An active learning genetic algorithm for integrated process planning and scheduling

In traditional approaches, process planning and scheduling are carried out sequentially, where scheduling is done separately after the process plan has been generated. However, the functions of these two systems are usually complementary. The traditional approach has become an obstacle to improve the productivity and responsiveness of the manufacturing system. If the two systems can be integrated more tightly, greater performance and higher productivity of a manufacturing system can be achieved. Therefore, the research on the integrated process planning and scheduling (IPPS) problem is necessary. In this paper, a new active learning genetic algorithm based method has been developed to facilitate the integration and optimization of these two systems. Experimental studies have been used to test the approach, and the comparisons have been made between this approach and some previous approaches to indicate the adaptability and superiority of the proposed approach. The experimental results show that the proposed approach is a promising and very effective method on the research of the IPPS problem.     

A two-stage-priority-rule-based algorithm for robust resource-constrained project scheduling

Traditionally, the resource-constrained project scheduling problem (RCPSP) is modeled as a static and deterministic problem and is solved with the objective of makespan minimization. However, many uncertainties, such as unpredictable increases in processing times caused by rework or supplier delays, random transportation and/or setup, may render the proposed solution obsolete. In this paper, we present a two-stage algorithm for robust resource-constrained project scheduling. The first stage of the algorithm solves the RCPSP for minimizing the makespan only using a priority-rule-based heuristic, namely an enhanced multi-pass random-biased serial schedule generation scheme. The problem is then similarly solved for maximizing the schedule robustness while considering the makespan obtained in the first stage as an acceptance threshold. Selection of the best schedule in this phase is based on one out of 12 alternative robustness predictive indicators formulated for the maximization purpose. Extensive simulation testing of the generated schedules provides strong evidence of the benefits of considering robustness of the schedules in addition to their makespans. For illustration purposes, for 10 problems from the well-known standard set J30, both robust and non-robust schedules are executed with a 10% duration increase that is applied to the same randomly picked 20% of the project activities. Over 1000 iterations per instance problem, the robust schedules display a shorter makespan in 55% of the times while the non-robust schedules are shown to be the best performing ones in only 6% of the times.     

Dealing with fuzziness in active mobile database systems

Current needs of industry required the development of advanced database models like active mobile database systems. An active mobile database system can be designed by incorporation of triggering rules into a mobile computing environment in which the users are able to access a collection of database services using mobile and non-mobile computers at any location. Fuzzy concepts are adapted to the field of databases in order to deal with ambiguous, uncertain data. Fuzziness comes into picture in active mobile databases especially with spatial queries on moving objects. Incorporating fuzziness into rules would also improve the effectiveness of active mobile databases as it provides much flexibility in defining rules for the supported application. In this paper we present some methods to adapt the concepts developed for fuzzy systems to active mobile databases.     

Analysis and optimization of active databases

We introduce a new formal semantics for active databases that relies on a transaction rewriting technique. A user-defined transaction, which is viewed here as a sequence of atomic database updates forming a semantic atomic unit, is translated by means of active rules into induced one(s). These transactions embody active rule semantics which can be either immediate or deferred. Rule semantics, confluence, equivalence and optimization are then formally investigated and characterized in a solid framework that naturally extends a known model for relational database transactions.     

Evolving processes and evolution schedulers for concurrent scheduling controls and parallel evolutionary computation

In this paper we present concepts of evolution scheduling, which will provide us with a better scheduling mechanism for competing processes in the ready-queue, the blocked-queue, the pc (priority control) queue, and the cs (client-and-server) queue. The evolution scheduling is acceptable for the soft real-time system and useful for its performance to achieve fair and effective scheduling. On the other hand, for those who pursue evolutionary computation, this is good news. It allows them to carry out natural competition in an easy way consistent with their operating system and programming language.     

A concurrent spectral-screening PCT algorithm for remote sensing applications

The paper presents a concurrent algorithm for remote sensing applications that provides significant performance and image quality enhancements over conventional uniprocessor principal component transform (PCT) techniques. The algorithm combines spectral angle classification, principal component transform, and human centered color mapping. It is evaluated from an image quality perspective using images collected with the hyper-spectral digital imagery collection experiment (HYDICE) sensor, an airborne imaging spectrometer. These images correspond to foliated scenes taken from an altitude of 2000–7500 m at wavelengths between 400 nm and 2.5 μm. The scenes contain mechanized vehicles sitting in open fields as well as under camouflage. The algorithm operates with close to linear speedup on shared memory multiprocessors and can be readily extended to operate on multiple, low-cost PC-style servers connected with high-performance networking. A simple analytical model is outlined that allows the impact on performance of practical, application-specific properties to be assessed. These properties include image resolution, number of spectral bands, increases in the number of processors, changes in processor technology, networking speeds and system clock rates.     

A distributed switch scheduling algorithm

The maximum weight matching algorithm is a high-performance scheduling algorithm for cross-bar switches. It is known that it performs optimally under heavy loads. However, its centralized nature and high computational complexity limit the algorithm’s applicability. This paper presents a randomized algorithm for distributed switch scheduling that is capable of delivering high throughput.     

A faster polynomial algorithm for 2-cyclic robotic scheduling

This paper addresses the 2-cyclic identical part scheduling in a no-wait robotic flowshop where exactly two parts enter and two parts leave the production line during each cycle. This problem was previously proved to be polynomially solvable in O(N⁸log N) time, where N is the number of tanks in the production line. This paper proposes an improved algorithm with reduced complexity O(N⁵log N).     

RM-Tool: A framework for discovering and evaluating association rules

Nowadays, there are a great number of both specific and general data mining tools available to carry out association rule mining. However, it is necessary to use several of these tools in order to obtain only the most interesting and useful rules for a given problem and dataset. To resolve this drawback, this paper describes a fully integrated framework to help in the discovery and evaluation of association rules. Using this tool, any data mining user can easily discover, filter, visualize, evaluate and compare rules by following a helpful and practical guided process described in this paper. The paper also explains the results obtained using a sample public dataset.     

基于规则引擎的通讯卫星应急任务调度

为降低调度规则变动对独占资源通讯卫星应急任务调度系统的影响,提高系统的灵活性、可扩展性及易维护性,提出一种基于规则引擎的独占资源通讯卫星应急任务调度方法,运用规则引擎技术实现调度规则与业务逻辑代码的解耦。通过研究独占资源通讯卫星应急任务调度策略,设计应急任务调度规则,结合Drools规则语法建立调度规则库。在Rete算法节点共享机制研究的基础上,设计基于权重的节点连接方法,改进推理网的构建过程,优化推理网的结构。实验结果表明,基于改进后的规则引擎的调度系统拥有更好的运行效率。