A new algorithm based CSP framework for RFID network planning

International audienceThe huge growth of industrial society requires the deployment of radio frequency identification networks on a large scale. This necessitates the installation of a large number of radio frequency identification components (readers, tags, middleware and others). As a consequence, the cost and complexity of networks are increasing due to the large number of readers to be installed. Finding the optimal number, placement and parameters of readers to provide a high quality of service for radio frequency identification systems is a critical problem. A good planning affords a basic need for radio frequency identification networks, such as coverage, load balance and interference between readers. This problem is famous in the literature as a radio frequency identification network planning problem. All the proposed approaches in the literature have been based on meta-heuristics. In this paper, we design a new algorithm, called the RNP-CSP algorithm based on the constraint satisfaction problem framework to solve the radio frequency identification network planning problem. The performance evaluation shows that the RNP-CSP algorithm is more efficient than PS 2 O , GPSO and VNPSO-RNP

Robust modeling and planning of radio-frequency identification network in logistics under uncertainties

To realize higher coverage rate, lower reading interference, and cost efficiency of radio-frequency identification networkin logistics under uncertainties, a novel robust radio-frequency identification network planning model is built and arobust particle swarm optimization is proposed. In radio-frequency identification network planning model, coverage isestablished by referring the probabilistic sensing model of sensor with uncertain sensing range; reading interference iscalculated by concentric map–based Monte Carlo method; cost efficiency is described with the quantity of readers. Inrobust particle swarm optimization, a sampling method, the sampling size of which varies with iterations, is put forwardto improve the robustness of robust particle swarm optimization within limited sampling size. In particular, the exploita-tion speed in the prophase of robust particle swarm optimization is quickened by smaller expected sampling size; theexploitation precision in the anaphase of robust particle swarm optimization is ensured by larger expected sampling size.Simulation results show that, compared with the other three methods, the planning solution obtained by this work ismore conducive to enhance the coverage rate and reduce interference and cost.info:eu-repo/semantics/publishedVersio

Soft computing for hazardous waste routing in Malaysia: a review

Nowadays, a significant number of researchers are focusing on utilizing soft computing approaches to address the issue of scheduling in applications concerned with hazardous waste management. In Malaysia, there is thoughtless awareness of the management of hazardous waste, even though the production of wastes in hazardous domains at the industrial and domestic levels has been rising lately. According to previous research findings, the location routing problem (LRP) can be designated as one of the models closer to the actual situation, evaluating the most suitable and optimal location for establishing facilities and utilizing transportation for pick-up and distribution. Recent studies have focused on enhancing the LRP model, and its methodologies approach to solve the waste management problem in hazardous domains. In this paper, a comprehensive review of the better promising and practicable mathematical model of LRP and its methodology approach is discussed, as well as an analysis of the publishing pattern and the trend of research over the preceding five years and more, as retrieved from the web of science (WoS) database. In conclusion, this research is significant in ensuring the effectiveness of reliable mathematical model development and suitable methodologies in the future for solving hazardous waste management problems

Multi‑Agent Foraging: state‑of‑the‑art and research challenges

International audienceThe foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of robots has to search and transport objects to specific storage point(s). In this paper, we investigate the Multi-Agent Foraging (MAF) problem from several perspectives that we analyze in depth. First, we define the Foraging Problem according to literature definitions. Then we analyze previously proposed taxonomies, and propose a new foraging taxonomy characterized by four principal axes: Environment, Collective, Strategy and Simulation, summarize related foraging works and classify them through our new foraging taxonomy. Then, we discuss the real implementation of MAF and present a comparison between some related foraging works considering important features that show extensibility, reliability and scalability of MAF systems. Finally we present and discuss recent trends in this field, emphasizing the various challenges that could enhance the existing MAF solutions and make them realistic

Design and optimization of an RFID-enabled passport tracking system

The implementation of RFID technology has been subject to ever-increasing popularity in relation to the traceability of products as one of the most cutting edge technologies. Implementing such a technology leads to an increase in the visibility management of products. Notwithstanding this, RFID communication performance is potentially affected by interference between the RFID devices. It is also subject to additional costs in investment that should be taken into account. Consequently, seeking a cost-effective design with a desired communication performance for RFID-enabled systems has become a key factor in order to be competitive in today’s markets. This study presents a cost and performance-effective design for a proposed RFID-enabled passport tracking system through the development of a multi-objective model that takes in account economic, performance and social criteria. The developed model is aimed at solving the design problem by (i) allocating the optimal numbers of related facilities that should be established and (ii) obtaining trade-offs among three objectives: minimising implementation and operational costs; minimising RFID reader interference; and maximising the social impact measured in the number of created jobs. To come closer to real design in terms of considering the uncertain parameters, the developed multi-objective model was developed in terms of a fuzzy multi-objective model (FMOM). To solve the fuzzy multi-objective optimization problem, two solution methods were used and a decision-making method was employed to select the final trade-off solution. A case study was applied to examine the applicability of the developed model and the proposed solution methods