Signal shaper with a distributed delay: Spectral analysis and design

Input shapers with time delays have proved useful in many applications related to controls for various oscillatory devices, for example flexible manipulators and cranes. In the paper, a novel approach for designing a zero-vibration signal shaper based on equally distributed delay is proposed. The parameter assessment of the shaper is based on the spectral approach. Various characteristics of the shaper are analyzed and compared with the classical zero-vibration shaper with a lumped delay. The analysis shows that the novel shaper is a slower, but more robust alternative to the classical shaper. Besides, the discrete implementation of the shaper is proposed and tested. It includes zero placement based parameter adjustment with the objective to preserve full compensation of the oscillatory mode by the discrete algorithm.     

Value driven load balancing

To date, the study of dispatching or load balancing in server farms has primarily focused on the minimization of response time. Server farms are typically modeled by a front-end router that employs a dispatching policy to route jobs to one of several servers, with each server scheduling all the jobs in its queue via Processor-Sharing. However, the common assumption has been that all jobs are equally important or valuable, in that they are equally sensitive to delay. Our work departs from this assumption: we model each arrival as having a randomly distributed value parameter, independent of the arrival’s service requirement (job size). Given such value heterogeneity, the correct metric is no longer the minimization or response time, but rather, the minimization of value-weighted response time. In this context, we ask “what is a good dispatching policy to minimize the value-weighted response time metric?” We propose a number of new dispatching policies that are motivated by the goal of minimizing the value-weighted response time. Via a combination of exact analysis, asymptotic analysis, and simulation, we are able to deduce many unexpected results regarding dispatching.     

Capacity allocation problem in flexible manufacturing systems: branch and bound based approaches

This study considers an operation assignment and capacity allocation problem that arises in flexible manufacturing systems. The machines have limited time and tool magazine capacities and the available tools are limited. Our objective is to maximise total weight of assigned operations. We develop a branch and bound algorithm that finds the optimal solutions and a beam search algorithm that finds high quality solutions in polynomial time.     

Regression trees approach for flow-time prediction in wafer manufacturing processes using constraint-based genetic algorithm

Understanding the factors associated with the flow-time of wafer production is crucial for workflow design and analysis in wafer fabrication factories. Owing to wafer fabrication complexity, the traditional human approach to assigning the due-date is imprecise and prone to failure, especially when the shop status is dynamically changing. Therefore, assigning a due-date to each customer order becomes a challenge to production planning. The paper proposes a constraint-based genetic algorithm approach to determine the flow-time. The flow-time prediction model was constructed and compared with other approaches. Better computational effectiveness and prediction results from the constraint-based genetic algorithm are demonstrated using experimental data from a wafer-manufacturing factory.     

A new algorithm for stable assignments

A new algorithm for solving stable assignment problems is presented. By using a weight table, the overall situation of a problem is considered. The algorithm can deal with not only the one-to-one type assignments but also multi-function assignments for both equal and unequal sets. Some examples are given to illustrate the algorithm.     

A weight-aware channel assignment algorithm for mobile multicast in wireless mesh networks

Wireless mesh networks (WMNs) are one of key technologies for next generation wireless networks. In this paper, we propose a heuristic channel assignment algorithm with weight awareness to support mobile multicast in WMNs. To enhance network throughput, our algorithm is based on the path forwarding weight to perform channel assignment. In addition to non-overlapping channels, partially-overlapping channels are also used in channel assignment. To fully exploit all available channels in channel assignment, we devise a new channel selection metric to consider the channel separation and the distance between nodes. In mobile multicast, the multicast tree structure cannot be fixed due to receiver (multicast member) mobility. The change of the multicast tree structure will result in channel re-assignment. The proposed algorithm is based on a critical-event driven manner to reduce the times of channel re-assignment as much as possible. Finally, we perform simulation experiments to show the effectiveness of the proposed channel assignment algorithm.     

Reframing Pinterest: Information Literacy for Interior Design Students

This article discusses providing information literacy to interior design students, specifically how over a few years, and several assignment iterations, we moved from a more traditional one-shot library instruction session to a creative Association of College and Research Libraries Framework-inspired assignment using Pinterest as the medium for the students' professionally relevant research project. The outcome of our instruction relied on flexible assignment planning, collaboration as faculty and librarians changed roles, and a focus on information literacy skills needed by professional designers.     

Application of metaheuristics-based clustering algorithm to item assignment in a synchronized zone order picking system

Warehousing management policy is a crucial issue in logistic management. It must be managed effectively and efficiently to reduce the production cost as well as the customer satisfaction. Synchronized zoning system is a warehousing management policy which aims to increase the warehouse utilization and customer satisfaction by reducing the customer waiting time. This policy divides a warehouse into several zones where each zone has its own picker who can work simultaneously. Herein, item assignment plays an important role since it influences the order processing performance. This study proposes an application of metaheuristic algorithms, namely particle swarm optimization (PSO) and genetic algorithm (GA), for item assignment in synchronized zoning system. The original PSO and GA algorithms are modified so that it is suitable for solving item assignment problem. The datasets with different sizes are used for method validation. Results obtained by PSO and GA are then compared with the result of an existing algorithm. The experimental results showed that PSO and GA can perform better than the existing algorithm. These results also show that PSO has better performance than GA, especially for bigger problems. It proves that item assignment policy obtained by PSO and GA has higher utilization rates than the existing algorithm.     

Two fault‐tolerant control problems for multiple‐integrator networks

The paper considers a network of agents with multiple‐integrator internal dynamics, which share partial information on their states according to an arbitrary topology. For this system, two control problems are addressed and solved. The first consists in assigning the dominant closed‐loop poles. The second consists in achieving a specified consensus with arbitrary dominant dynamics. In both cases, the regulator is required to be decentralized, and the controlled network has to result tolerant with respect to faults in the communication apparatuses of the agents. Copyright © 2015 John Wiley & Sons, Ltd.