Dynamics in Logistics

Cyber-physical systems are small yet powerful systems which are embedded into their environment, adapting to its changes and at the same controlling it, and often operating autonomously. These systems have reached a level of complexity that opens up new application areas, but at the same time strains the existing design flows in system development. To ameliorate this problem, we propose a novel design flow for cyber-physical systems by adapting model-based specification and refinement methods known from software development. The design flow allows to start with a system specification and its essential properties at a high level of abstraction, and gradually refines it down to an electronic system level. Properties of higher levels can be inherited during refinements to lower levels by relying on local proof obligations only, which results in a design flow capable to keep up with the incre asing complexity of cyber-physical systems.

One of the important issues in container terminals for the efficient operation of a rail-based transport system, called “flat car system (FCS),” is how to determine the route and travel schedule of each flat car (FC). It is assumed that a flat car may have to reserve multiple resources simultaneously at a moment during its travel. Example of the resources are transfer points (TPs) and intersections (ISs) on guide-path network. A travel-scheduling algorithm based on Dijkstra’s algorithm is suggested using the concept of time-windows.

The term “synchronization” in manufacturing refers to the provision of the right components to the subsequent production steps at the right moment in time. It is still unclear how manufacturing system characteristics impact synchronization. Thus, the purpose of this paper is to investigate the effect of manufacturing systems’ characteristics on the emergence of logistics synchronization in them. We conduct a discrete-event simulation study to examine the effect of three system characteristics: (1) material flow network architecture, (2) work content variation, and (3) order arrival pattern. Our findings suggest that the material flow network architecture and the work content variation are related to logistics synchronization. Linear manufacturing systems with stable processing times such as flow shops operate at high logistics synchronization levels, while highly connected systems with high variability of processing times such as job shops exhibit lower synchronization levels.

The paper identifies the basic technology and functional requirements of a cyber-physical system to control human–robot collaboration in an industrial environment. The paper defines the collaboration grading of human–robot co-working environment based on the prevailing safety concepts in workspace sharing. Detailed requirements are generated for each interaction mode and few collaboration indices are established. Different indices are found to be useful for the purpose of categorization of collaboration levels. A specific case is discussed later for a detailed Cyber-Physical System solution in a smart production or logistical context. The paper ends with a general guideline that is formulated to cater for various industrial level human–robot collaborative scenarios. An important aspect of the collaboration guideline is a sensor catalogue to meet cyber-physical system design requirements.

Trust is an essential factor for successful resource sharing in logistics. To build and sustain trust among collaborating partners in logistics requires, amongst others, conceptualizing on various aspects constituting underlying mistrusts. The conception is achieved by setting up a framework describing trust-based collaborative interactions of these partnering entities, referred to as agents. This research establishes a trust framework addressing agents’ trustworthy interactions and thus aims at overcoming a knowledge gap identified in the literature. The framework depicts trust-based interactions concentrating to sharing of vehicle capacities. The trust framework is conceived on a foundation of theoretical body of knowledge in the literature. It engages knowledge on collaborative networks, logistics and transportation, agent behavior as well as trust. This research contributes by identifying key agents together with their roles, characteristics, tasks, information exchange as well as perceptions; all of which linked to agent trust. The framework is reusable in many ways, including formal conception of models aspiring to empirically investigate trust amongst agents sharing logistics resources. It also provides more understanding to practitioners, especially on issues relating to compromising differences resulting from agent’s perspectives.

This paper proposes a new mathematical model for predictive scheduling of perishable material transports with the aim of reducing losses of perishable goods. The model is particularly designed for allocation of potatoes from several farms to a nearby starch mill, which produces starch from a limited amount of potatoes each day. Scheduling should determine how much amount of potatoes be sent from which farm to the mill on each day. It is known that the quality of potatoes decreases over time and as a result less starch is produced. A model predictive control approach is proposed to maximize the production of starch. Simulation experiments indicate that predictive scheduling can yield higher starch production compared to non-predictive approaches.

Processes in manufacturing and logistics are characterized by a high frequency of changes and fluctuations, caused by the high number of participants in logistic processes. The heterogeneous landscape of data formats for information storage further complicates efforts to automatically extract process models from this data with the tools from Process Mining. This article introduces a concept for constantly updating process models in logistics, called Process Maintenance, collects requirements for a common view on information in logistics, and shows that Process Mining with logistic data is possible, but still needs improvement to become a regular practice.

The objective of this study is to investigate supply chain performance with a focus on transport logistics. The expected effects and capacity of potential changes in supply chain performance should be taken into account while developing a management decision on logistics network functioning. The paper proposes an analysis and a quantitative estimation of positive/negative effects and supply chain performance caused by a change of average speed and loading/unloading time in logistics network of trucks with full load. It is shown that a combination of these two factors leads to the overall synergistic effect with increased output.

Automation always plays an important role in industry. Today, it is a basic need for industry. To develop faster manufacturing or delivery, automation is an important need. Robots always play the main role for automation in the industry. Robots are mainly designed for specific task. But, the main problem is robots are too expensive for one task. Thats why, it is almost impossible to use robots for small industries. Therefore, we are aiming to develop a pipeline to design a multitasking robot, especially for different kinds of packaging tasks. Typical text-based instruction sheets are the main source of these automation robots, that means robots can pack different types of shapes using typical text-based packaging instructions. In robotics, learning by demonstration in robotics, could benefit from large body movement dataset extracted from textual instructions. The interpretations of instructions for the automatic generation of the corresponding movements thereof are difficult tasks. We examine methods for converting textual surface structures into the semantic representations and explore tools for analysis and automated simulation of activities in industrial and household settings. In our first step, we try to develop a pipeline from textual instructions to virtual actions that includes traditional language processing technologies as well as human computation approaches. Using the resulting virtual actions, we will train robots through imitation learning or learning by demonstration for multitasking packaging robots.

Transport risks in supply chains have increasingly lead to significant capital losses. Insurance claims against such losses have grown accordingly, while simultaneous advances in technology lead to continuously larger volumes of data recorded. Traditional risk evaluation methods in insurance struggle to account for rising supply chain complexity which is reflected by growing amount and dimensionality of supply chain data. Therefore decision-makers in the transport insurance industry need new ways of appropriate knowledge representation to support transport insurance providers with daily tasks such as premium tariffing. This paper presents a method based on multidimensional scaling (MDS) for the identification of groups of similar claims as a first step towards the improvement of supply chain risk evaluation and forecasting. We show the application potential of transforming and visualising transport damage claims data as the basis for developing decision support systems (DSS) to support transport insurance providers in tasks such as premium tariffing as well as transport and supply chain managers in risk mitigation and prevention activities.

The purpose of this study is to develop and demonstrate a semi-automated text analytics approach for the identification and categorization of information that can be used for country logistics assessments. In this paper, we develop the methodology on a set of documents for 21 countries using machine learning techniques while controlling both for 4 different time periods in the world FDI trends, and the different geographic and economic country affiliations. We report illustrative findings followed by a presentation of the separation of concerns/division of labor between the domain expert and the text analyst. Implications are discussed and future work is outlined.

The aim of this study is to discuss the characteristics of the input data to the data analytical algorithms of a predictive maintenance system, from the viewpoint of big data technology. The discussed application is for the maintenance of off-shore wind turbines. The maintenance of off-shore wind turbines is an expensive and sensitive task. Therefore, making decision for planning and scheduling of maintenance in a wind farm (which is made by the operating company of a wind farm) is important and plays a critical role in the cost of maintenance. In this paper, the current state of the art for big data technology in the maintenance of off-shore wind turbines is presented. The dimensions of big data analytics and the technical requirements of data for the use of this technology in the maintenance of off-shore wind turbines are described. A contribution of this paper is to study the technical requirements of suitable data for decision-making. The outcomes of presented study are identifying the characteristics of input data to predictive maintenance in the era of big data and the discussion of these characteristics in the condition monitoring for off-shore wind energy.

The objective of this study is to investigate an optimal vehicle routing scheme to perform an OEM milk-run pickup service over a regional road network. The manufacturing of components is subject to varying tardiness among suppliers when fulfilling OEM orders. This often leads to non-accomplished orders at the end of the vehicle cycle time since the transport operation, predominantly composed by random variables, must comply with a strict delivery time limit set up by the OEM company. The mathematical model searches for the optimal vehicle routing sequence, together with searching for the best tardiness tolerance level that minimizes the sum of penalty costs levied against faulty suppliers, and transport expenses.

Supply chain risk management process (SCRMP) is being advanced as a systematic and structured approach for identifying, assessing, mitigating, and monitoring all risks arising from complex supply chains. However, while the literature deems it necessary to implement such a process as the solution to the increasing vulnerability companies face, there is a lack of empirical evidence on whether the process model can be implemented. This paper shows possible hindrances in the implementation of SCRMP for companies with global supply chains based on the findings of an in-depth case study. Our empirical findings indicate that the unavailability of information and lack of proper data management hinders the implementation of SCRMP in the context global supply chains.

This paper focuses on different aspects of fashion markets. In a first step, fashion levels will be classified; followed by definitions on fashion trends, and the suggestions on a fashion trend concept. In order to fill this concept and support decision-making processes along the supply chain, such as the catching of actual fashion trends, it is required to fill this concept with relevant information on different product features. Social media text data is considered as one relevant source. Showing previous researches, we assume that for instance fashion weblogs can be used for extracting this information. In a further step, we describe different fashion markets, namely fast fashion and luxury, in order to examine the applicability of the approach to real-life markets and their supply chain processes. The paper concludes by formulating hypotheses on a potential application of the approach.

In developing countries, significant food losses occur during distribution. The use of technologies such as Intelligent Packaging (IP) and the Internet of Things (IoT) may provide improvements in controlling the distribution of food products, minimizing losses. This paper identifies the Brazilian food supply chains current technological state and their receptivity to the IP and IoT technologies adoption. The analysis was based on a survey with logistics professionals from the country’s largest food companies, which represent 75.3 % of this market (in sales). The results show that these companies do not currently use IP and that a few use what they consider to be IoT systems. Cost is the greatest barrier to the use of these technologies; however, the lack of knowledge about these technologies also represents a strong barrier to their use.

The Intelligent Transportation System (ITS) addresses issues regarding traffic management and road safety in the domain of Vehicular Ad hoc Networks (VANETs). With the evaluation of new applications, new goals regarding efficiency and security have been added for logistics and general user application, which demand time-bounded and reliable services. In this paper, we evaluate VANET with regard to its suitability in logistics scenarios. We simulated VANET by considering different application scenarios for logistics and transportation using varying parameters such as speed, number of nodes, traffic load and bit error rate etc. We observed that it performs well in most of the scenarios due to its highly suitability in vehicular environment.

Smartphones provide rich applications and offer many crowdsensing services to end users. However, the power consumption of smartphones is still a primary issue in green computing. This paper presents a general power management framework including a data logger, an unsupervised learning algorithm of classifier, and a power-saving decision maker. The framework gathers and analyses the usage patterns of smartphones and separates end users into non-active and active ones, and their mobile devices into low-power ones and high-power ones using an unsupervised learning algorithm. If the smartphone of a non-active user belongs to the high-power group, we observe abnormal usage behaviour. The framework provides recommendations, e.g. as a power-saving notification to the user. We collected device usage and power consumption attributes on two kinds of Android–smartphones and evaluated the framework in experimental studies with 22 users. The results show that our framework can correctly identify non-active users’ devices consuming much more power than active users by recognizing reasons of the abnormal usage behaviour of non-active users and providing recommendations for adjusting the device towards power saving.

In this paper, a prototype is developed, which can easily integrate a wireless sensor with programmable logic controllers (PLC) using Profinet. The low-cost embedded system Raspberry Pi was used to connect a wireless gateway with ‘ProfiNet’. A Wizziboard wireless sensor node, running the Dash7 communication protocol, periodically sends temperature and humidity data. This data is received at the UART of Raspberry Pi. A modified Snap7 library is used for communication between Raspberry Pi and SIMATIC Manager. The data is transferred using the Ethernet bus. The prototype is tested on SIMATIC Manager with a PLCSIM simulator installed on a PC. The Siemens HMI software Wincc flexible is used to monitor the state of PLC systems. Here, Wincc flexible is connected to the PLC over the TCP/IP protocol. SIMATIC Manager and Wincc flexible have been tested on a windows PC. Data logging is also implemented in the HMI software.

The benefits of remote monitoring of perishable food items in transport and storage of a cool chain have been established and much talked about in the past decade. In order to convey the measured parametric data over for processing, wireless sensor networks are used, mostly in 2.4 GHz communication range, but it suffers from high signal attenuation in environments with high water content. This paper analyzes the possibility of wireless communication in 433 MHz and its lower sensitivity to water containing environments by means of a case study in an apple storage warehouse. The experiment shows near conformity to a previously implemented theoretical model of signal attenuation, the only error being due to ca. 10 % more water in apples compared to bananas. Second, the papers focuses on practical experiments with the focus on the DASH7 protocol, which is dedicated to sub-GHz communication, the OSS-7 software stack, and a multi-sensor, small-footprint hardware platform.

In horizontal coalitions for auction-based exchange of transportation requests, (freight) carriers have to identify requests that are selected for offering to coalition partners. This paper contributes pre-selection strategies, where requests are selected based on their approximated potential to increase the carriers’ profit. In a computational study, the strategies’ ability to achieve savings is analyzed.

Small- and medium-sized forwarders are confronted with thin margins and high demand fluctuations in competitive transportation markets. In these markets forwarders try to improve their planning situation by using external resources besides their own resources. These external resources might belong to closely related subcontractors, common carriers or cooperating forwarders in horizontal coalitions. In recent publications, it is assumed that some transportation requests are prohibited to be fulfilled by certain external resources due to contractual obligations. These requests are known as compulsory requests. In this paper, a transportation planning problem including external resources is extended by the mentioned compulsory requests. It is proposed to consider different types of compulsory requests depending on the applicable external resources for fulfilling these requests. As a solution approach a column generation-based heuristic is applied, which uses a strict composition procedure and a strict generation procedure for handling compulsory requests. In a detailed computational study, the increase of transportation costs is analyzed.

Commercial vehicles are a common mode of transport employed in the urban freight transport. Normally, they are internal combustion engine (ICE) vehicles powered by burning fossil fuels. However, with deteriorating air quality and decreasing energy resources, ICE vehicles are experiencing a significant transition. Electric commercial vehicles (ECVs) as a feasible alternative to alleviate emissions and save energy resources are proposed in the field of urban freight transport. Nevertheless, with the increasing intention of adopting ECVs, the number of employed ECVs is lower than ICE vehicles and electric passenger cars. Therefore, this paper reviewed and analyzed 25 related articles to collect factors affecting the employment. Furthermore, we classified the factors into the three pillars of sustainability with integrating technological dimension. The results illustrate the influence of positive factors and negative factors on the employment. The future work will focus on ranking the priorities of factors and suggesting the logistics company to consider ECVs in their fleets thereby improving the adoption and the sustainable urban freight transport.

Integrated operational transportation planning (IOTP) refers to the extension of vehicle routing by the option of subcontraction. In practice, planners often use semi-manual strategies for IOTP. Two wide spread semi-manual strategies for IOTP are presented. The strategies are evaluated by computational experiments comparing the results achieved by these strategies with the exact solutions which have been provided by MIP approach for IOTP.

This paper presents a state-of-the-art analysis of Intelligent Transport Systems (ITS) for road freight transport including an overview of telematics applications for road freight transport. Furthermore, an analysis on how different actors of a transport chain perceive the developments of ITS is given. The paper also presents selected examples of practical ITS usage.

Researchers intend to employ emission trading scheme (ETS) as one of the cost-effective policy instruments on the level of supply chains (SCs) to control SC emissions. This paper reviews literatures from recent 5 years in addressing ETS in the context of SCs. Through analyzing mathematical models adopted in available literatures to address the implementation, it identifies the intentions and approaches of imposing ETS in the context of SCs. This paper is believed to be one of the first literature reviews in addressing ETS in the context of SCs. It provides insights for supply chain managers and policy makers on whether including SCs into ETS.

In the last decade, the erection of offshore wind turbines, especially in the northern sea, has been showing a significant growth and this trend will continue further in the next years. In order to make the offshore wind energy competitive and attractive, the different processes related to the overall life cycle cost of an offshore wind farm have to be optimized. In this context, the cost for operation and maintenance (O&M) is estimated between 15 and 30 % of the total costs generated by offshore wind farms. Thereby, the efficiency of the maintenance processes is a crucial factor to guarantee sustainable energy and improve the reliability and availability of an offshore wind turbine. Indeed, the maintenance activities in the offshore field are a challenging task especially due to the harsh maritime environment which leads to high material stress and low resource utilization. In this paper, we model the maintenance processes of an offshore wind farm by means of a discrete event and agent -based simulation model. The objective is to schedule the maintenance tasks taking into account all real restrictions based on historical data in order to determine important factors and potential operational improvement. As an example, the simulation will be used to determine the optimal number of resources needed to perform maintenance activities by keeping the resource utilization in an acceptable level.

This paper discusses the process model of consumer logistics by Granzin and Bahn (1989) from the perspective of the shopper. Thereby, we propose a model of shopper logistics and provide further insight into the planning and execution of shopper logistics based on an empirical study amongst private households. Our findings identify a distinction between planning and executing logistics activities which are not performed in a sequential but in a simultaneous manner.

The repositioning of empty containers is one of the most important tasks in container shipping. The paper discusses the repositioning with non-logistic theories to explain problems between different actors in the supply chain of empty container repositioning. The transaction cost theory explains the conditions under which ocean carriers or exporters have to find a transaction partner or the choice between hierarchy and market. The principal agent theory explains how different actors react in a supply chain and gives advices for contract design. By means of these theories one specific relationship in empty container management is analyzed. With the help of this analysis, advices for later research could be given and a better understanding for problems between the actors could be shown up. The fact that only the relationship between exporter-ocean carrier-railway company is analyzed and not the whole supply chain limit the research. The main contribution is that a research gap in empty container repositioning could be closed, because there is no paper with a theoretical background of new institutional economics.

Frugal innovation gains momentum in literature and practice as the next important management approach transferred from East to West after lean. The two paradigms have launched powerful ideas challenging the traditional Western approaches, with frugal innovation being the most recent. This paper focuses on the application of lean management and frugal innovation in the field of engineering. The principles employed during the design stages are responsible for a high percentage of logistics costs and consequently influence significantly logistics processes. Therefore, we highlight similarities and differences of the two approaches, and show how to combine the underlying principles in order to develop more efficient products with efficient production processes especially for cost-sensitive consumers in both developing and developed countries. The implications of lean and frugal engineering for procurement, production, distribution and disposal logistics are relevant not only for sustainability managers, product or logistics managers concerned with sustainability aspects but also for executives aiming to expand their operations in developing countries.

Change and dynamics in logistics are interestingly driven at the same time by external as well as internal forces. This contribution outlines a big data literature review methodology to overview recognizable external changes and analyzes the interaction of one major trend—demographic change—further in order to allow for change management and adaption concepts for successful logistics. Therefore, this may be a first blueprint of how to analyze and react to specific trends in a holistic manner embedded into a context and environment of trends and changes. This may allow logistics dynamics concepts also to be possibly more sustainable in terms of applicable for a longer period of time—and not to be overcome by other trends.

As a result of global societal and economic as well as technological developments logistics and supply chains face unprecedented challenges. Climate change, the need for more sustainable products and processes, major political changes, the advance of internet technology in logistics and cyber-physical production systems pose challenges that require radical solutions, but also present major opportunities. The authors provide a literature review as well as a roadmap on selected issues and argue that logistics has to reinvent itself not only to address these challenges, but also to cope with mass individualization on the one hand while exploiting business applications of artificial intelligence on the other hand. An essential challenge will be to find a compromise between these two developments—in line and in combination with the known triple-bottom line for sustainability. This is presented with an extended analysis on what to do regarding worker qualification and training in logistics in order to cope with these developments.

The purpose of this exploratory study is to understand how technology innovations discussed with respect to the fourth industrial revolution diffuse within the supply chain. First, the main characteristic and interrelated features of the term “Industry 4.0” are identified to come up with a definition. We understand that the fourth industrial revolution is characterized by the technological trends of digitalization, autonomization, transparency, availability of real-time information and collaboration. Based on the assumption, that all technological innovations addressing these characteristics, are contributing to diffuse the concept of Industry 4.0 in the supply chain, causal hypotheses are stated within a structural model to further discuss the diffusion-factors and barriers of implementation. All results are conceptually derived based on a structured literature review.

The problem of minimizing the maximum deviation between the actual and the ideal cumulative production of a variety of models of a common base product, commonly known as the bottleneck product rate variation problem, arises as a sequencing problem in mixed-model just-in-time production systems. The problem has been extensively studied in the literature with several pseudo-polynomial exact algorithms and heuristics. In this paper, we estimate an improved largest function value of a feasible solution for the problem when the $$m^{th}$$mth power of the maximum deviation between the actual and the ideal cumulative productions has to be minimized.

Crowdsourcing concepts are a major development in the context of social media and Industry 4.0 as well as a possible enabler for dynamic logistics processes and concepts. In the light of increasing sustainability, flexibility, and security expectations towards global supply chains, solutions for logistics based on crowdsourcing may be promising—but still the proof of concept is missing for logistics applications in practice. Combined with a theoretical framework for crowdsourcing in logistics, this contribution outlines two case studies for regarding e-mobility, knowledge management in logistics, as well as disaster applications. This could be a supportive approach in developing new business models for real-life intelligent transport systems including customers and multiple stakeholder perspectives.

Providers of logistics services are confronted with multivariant customer needs and high competitive pressures. The concept of service modularization has been proposed as a strategy to allow for both standardization and individualization at the same time so that customer-specific demands can be fulfilled efficiently. However, the discussions on applying the concept of modularization to the service sector have largely remained academic. Little empirical evidence is available on how service modularization strategies can be applied. In this paper, we synthesize the status quo of service modularization in logistics by reviewing existing contributions from the literature and conducting expert interviews. The contribution is an overview of the current state of modularization in logistics and an outlook toward the potential for future adoption of modular strategies in this industry.

In this paper, we discuss the basic constraints and objectives of a unified logistics modeling language that allows to model dynamic logistic systems on various levels of concern from the requirements definition to their design and realization. It should, furthermore, support the compositionality and interoperability among all processes and other components even if they are modeled by means of different methods. Lastly, it should enable domain-specific views on logistic systems. A key concept, therefore, is that the model transformation allows one to bridge the gaps between different data formats and different modeling methods.

Drayage operations at a marine container terminal are considered. The idea is to increase efficiency of truck-based drayage operations by reusing empty import containers (street turns). Different sizes of containers, i.e., 20-foot and 40-foot containers, are considered. The transport planning problem is identified as a generalization of the well-known pickup and delivery problem. However, in extension, the transport requests are only partially specified. A heuristic is developed to solve this problem. The effect of different mixtures of 20-foot and 40-foot container requests on the possibility of street turns is tested. This is based on total operation time and other performance criteria for an example instance.

This paper presents a new model for Inventory Routing Problem (IRP) considering simultaneously economic criteria, customer satisfaction level and environmental aspect for perishable products with expiration date. For this consideration, a multi-objective mathematical model has been developed. The first objective focuses on traditional inventory and distribution costs as well as recycling cost of perished products. The second objective concerns in customer satisfaction by minimization of three criteria, such as the number of delays (deliver after time windows), the quantity of backordered, and the frequency of backorders. The third objective considers Greenhouse Gas (GHG) emission, produced by different IRP activities. The proposed model is also enabled to investigate the possibility of using diesel and electrical vehicles in urban transportation. In order to cope with complexity of proposed model, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is tuned and applied. Finally, sensitivity analysis is performed to investigate the effects of variation of customer satisfaction and green aspects in economic side.

The use of decentralized control systems in the field of intralogistics offers high promises, but presents also various technical challenges. One of them is the development of adequate modeling approaches. Based on the problem of routing of automated guided vehicles (AGVs), this paper demonstrates how graph-transformational swarms can be used to model and develop decentralized solutions. Representing the environment as a graph, every AGV is modeled as a swarm member. They act locally and simultaneously like swarms members do in nature. The movement of the AGVs is regulated in such a way that they reach their targets on shortest paths without collisions.

The information flow in logistics is covered by a growing set of systems and standards. To handle the increasing heterogeneity, the exchange of information rather than the exchange of data is favored because unlike data information contains a precise meaning. This precise meaning would reduce the false interpretation between stakeholders within information flows. The exchange of information on the system level requires the application of transformations on the communication interface levels. In this paper, a corresponding transformation and communication approach is presented. Finally, the impact of the conceptual approach is shown on a hypothetical application scenario.

Supply chains are a highly evolving line of trading. The cool chains responsible for transportation of perishables are one subcategory that is demanding technological support to reduce the quality-related losses that they suffer due to temperature variations, among other reasons. Even distribution and ventilation of refrigerated air inside containers is imperative to maintain the perishables at the desired temperature range, avoiding degradation and spoilage. However, lack of research on airflow movement behavior—and convenient means of measuring spatial airflow speed—within packed containers makes it difficult to determine the hotspot scenarios, which is a prime cause of the said degradation. This paper presents a methodology to parametrically measure spatial airflow and analyzes the airflow behavior under different container loading schemes and how the airflow affects the internal pallet temperature.

Elevation of technology in hardware, software and communication have lead to an idea of Intelligent Vehicles, which communicate via Vehicular Ad hoc Networks (VANET) to provide road safety and improved driving conditions. In VANETs, both multi-cast and uni-cast routing is applicable. These protocols can be used by an attacker to breach and endanger the network by accessing and manipulating information of the vehicle and communicating data. As authentication allows to trust both user and data, it requires considerable attention in the security framework of VANETs. Here, we provide a classification of attacks on the basis of security requirements to narrow down the parameters of consideration and comparison of previously proposed protocols in VANET. Pointing out security gaps regarding distinct threats, the classification will allow to designed new secure network control methods.