Physical distribution,logistics, supply chain management,and the material flow theory: a historical perspective

The evolution from physical distribution to logistics, and then to supply chain management has invoked many interesting studies in recent years. By applying the material flow theory that expands attributes of logistics to coordination of environment, economy, and society, this paper examines the evolution from a unique perspective: sustainable development. The purpose of this paper is to provide a summary review of the evolution of physical distribution, logistics, supply chain management, and their new stage-material flow theory. The paper aims at tracing how concepts and findings in physical distribution, logistics, supply chain management, and material flow have been developed and extended continually. The results show that the requirements for material flow services vary with the economic development level in different countries and that material flow service is required to be compatible with a country’s development stage. The paper concludes that the MF theory is the option for dealing with the overwhelming complexity of material flow systems in the global economy of the twenty-first century.     

Study of the miscibility and mechanical properties of NBR/HNBR blends

In this study, hydrogenated acrylonitrile butadiene rubber (HNBR, ZETPOL‐2010L) and nitrile butadiene rubber (NBR, NIPOL‐DN4555) were blended at different ratios in a Haake melt blender at 130°C. The HNBR and the NBR were of very similar acrylonitrile content and Mooney viscosity. The melt miscibility and solid‐state properties were investigated by rheological, thermal, and mechanical testing and scanning electron microscopy (SEM) techniques. The dynamic viscosity of the blends followed the log‐additivity rule, while the flow activation energy closely followed the inverse additivity rule. On the other hand, the storage modulus showed synergistic effects at all compositions, suggesting the presence of emulsion morphology at both ends of the composition range. For the 50/50 HNBR/NBR blend, the SEM micrographs suggest a uniform elongated structure. The thermal analysis showed the presence of two glass transitions, representing the pure components, at all blend ratios, suggesting the absence of segmental miscibility of the blends. The small‐strain mechanical properties such as tensile modulus and yield stress followed linear additivity. However, HNBR and HNBR‐rich blends were observed to strain harden at a rate higher than that of NBR. Induced crystallization of HNBR was suggested to be the reason for the strain hardening. The different rheological, thermal, and mechanical testing techniques agree in suggesting that the structurally similar HNBR and NBR are not thermodynamically miscible but mechanically compatible. Polym. Eng. Sci. 44:2346–2352, 2004. © 2004 Society of Plastics Engineers.     

Pareto-based evolutionary computational approach for wireless sensor placement

Wireless sensor networks (WSNs) have become increasingly appealing in recent years for the purpose of data acquisition, surveillance, event monitoring, etc. Optimal positioning of wireless sensor nodes is an important issue for small networks of relatively expensive sensing devices. For such networks, the placement problem requires that multiple objectives be met. These objectives are usually conflicting, e.g. achieving maximum coverage and maximum connectivity while minimizing the network energy cost. A flexible algorithm for sensor placement (FLEX) is presented that uses an evolutionary computational approach to solve this multiobjective sensor placement optimization problem when the number of sensor nodes is not fixed and the maximum number of nodes is not known a priori. FLEX starts with an initial population of simple WSNs and complexifies their topologies over generations. It keeps track of new genes through historical markings, which are used in later generations to assess two networks’ compatibility and also to align genes during crossover. It uses Pareto-dominance to approach Pareto-optimal layouts with respect to the objectives. Speciation is employed to aid the survival of gene innovations and facilitate networks to compete with similar networks. Elitism ensures that the best solutions are carried over to the next generation. The flexibility of the algorithm is illustrated by solving the device/node placement problem for different applications like facility surveillance, coverage with and without obstacles, preferential surveillance, and forming a clustering hierarchy.     

Dynamic Template Tracking and Recognition

In this paper we address the problem of tracking non-rigid objects whose local appearance and motion changes as a function of time. This class of objects includes dynamic textures such as steam, fire, smoke, water, etc., as well as articulated objects such as humans performing various actions. We model the temporal evolution of the object’s appearance/motion using a linear dynamical system. We learn such models from sample videos and use them as dynamic templates for tracking objects in novel videos. We pose the problem of tracking a dynamic non-rigid object in the current frame as a maximum a-posteriori estimate of the location of the object and the latent state of the dynamical system, given the current image features and the best estimate of the state in the previous frame. The advantage of our approach is that we can specify a-priori the type of texture to be tracked in the scene by using previously trained models for the dynamics of these textures. Our framework naturally generalizes common tracking methods such as SSD and kernel-based tracking from static templates to dynamic templates. We test our algorithm on synthetic as well as real examples of dynamic textures and show that our simple dynamics-based trackers perform at par if not better than the state-of-the-art. Since our approach is general and applicable to any image feature, we also apply it to the problem of human action tracking and build action-specific optical flow trackers that perform better than the state-of-the-art when tracking a human performing a particular action. Finally, since our approach is generative, we can use a-priori trained trackers for different texture or action classes to simultaneously track and recognize the texture or action in the video.     

Consolidating Multi-Criteria Visible-light Personal Network Coordinator Selection Process in Attocells

Wireless Personal Communications - Visible Light Communication (IEEE 802.15.7) implements a centralized topology using a visible-light personal network coordinator (VPNC) for network access and...     

Multi scale entropy based adaptive fuzzy contrast image enhancement for crowd images

Multimedia Tools and Applications - Contrast enhancement is a very important issue in image processing, pattern recognition and computer vision. Fuzzy logic based techniques perform enhancement...     

Thermodynamic Properties of Liquid 3He-4He Mixtures Between 0.15 K and 1.8 K

Thermodynamic property relations for liquid ³He-⁴He mixtures at saturated pressure based on experimental measurements of the specific heat are determined. The relations are valid over the entire concentration range and for temperatures from 0.15 K to 1.8 K. Thermodynamic properties are first determined in the two-phase region, and then extended to the single-phase He-II and He-I regions. The results are in good agreement with some other ³He-⁴He mixture property data, though the scarcity of experimental data in large parts of the region of interest precludes a more thorough comparison. We derive some thermodynamic quantities that may be useful to the analysis of heat exchangers and throttles with superfluid ³He-⁴He flows. We also discuss how these properties can be extended to higher pressures.     

Internet of things and cloud computing‐based energy management system for demand side management in smart grid

A smart grid is an electricity network, which deals with electronic power conditioning and control of production, transmission, and distribution of electrical power by employing digital communication technologies to monitor and manage local changes in electricity usage. In the traditional power grid, energy consumers remain oblivious to their power consumption patterns, resulting in wasted energy as well as money. This issue is severely pronounced in the developing countries where there is a huge gap between demand and supply, resulting in frequent power outages and load‐shedding. For electrical energy savings, the smart grid employs demand side management (DSM), which refers to adaptation in consumer's demand for energy through various approaches such as financial incentives and awareness. The DSM in future smart grid must exploit automated energy management systems (EMS) built upon the state‐of‐the‐art technologies such as the internet of things (IoT) and cloud and/or fog computing. In this paper, we present the architecture framework, design, and implementation of an IoT and cloud computing‐based EMS, which generates load profile of consumer to be accessed remotely by utility company or by the consumer. The consumers' load profiles enable utility companies to regulate and disseminate their incentives and incite the consumers to adapt their energy consumption. Our designed EMS is implemented on a Project Circuit Board (PCB) to be easily installed at the consumer premises where it performs the following tasks: (a) monitors energy consumption of electrical appliances by means of our designed current and voltage sensors, (b) uploads sensed data to Google Firebase cloud over many‐to‐many IoT communication protocol Message Queuing Telemetry Transport (MQTT) where consumer's load profile is generated, which can be accessed via a web portal. These load profiles serve as input for implementing the various DSM approaches. Our results demonstrate generated load profiles of consumer load in terms of current, voltage, energy, and power accessible via a web portal.     

An inter-temporal resource emergency management model

One of the most important ways for promoting the service level during emergency management in public sectors is through enhancing management of strategic resources. From the perspective of vertical integration of operational process in emergency management, emergency resources can be classified as those resources used during the response period as well as those resources that are consumed during the recovery period. An inter-temporal integrated single-period resource model for solving optimal order quantity is proposed that meets the characteristics during the demand for the recovery resources depending on the stock shortage of the response resources. In the light of the classification of the dependent relationship of the two kinds of resources, namely deterministic or stochastic, the research investigates the analytical properties of the model, based on which, a genetic algorithm-based simulation approach is proposed. Finally, a real case with numerical example is provided to assess and validate our model, as well as managerial insights are obtained through key parameters sensitivity analysis.     

Application of genetic algorithms in production and operations management: a review

Over the last decade, there has been a rapid growth of the use of genetic algorithms in the various areas of production and operations management. This paper provides a review of genetic algorithms research published in twenty-one major production and operations management journals from 1990–2001. More specifically, it identifies research trends and publication outlets of genetic algorithms applications. Our findings show that there are only a handful of production and operations management areas to which genetic algorithms have been applied as the solution approach. Furthermore, we recognize and discuss potential research areas and outlets in which researchers may target their work as well as the need for top ranked POM journals to consider publishing genetic algorithms related papers.