A Literature Survey on Reverse Logistics of End of Life Vehicles

Today, recycling of used products and materials has become an increasingly important sector. Mankind, who uses the natural resources unconsciously, has found ways to improve recycling techniques when they realized that resources are becoming increasingly depleted. In the automotive sector, which is one of the largest sectors in the world, natural resources are being used to a great extent. According to the statistics, in 2009, approximately 9 million end-of-life vehicles (ELV) in Europe were withdrawn from traffic. Undoubtedly, this figure shows the necessity and importance of designing reverse logistics network optimized for ELVs. This research aims to determine the gaps in the literature by examining the studies made from the past to the present day in the field of reverse logistic network design for vehicles that have completed their life cycle. In this article, the studies in the fieldare analyzed based on objective functions, decision variables, constraint handling metod, optimization methods used. Considered studies in this work are clustered using a special artificial neural network tool, Self-Organizing Maps (SOM), and the frequencies of the characteristics are shown in the study. This study, which includes a review of the literature and a clustering of studies, aims to guidethe researchers working on the design of rreverse logistics networks for ELVs

Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms. A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0

A phase-locked frequency divide-by-3 optical parametric oscillator

Accurate phase-locked 3:1 division of an optical frequency was achieved, by using a continuous-wave (cw) doubly resonant optical parametric oscillator. A fractional frequency stability of 2*10^(-17) of the division process has been achieved for 100s integration time. The technique developed in this work can be generalized to the accurate phase and frequency control of any cw optical parametric oscillator.Comment: 4 pages, 5 figures in a postscript file. To appear in a special issue of IEEE Trans. Instr. & Meas., paper FRIA-2 presented at CPEM'2000 conference, Sydney, May 200

Mathematical optimization and learning models to address uncertainties and sustainability of supply chain management

As concerns about climate change, biodiversity loss, and pollution have become more widespread, new worldwide challenges deal with the protection of the environment and the conservation of natural resources. Thus, in order to empower sustainability and circular economy ambitions, the world has shifted to embrace sustainable practices and policies. This is carried out, primarily, through the implementation of sustainable business practices and increased investments in green technology. Advanced information systems, digital technologies and mathematical models are required to respond to the demanding targets of the sustainability paradigm. This trend is expanding with the growing interest in production and services sustainability in order to achieve economic growth and development while preventing their negative impact on the environment. A significant step forward in this direction is enabled by Supply Chain Management (SCM) practices that exploit mathematical and statistical modeling to better support decisions affecting both profitability and sustainability targets. Indeed, these targets should not be approached as competing goals, but rather addressed simultaneously within a comprehensive vision that responds adequately to both of them. Accordingly, Green Supply Chain Management (GSCM) can achieve its goals through innovative management approaches that consider sustainable efficiency and profitability to be clearly linked by the savings that result from applying optimization techniques. To confirm the above, there is a growing trend of applying mathematical optimization models for enhancing decision-making in pursuit of both environmental and profit performance. Indeed, GSCM takes into account many decision problems, such as facility location, capacity allocation, production planning and vehicle routing. Besides sustainability, uncertainty is another critical issue in Supply Chain Management (SCM). Considering a deterministic approach would definitely fail to provide concrete decision support when modeling those kinds of scenarios. According to various hypothesis and strategies, uncertainties can be addressed by exploiting several modeling approaches arising from statistics, statistical learning and mathematical programming. While statistical and learning models accounts variability by definition, Robust Optimization (RO) is a particular modeling approach that is commonly applied in solving mathematical programming problems where a certain set of parameters are subject to uncertainty. In this dissertation, mathematical and learning models are exploited according to different approaches and models combinations, providing new formulations and frameworks to address strategic and operational problems of GSCM under uncertainty. All models and frameworks presented in this dissertation are tested and validated on real-case instances

Assessing the Possibilities of Integrating Ports into the Circular Economy

Ports are very important international hubs within transport networks where various industrial and logistical activities are performed. However, by use of the closed-loop mechanism the circular economy would reduce the need for imported primary raw materials because it would treat waste as a secondary raw material. This would greatly affect the way in which ports operate because primary raw materials make up most of their cargo volume. Therefore, it is essential to evaluate the possibilities of integrating ports into the circular economy. This paper aims to use the descriptive research method to assess ports on their role in the circular economy, and based on the SWOT analysis has highlighted the key elements of circular analysis of waste streams to objectively evaluate the closed - loop mechanism. The conclusion is that due to the decline in primary raw material volumes within the circular economy, ports would therefore become centers of innovation in order to attract as many new industries as possible within their sector. The circular business model of ports would also require renewed cooperation from key actors within the port sector, the most important being the port authority, industrial clusters and academic institutions

Link-time smart card code hardening

This paper presents a feasibility study to protect smart card software against fault-injection attacks by means of link-time code rewriting. This approach avoids the drawbacks of source code hardening, avoids the need for manual assembly writing, and is applicable in conjunction with closed third-party compilers. We implemented a range of cookbook code hardening recipes in a prototype link-time rewriter and evaluate their coverage and associated overhead to conclude that this approach is promising. We demonstrate that the overhead of using an automated link-time approach is not significantly higher than what can be obtained with compile-time hardening or with manual hardening of compiler-generated assembly code

Visual analytics for supply network management: system design and evaluation

We propose a visual analytic system to augment and enhance decision-making processes of supply chain managers. Several design requirements drive the development of our integrated architecture and lead to three primary capabilities of our system prototype. First, a visual analytic system must integrate various relevant views and perspectives that highlight different structural aspects of a supply network. Second, the system must deliver required information on-demand and update the visual representation via user-initiated interactions. Third, the system must provide both descriptive and predictive analytic functions for managers to gain contingency intelligence. Based on these capabilities we implement an interactive web-based visual analytic system. Our system enables managers to interactively apply visual encodings based on different node and edge attributes to facilitate mental map matching between abstract attributes and visual elements. Grounded in cognitive fit theory, we demonstrate that an interactive visual system that dynamically adjusts visual representations to the decision environment can significantly enhance decision-making processes in a supply network setting. We conduct multi-stage evaluation sessions with prototypical users that collectively confirm the value of our system. Our results indicate a positive reaction to our system. We conclude with implications and future research opportunities.The authors would like to thank the participants of the 2015 Businessvis Workshop at IEEE VIS, Prof. Benoit Montreuil, and Dr. Driss Hakimi for their valuable feedback on an earlier version of the software; Prof. Manpreet Hora for assisting with and Georgia Tech graduate students for participating in the evaluation sessions; and the two anonymous reviewers for their detailed comments and suggestions. The study was in part supported by the Tennenbaum Institute at Georgia Tech Award # K9305. (K9305 - Tennenbaum Institute at Georgia Tech Award)Accepted manuscrip

Baseband analog front-end and digital back-end for reconfigurable multi-standard terminals

Multimedia applications are driving wireless network operators to add high-speed data services such as Edge (E-GPRS), WCDMA (UMTS) and WLAN (IEEE 802.11a,b,g) to the existing GSM network. This creates the need for multi-mode cellular handsets that support a wide range of communication standards, each with a different RF frequency, signal bandwidth, modulation scheme etc. This in turn generates several design challenges for the analog and digital building blocks of the physical layer. In addition to the above-mentioned protocols, mobile devices often include Bluetooth, GPS, FM-radio and TV services that can work concurrently with data and voice communication. Multi-mode, multi-band, and multi-standard mobile terminals must satisfy all these different requirements. Sharing and/or switching transceiver building blocks in these handsets is mandatory in order to extend battery life and/or reduce cost. Only adaptive circuits that are able to reconfigure themselves within the handover time can meet the design requirements of a single receiver or transmitter covering all the different standards while ensuring seamless inter-interoperability. This paper presents analog and digital base-band circuits that are able to support GSM (with Edge), WCDMA (UMTS), WLAN and Bluetooth using reconfigurable building blocks. The blocks can trade off power consumption for performance on the fly, depending on the standard to be supported and the required QoS (Quality of Service) leve