelength assignment problem which is NP-complete. To gain a deeper insight into the computational complexity of the SP-WRN problem, we define a graph-theoretic version of the splitter placement problem (SPG), and show that even SPG is NP-complete. We develop three heuristics for the SP-WRN problem with different degrees of trade-off between computation time and quality of solution. The first heuristic uses the CPLEX general solver to solve an integer-linear program (ILP) of the problem. The second heuristic is based on a greedy approach and is called most-saturated node first (MSNF). The third heuristic employs simulated annealing (SA) with route-coordination. Through numerical examples on a wide variety of network topologies we demonstrate that: (1) no more than 50% of the cross-connects need to be multicast-capable, (2) the proposed SA heuristic provides fast near-optimal solutions, and (3) it is not practical to use general solvers such as CPLEX for solving the SP-WRN problem.     

MR-MC无线传感器网络最小延迟数据聚集调度研究

单radio单信道无线传感器网络的最小延迟聚集调度是一个NPC问题,已提出许多解决方案。在多radio多信道网络中,节点可以同时接收多个不同节点传输的数据,降低延迟。基于上述特点,考虑树结构约束,时槽、信道和radio分配等约束条件,将多radio多信道无线传感器网络最小延迟聚集调度问题定义为一个优化问题,并分解为建立聚集树和节点调度2个子问题,针对这2个子问题分别提出启发式算法。实验结果表明,提出的算法具有良好的性能。     

Multi‐target localization in wireless sensor networks: a compressive sampling‐based approach

This paper considers the problem of localizing a group of targets whose number is unknown by wireless sensor networks. At each time slot, to save energy and bandwidth resources, only part of sensor nodes are scheduled to activate to remain continuous monitoring of all the targets. The localization problem is formulated as a sparse vector recovery problem by utilizing the spatial sparsity of targets’ location. Specifically, each activated sensor records the RSS values of the signals received from the targets and sends the measurements to the sink node where a compressive sampling‐based localization algorithm is conducted to recover the number and locations of targets. We decompose the problem into two sub‐problems, namely, which sensor nodes to activate, and how to utilize the measurements. For the first subproblem, to reduce the effect of measurement noise, we propose an iterative activation algorithm to re‐assign the activation probability of each sensor by exploiting the previous estimate. For the second subproblem, to further improve the localization accuracy, a sequential recovery algorithm is proposed, which conducts compressive sampling on the least squares residual of the previous estimate such that all the previous estimate can be utilized. Under some mild assumptions, we provide the analytical performance bound of our algorithm, and the running time of proposed algorithm is given subsequently. Simulation results demonstrate the effectiveness of our algorithms.Copyright © 2013 John Wiley & Sons, Ltd.     

基于拓扑分析的多层通道布线算法

本文提出了一种基于拓扑分析的多层通道布线算法。算法把整个布线过程分成拓扑分层和物理布线两个部分。拓扑分层利用线段交叠图及模拟退火算法解决线段分层及通孔最少化问题，物理布线过程引入虚拟走线道解决交叉问题，再利用轮廓线跟踪的方法来决定最终确定各线段的布线位置。算法还解决了多层布线分层的管脚约束问题和相邻约束问题。实验结果表明，这是一种有效的方法。     

多小区多用户分布式天线系统中发射功率、天线选择和波束因子的联合优化

本文研究多小区多用户的分布式天线系统中最大化用户的最小信干噪比的优化问题。通过块坐标下降法,逐次优化发射功率、天线选择和波束因子中的每一块变量,而固定其余两块变量,使得每个子问题容易求解,减少计算复杂度。由于目标函数中每个子问题都是NP-hard,因此我们基于半正定松弛和高斯随机化的近似算法求解每个子问题。本文逐级优化分布式天线系统的方法首次综合考虑了包括发射功率在内的主要系统参数,仿真结果表明本文所提出的算法优于以往文献中的方法。      

Shared relay assignment in cooperative communications for bandwidth maximization

Previous research mostly focused on mutually assigning one relay node for a source node to explore the transmission gains of cooperative communications. However, sharing a relay among multiple source nodes can improve the utilization of the relay node. This paper first defines the shared relay assignment for max–min bandwidth (SRA-MMB) problem and max–total bandwidth (SRA-MTB) problem, and formalizes them using integer programs. For the SRA-MMB problem, we propose a heuristic shared relay assignment algorithm (called RRA-MB) using binary search and rounding methods to maximize the minimum bandwidth of all the sources. The theoretical analysis proves that, in the worst case, the proposed algorithm can reach the approximate performance of \(2 + \varepsilon\), where \(\varepsilon\) is an arbitrarily small positive number. For the SRA-MTB problem, we propose a 2-approximation algorithm based on the rounding method to maximize the total bandwidth when each source has a relay usage time limit. Moreover, an optimal algorithm is designed to solve the special instance of SRA-MTB, in which all the source nodes have the same (i.e., uniform) limit on its relay usage time. Our simulations show that the proposed algorithms can achieve about 18 and 27 % improvements over the existing approaches in terms of minimum and total bandwidths respectively.     

The scheduling and wavelength assignment problem in optical WDM networks

We consider a scheduling problem, which we call the scheduling and wavelength assignment (SWA) problem, arising in optical networks that are based on the wavelength-division-multiplexing (WDM) technology. We prove that the SWA problem is NP-complete for both the preemptive and the nonpreemptive cases. Furthermore, we propose two efficient approximation algorithms. The first is for the preemptive case and is based on a natural decomposition of the problem to the classical multiprocessor scheduling and open-shop problems. For the nonpreemptive case, we prove that a naive implementation of list scheduling produces a schedule that can be m times far from the optimum, where m is the number of processors (equivalently, WDM channels). Finally, we give a more refined version of list scheduling and we prove it to be a 2-approximation algorithm for both the off-line and the on-line contexts.     

基于遗传算法的排产优化方法

针对单件小批量生产车间的优化排产问题,采用遗传算法进行研究,设计了一种分组编码方案,提出了可处理零部件间装配关系的适应度函数算法。介绍了遗传算法在车间优化排产中的实现方法及结果,重点讨论了编码方案及适应度函数设计。基于此算法,开发了生产作业计划排产软件,可完成顺排产与倒排产的自动排产,实现工艺排产的智能化。     

A Tabu search algorithm for static routing and wavelength assignment problem

Static routing and wavelength assignment (RWA) is usually formulated as an optimization problem with the objective of minimizing wavelength usage (MWU). Existing solution methodologies for the MWU problem are usually based on a two-step approach, where routing and wavelength assignment are done independently. Though this approach can reduce computational cost, the optimality of the solution is compromised. We propose a novel tabu search (TS) algorithm, which considers routing and wavelength assignment jointly without increasing the computational complexity. The performance of the proposed TS algorithm is compared with the integer linear programming (ILP) method, which is known to solve the MWU to optimality. The results for both small and large networks show that our proposed TS algorithm works almost as well as the ILP solution and is much more computationally efficient.     

Preferred Link Based Distributed Adaptive Routing in Wavelength Routed Optical Network

As the WDM technology matures and the demand for bandwidth increases, dynamic provisioning of lightpaths at the WDM layer becomes an important and challenging problem. In this paper, we consider the problem of dynamic routing and wavelength assignment in wavelength-routed optical networks. The conventional approach to this problem is to select a route from a set of candidate routes, which has a common wavelength available on all the links of the route. In this paper, we propose a distributed algorithm which selects a route based on the state of the network (called preferred link approach). In this approach, a route is selected link by link based on a preference value given to each of the links. We propose three different heuristic functions for calculating the preference of the links, depending on the cost and congestion on the links. We evaluate our routing algorithm in terms of call acceptance ratio, cost of the path, hop length, and call setup time. Our experimental results suggest that our algorithm not only out performs the existing methods with respect to average call acceptance ratio, but, also improves the fairness among different hop connections, which is an important result in the case of WDM optical networks.     

Optimization of System Reliability via Redundancy and/or Design Considerations

The two traditional methods used in improving the reliability of a multi-stage system are examined. The first method is the creation of redundancy in system components, whereas the second method consists of overdesigning the system components. An Integrated Reliability Optimization Model that includes both of these two methods for improving the reliability of a system is presented. It is shown that under certain assumptions the integrated model reduces to a Redundancy Optimization Model and under certain other assumptions reduces to a Design Optimization Model. Several methods that have been previously suggested for obtaining solutions to the Integrated Optimization Model are reviewed and a generalized solution procedure for such a model is presented. This solution procedure involves the successive solution of two subproblems a number of times. The first subproblem is a design optimization problem that is solved by the Davidon-Fletcher-Powell optimization algorithm. The second subproblem is a reliability redundancy optimization problem that is solved with the heuristic approach of Aggarwal, et al.