Remanufacturability pre-evaluation is an important step at used products consolidation stage. In order to provide a quick sorting speed and reliable evaluation with a long lifespan, radio frequency identification (RFID) system is often employed in practice. A major factor that influences RFID system's reliability is the inaccuracy arising from missing data and reading errors, which are magnified to produce deleterious effects on reliability. A good yet simple solution is to add more redundant components (i.e., RFID readers) to smooth the RFID system's reliability. In this paper, we first formulate our focal scenario as a reliability-redundancy allocation problem (RRAP). Then, one of the recently developed swarm intelligence approach called teaching - learning-based optimization (TLBO), which is based on the effect of the influence of a teacher on the output of learners in a class, is employed to address our focal problem. Simulation results suggest that the proposed TLBO is a viable optimization technique in dealing with the optimization of RFID system's reliability.

A number of cuckoo-inspired algorithms have been introduced recently in the literature. These procedures are based on the parasitic behavior observed in some cuckoo species, in combination with the Lévy flight behavior discovered in some birds and fruit flies. Thus far, many different applications have been reported. In this paper we provide a quick overview of the current stage of cuckoo-inspired algorithms and their various applications.

Radio frequency identification (RFID) research based upon computational intelligence (CI) is currently attracting a lot effort from the research community. Characteristics of CI, such as adaptation, fault tolerance, high computational speed and error resilience, fit the requirements of RFID research. In this article we provide an overview of the research progress in applying CI methods to various problems within RFID research area. The findings of this review should be a good source for anyone who is interested in the application of CI approaches to RFID and its corresponding fields.

In this work, we first propose an NP-hard combinatorial problem, that is, the storage and retrieve (S/R) machine travel path optimization for batch order picking (BOP). Successful solving this problem is valuable to many application areas such inventory items in logistics and work-in-process storage in manufacturing systems. And then, we investigate the feasibility of using ant colony optimization (ACO) meta-heuristics to address the proposed problem. Simulation tests are executed separately based on two ACO algorithms. Finally, the S/R machine operating performance measure index such as total travel distance and total travel time are employed to evaluate the experimental results achieved by different ACO algorithms. Experimental case study demonstrates the effectiveness and applicability of the selected ACO approaches to our proposed BOP problem.

In this article, we attempt to solve cellular manufacturing system (CMS) scheduling problem from group technology point of view. Since the operations of a job in a CMS can be performed on more than one machine within a cell, the scheduling problem of the CMS is always considered as a computationally hard problem. Two approaches called fuzzy logic and fuzzy MAX-MIN ant system are employed respectively. Under the consideration of multiple criteria, the performance of different manufacturing cells in a CMS is improved by means of proposed methodologies.

Spatial dissemination is a specific form of information dissemination that enables mobile users to send information to other mobile users who are or will appear at a specific location (a user-defined region). Such geo-messaging services are on the rise; they typically are built upon centralized solutions and require users to have reliable access to a stable backend infrastructure for storing and communicating content. In this paper, we develop a distributed solution to spatial dissemination, that can work without the need for such an infrastructure. Our solution utilizes the concepts from disruption-tolerant networking to build a flexible/best-effort service that leverages the intermittent ad-hoc connectivity between users. We propose Sticker, a spatial dissemination protocol that aims to maximize delivery reliability without incurring significant storage/transmission overheads. Sticker employs the store-carry-and- forward model, and strives to optimize dissemination performance by addressing three sub-problems - replication, forwarding and purging. Our experiments show that, Sticker achieves delivery ratios that are close to the maximum possible values; as compared to existing techniques, it either cuts down storage/transmission overheads by over 50%, or greatly enhances both delivery reliability and storage efficiency.

In this work, we propose the bridge crane travel path optimization for batch order picking (BOP). Successful solving this problem is valuable to many application areas in manufacturing systems. We then investigate the feasibility of using ant colony optimization (ACO) meta-heuristics to address the proposed problem. Simulation tests are executed separately based on two ACO algorithms. Finally, the bridge crane operating performance measure index is employed to evaluate the experimental results achieved by different ACO algorithms. Experimental case study demonstrates the effectiveness and applicability of the selected ACO approaches to our proposed BOP problem for bridge crane.

In manufacturing environment, an automated guided vehicle (AGV) system is composed of a set of driver-less vehicles that transport goods and materials between distinct workstations and storage locations of shops. In soft computing area, ant algorithms are a series of population-based approaches inspired by various behaviors of real ant colonies. During the last two decades, ant algorithms have achieved a great success in solving many combinatorial optimization problems. In this article we make an attempt to study the feasibility of applying ant algorithms to different problems encountered in AGV system design and control. By making use of ant algorithms' strengths, we hope to provide the readers with alternative options for solving conventional AGV system design and control problems, as well as to point out some new directions for AGV system research.

In this paper we consider an alternative approach to the problem of infusing local shop floor control decisions with more global performance information. The approach proposed is that of using artificial ant colonies to decentralize and autonomize the shop floor routing. The proposed ant stigmergy shop floor control (ASSFC) has two functional levels: a virtual shop floor level in which ant agents explore optimal processing route stochastically, and a physical shop floor in which intelligent work pieces (IWPs) deterministically exploit the best paths that have been detected by their corresponding ant agents on virtual shop floor. The simple indirect communication and the robust adaptability to disturbances make the ASSFC more suitable for intelligent product oriented manufacturing system than tradition shop floor control.

In this work, the authors design and prototype PassItOn, a fully distributed opportunistic messaging system. Our goal is to build up a proof-of-concept platform on real mobile devices, and thus show the feasibility and potentials of utilizing human movements for dissemination applications. Meanwhile, we seek to shed lights on the design, implementation and deployment issues in building such systems, and thus stimulate new ideas and perspectives on addressing these issues. Moreover, we aim to offer a real testbed on which new mechanisms, protocols and use cases can be tested and evaluated.

This paper deals with the problem of reliable and fast broadcast of mission-critical data with rich content over ad hoc networks. Existing approaches to dissemination reliability often assume network size knowledge, or that receivers know about the dissemination in advance. Without making similar assumptions, we propose a distinct approach which accommodates the varying reliability needs of applications. We develop the RADcast (reliable application data broadcast) protocol as an integration of two components: (a) Peddler, which ensures that receivers obtain the dissemination metadata, and (b) Pryer, which delivers the actual data to dissemination-aware receivers. We indicate how reliability guarantees/performance tradeoffs can be achieved by a careful instantiation of Peddler and Pryer. We implement RADcast on mobile devices inside a middleware and determine its feasibility. Furthermore, through extensive simulations, we show that RADcast achieves desired reliability in all cases, and performs consistently under varying network conditions and device mobilities. As compared to existing approaches, RADcast either incurs significantly lower latency/message overhead, or reduces latency by 50% with a tradeoff in message overhead.

The recent years have witnessed an explosive amount of user-generated contents, along with users' soaring demands on sharing these contents. An evidence of that is the blossom of hundreds of sharing sites on the web, among which the most famous are MySpace [1], Flickr [2], YouTube [3], Facebook [4] and LinkedIn [5]. Through these sites, users upload the contents they generate (blogs, pictures, videos, resumes, etc.), and make them available to their friends or the general public. A common characteristic of such Internet-based content sharing scenarios is that all contents are hosted by third-party servers on the Internet, and sharing happens when content consumers (regardless of where they are) access the hosting web sites.

Application data broadcast in ad hoc networks is an important primitive that has received little systematic research - the main focus of prior research being on control data broadcast. In this paper, we show why control data broadcast and even multicast techniques are insufficient for reliable application data broadcast; in fact their reliability degrades sharply with increasing application data size. We discover the root cause of this to be IP fragmenting the application data but not providing good reliability control on the fragments. We hence propose READ (reliable and efficient application-data dissemination), a protocol based on higher-layer fragmentation with fragment-level reliability control. READ splits a data packet into fragments, and disseminates them separately at dynamically adaptive intervals. Receivers piggyback implicit NACKs when propagating the fragments, and retrieve missing fragments from neighbors. Through experiments, we show that READ consistently achieves high delivery ratio and short latency, outperforming all other examined protocols.

Online advertising market is becoming a popular area of academic research. Among other types of advertising, search engine advertising is leading the growth in terms of revenue. In general, there are two types of search engine advertising: paid placement and search engine optimization (SEO). This study aims to analyze the condition under which SEO exist and further, its impact on the advertising market. With an analytical model, several interesting insights are generated. The results of the study fill the gap of SEO in academic research and help managers in online advertising make informed advertising decisions.

In this paper, we explore a new form of dissemination called Flash Dissemination that involves dissemination of fixed, rich information to a large number of recipients in as short a time as possible. Key characteristics of Flash Dissemination include unpredictability in its need, scalability to large number of recipients and autonomic performance in highly heterogenous and failureprone environments. Previous work either addresses large content delivery in heterogenous networks or fault-tolerant dissemination of (streaming) events. We investigate a peer-based approach using foundations from broadcast networks, gossip theory and random networks. In this paper, we propose CREW (Concurrent Random Expanding Walkers), a scalable, lightweight, and autonomic gossip-based protocol. CREW is also explicitly designed to maximize the speed of dissemination using adaptive and intelligent intra and inter node concurrency. We implemented CREW on top of a scalable middleware environment and compared it to optimized implementations of popular gossip and peer-based systems. Our experiments show that CREW outperforms both traditional gossip and current large content dissemination systems, across a wide range of comparative metrics, even though its design is counterintuitive from a systems perspective.

In future generation networks, various access technologies, such as Wi-Fi, Bluetooth, GPRS and UMTS, etc., are simultaneously available to mobile devices. They vary in characteristics (communication range, power consumption, security, etc.) and QoS parameters (bandwidth, delay, etc.) The notion of always best connected (ABC) enables people to run applications over the most efficient combination of access technologies with continuous connectivity. Access selection is the key functional block in ABC solutions, as it chooses the most suitable access networks for application traffic flows. However, it is important that access selection decisions be dynamically made, minimizing the power consumption on mobile devices while satisfying QoS requirements and user/application preferences. In this paper, we model the problem of multi-constraint dynamic access selection (MCDAS) as a variant of bin packing problem. A series of approximation algorithms derived from the first fit decreasing (FFD) algorithm are proposed for finding near-optimal solutions. Simulation studies show that the algorithms we propose gradually improve performance towards quasi-optimal solutions in terms of power consumption and preference satisfaction.