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Low-carbon regulation and market competition present new opportunities and challenges for supply chain firms, emphasizing the significance of carbon reduction and channel encroachment in enhancing competitiveness. This study formulates various game models to evaluate manufacturers’ encroachment strategies (with or without encroachment) under different conditions of low-carbon investment by retailers. It investigates the operational decisions and carbon abatement strategies of firms under various scenarios. The findings reveal that encroachment elevates unit abatement levels but decreases wholesale prices and retailer profits when unit encroachment costs are below certain thresholds. In contrast, the manufacturer consistently benefits from channel encroachment. Retailer-initiated low-carbon investments can motivate manufacturers to reduce emissions. A lower carbon price potentially offers financial advantages to retailer engaging in such investments. Additionally, the likelihood of reduced environmental damage postchannel encroachment, compared to preprofessional encroachment, increases when the retailer invests in low-carbon initiatives. The retailer’s profit is inversely related to the carbon price, and a higher carbon price can strengthen the incentive effect of low-carbon investment on the manufacturer’s abatement endeavors.

In this article, we investigate whether risk aversion, risk seeking, loss aversion, or prospect theory could explain the ordering decisions in multiechelon supply chains. First, we develop analytical models based on various theories of risk preference to predict order quantities of decision-makers. Then, using controlled laboratory experiments with participants from universities in the U.S. and Australia, we assess if the models predict the ordering behavior of decision-makers in profit-maximization and loss-minimization settings. For the profit-maximization setting, two variations of the beer game are tested, namely, games with purchase cost and games without purchase cost. The results for games with no purchase cost are consistent with loss-averse and risk-averse preferences, while the results for games with purchase cost are partially consistent with risk-seeking preferences. For loss-minimization games, the results are consistent with loss-averse preferences. We examined our results using two different reference points, namely, wealth at the beginning of each period of the game and changes in payoffs between two consecutive periods. Furthermore, we find that, based on the Berlin numeracy test, decision-makers with greater comprehension of risk usually order with lower variation. Finally, we find that decision-makers in a supply chain increase their order quantities when the objective is to maximize profit and reduce their order quantities when the objective is to minimize loss. Therefore, if the profit margins are higher in the supply chain, then the likelihood of a bullwhip effect increases.

In practice, an increased interest into end-to-end visibility as a future-oriented driver and capability of resilient supply chains can be observed. However, the research in this area is in its infancy. Even less is understood about resilience and the potentials of a digital supply chain in pandemic settings. Based on an analysis of the relevant literature supplemented with the multiple case studies constructed with the use of primary data, we build a framework that could be instructive for supply chain managers seeking to manage resilience during pandemic disruptions and using digital technology. Our main methodological contributions are unlocking the value and potentials of end-to-end supply chain visibility for resilience management in the face of pandemic disruptions. We propose an associated design and implementation framework containing multiple dimensions—management, organizational, and technological. The outcomes of the article offer a conceptual guideline concerning the potentials and implementation of end-to-end visibility in the management of supply chain resilience.

The concept of lead time sensitivity is proposed in the setting of supply chain resilience. We define lead time sensitivity as the extent to which supply chain performance curve, (e.g., demand fulfilment) reacts to delays in lead time. If a lead time change for a product causes a substantial change in the product availability, (e.g., a shortage leading to a non-fulfilled demand), it is considered sensitive. Through simulations using anyLogistix, we find out that variations in lead time sensitivity directly reflect product availability variations under disruptions, and lead time sensitivity coefficients can be used as indicators of resilience. Our approach contributes to research on supply chain resilience and ripple effect by proposing the concept of lead time sensitivity and can be immediately used in practice for stress-testing the supply chains. We discuss the practical application of our approach for different sourcing strategies.

The acquisition of used products (cores) is essential for leveraging the advantages of remanufacturing. Due to limited historical information about cores, it is difficult for the remanufacturers to accurately assess the quality of cores before disassembly and inspection. In this article, we investigate the joint acquisition and remanufacturing problem with a discrete empirical distribution. Under both deterministic and stochastic demands, two integer programming models are developed to obtain operational rules for making optimal decisions. Since the discrete empirical distribution is assumed to be the nominal distribution or asymptotic distribution of the true quality, the error caused by the difference between the estimated discrete empirical distribution and the true distribution cannot be neglected. Thus, we evaluate the error using the Phi-divergence measure, and the corresponding effects of this difference on the resultant acquisition and remanufacturing rules are analyzed. Through numerical examples, we observe that optimal acquisition and remanufacturing decisions are negatively affected by the difference between the estimated distribution and the true distribution, even if the estimated distribution can pass a hypothesis test. Furthermore, to overcome suboptimal outcomes, a multisource information fusion approach is proposed, which can utilize quality information collected from multiple sources to make accurate acquisition and remanufacturing decisions.

This tertiary study systematically analyses 65 literature reviews on supply chain resilience (SCR) published in academic journals or conference proceedings. Our focus is on the vulnerabilities and capabilities of a supply chain that need to be balanced to achieve resilience. We explore the interdependencies of these two categories of SCR by developing an innovative framework to realise capabilities after identifying the SCR vulnerabilities. First, we propose a framework that systematises the vulnerabilities and capabilities identified in the literature. Then, we discuss the identified SCR characteristics based on the framework and quantitatively evaluate the literature reviews' focus on the two SCR categories. A synthesis of the research results shows the SCR characteristics addressed in the literature and reveals deficits for specific vulnerabilities. Finally, we outline future research opportunities based on these findings by mapping SCR capabilities and vulnerabilities in light of Industry 4.0 and digital supply chain developments. Then, we derive research gaps and recommended actions for practitioners in the context of SCR and Industry 4.0.

Modelling of resilience and viability became crucial and visible research avenues for decision-making support in case of supply chain disruptions. This article discusses some research-practice paradoxes stemming from typical problem settings and assumptions in supply chain resilience and viability modelling. We show that the categories of probability, adaptability, and time are major determinants of resilience and viability modelling in supply chains. As an example, we discuss how probability-based supply chain design models can be enhanced through inclusion of adaptability and connectivity characteristics of the network elements with consideration of time. We deduce some open questions the elaboration on which can provide novel and substantial contributions in future. In particular, we stress the importance of furthering our understanding of reliable suppliers, disruption probabilities, disruption time and ripple effect estimation, value-creation perspective of resilience, and viability of intertwined supply networks.

Optimal control is a convenient way to develop both supply chain process optimization models and describe the dynamics of process fulfillment. A rich diversity of knowledge has been developed for the integration of optimization and simulation methods with applications to supply chain management at conceptual, informational, and computational levels. At the same time, model-algorithmic integration and alignment frameworks have received less attention. The importance of this level should not be underestimated since synthesis and analysis problems in supply chains imply tight intersections between and within the models (e.g., objective functions and constraint systems). This paper seeks to bring the discussion forward by carefully elaborating on the issues of optimization and simulation model and algorithm integration and providing implementation guidance. Conventionally, optimization has pre-dominantly been used at the planning level while dynamic system control was frequently investigated using simulation models. This study develops an integrated optimization-simulation framework at the model-algorithmic level for the given domain. We offer insights on how to describe planning and control in a unified model-algorithmic complex with consideration of uncertainty factors which are anticipated at the planning and confronted at the control stages. The developed theoretical framework was exemplified by a combined optimization-simulation modelling of the SC design and planning problem with disruption risks consideration in anyLogistix.

Current global supply chain events demonstrate how supply chain resilience and sustainability are intertwined and how both strategies can influence each other positively but as well as negatively. We aim to analyse the relationship of resilience and sustainability, understand the tensions between both supply chain strategies and to resolve them by applying the paradox theory. Thereby one resolution strategy is actively applied by analysing the capabilities in both domains. For this purpose, we conducted two systematic literature reviews including content analysis. This study identified several paradoxes among one of them related to congruent and divergent capabilities. As a result, we identified 29 congruent capabilities (e.g. collaboration, agility, transparency) and 200 related practices with the potential for developing a sustainable and resilient supply chain. In the same time several divergent capabilities were identified (e.g. redundancy) which can potentially cause tensions. This study demonstrates the relevance of a common consideration of both SC strategies, resiliency and sustainability. On the other side the importance of paradoxes and the need for better understanding of relationships during integration process within strategic management can be derived.

The growing competition and the destabilising effects of climate, disease and other external perils bring important challenges to companies worldwide. Supply chain (SC) networks become more and more complex with a growing exposure to a broad range of uncertainties, some of which may cause network disruptions. Neglecting these kinds of risks may lead to adverse consequences, such as negative financial effects, higher transportation costs, order delays, inventory shortages, and loss of market shares. Frequent disruption events that have been continuously increasing over recent years have clearly shown the key role of supply chain resilience. To hedge against SC disruptions, a well-designed and reliable supply network that performs efficiently in normal situations and resiliently during unstable conditions is a top priority. In this paper, starting from the discussion of the main design drivers for a resilient SC, a methodological framework is proposed to support both efficient and resilient supply network design. The procedure gets foundation from the recent literature, and it is directly derived from its application in a numerical example and an industrial case.

An unprecedented global crisis caused by the COVID-19 pandemic has revealed multiple challenges for operation management. One of them is yield uncertainty. In this paper, we examine the effect of the dual sourcing strategy and blockchain adoption on the performance of supply chain members in the presence of yield uncertainty. We develop a stylish model in which a decentralized supply chain consists of a downstream firm and the supplier and a centralized supply chain are competing. The downstream firm faced with yield uncertainty decides whether to procure from the competitor and decided whether to adopt blockchain to enhance the information quality about yield rate. Our results show that the dual sourcing strategy is a dominant strategy and the decision-maker benefits from the blockchain adoption if the volatility level of the yield rate is low.

Understanding the mechanisms and controlling the possibilities of surface nanostructuring is of crucial interest for both fundamental science and application perspectives. Here, we report a direct experimental observation of laser-induced periodic surface structures (LIPSS) formed near a predesigned gold step edge following single-pulse femtosecond laser irradiation. Simulation results based on a hybrid atomistic-continuum model fully support the experimental observations. We experimentally detect nanosized surface features with a periodicity of ∼300 nm and heights of a few tens of nanometers. We identify two key components of single-pulse LIPSS formation: excitation of surface plasmon polaritons and material reorganization. Our results lay a solid foundation toward simple and efficient usage of light for innovative material processing technologies.

Dear Sir: I am a computer science instructor for one of the numerous universities that use Pascal as the primary teaching language. Recently, at a curriculum planning group meeting, I […]

Earlier it was shown that the oxygenation of double bonds in polydienes by nitrous oxide (N2O) via the 1,3-dipolar cycloaddition mechanism (the so-called ketonization reaction) provides a synthetic route for obtaining a new type of carbonyl functionalized polymers and oligomers–unsaturated polyketones. Here, the ketonization of polybutadiene with a relatively high content of trans-1,4-units and 1,2-units at 160–230 °C and elevated pressure was studied. Oxygenated polymer products containing from 0.3 to 8.8 wt % oxygen in the form of carbonyl, mainly ketone, groups and having Mnfrom 34000 to 860 g/mol, respectively, have been obtained. The conversion of ca. 10% of double bonds in the parent polybutadiene during the reaction transforms it into carbonyl functionalized liquid rubber. A comparison of the obtained results with those for ketonization of cis-1,4-polybutadiene has revealed the effect of cis/transisomerism of 1,4-units and the content of 1,2-units on the reaction mechanism and the characteristics of the resulting polymer products.

Aging is a complex biological process, a major aspect of which is the accumulation of somatic changes throughout life. Cellular senescence is a condition in which cells undergo an irreversible cell cycle arrest in response to various cellular stresses. Once the cells begin to senesce, they become more resistant to any mutagens, including oncogenic factors. Inflammaging (inflammatory aging) is an age-related, chronic, and systemic inflammatory condition realized by cells with the Senescence-Associated Secretory Phenotype (SASP). These recently recognized senescent phenotypes associated with aging have been reported to promote better wound healing, embryonic development, as well as stimulation and extension of the tumor process. It is assumed that cellular senescence contributes to the age-related decline of reproductive function due to the association of senescent cells with aging and age-related diseases. Thus, SASPs have both positive and negative effects, depending on the biological context. SASP cell accumulation in tissues contributes to an age-related functional decline of the tissue and organ state. In this review, the term “cellular senescence” is used to refer to the processes of cells irreversible growth inhibition during their viable state, while the term “aging” is used to indicate the deterioration of tissues due to loss of function. Late reproductive age is associated with infertility and possible complications of the onset and maintenance of pregnancy. Senescent cells express pro-inflammatory cytokines, growth factors, and matrix metalloproteinases and some other molecules collectively called the Senescence-Associated Secretory Phenotype (SASP).

Purpose: This paper aims to considered the problem of identification of temperature-dependent thermal conductivity in the nonstationary, nonlinear heat equation. To describe the heat transfer in the furnace charge occupied by a homogeneous porous material, the heat equation is formulated. The inverse problem consists in finding the heat conductivity parameter, which depends on the temperature, from the measurements of the temperature in fixed points of the material. Design/methodology/approach: A numerical method based on the finite-difference scheme and the least squares approach for numerical solution of the direct and inverse problems has been recently developed. Findings: The influence of different numerical scheme parameters on the accuracy of the identified conductivity coefficient is studied. The results of the experiment carried out on real measurements are presented. Their results confirm the ones obtained earlier by using other methods. Originality/value: Novelty is in a new, easy way to identify thermal conductivity by known temperature measurements. This method is based on special finite-difference scheme, which gives a resolvable system of algebraic equations. The results sensitivity on changes in the method parameters was studies. The algorithms of identification in the case of a purely mathematical experiment and in the case of real measurements, their differences and the practical details are presented. [ABSTRACT FROM AUTHOR]

Firms have learned how to strengthen the resilience of their global supply chains (SC) to confront disruptions triggered by severe disasters. However, a new instigator of SC disruption, quite unlike any seen in recent times, has now emerged – the COVID-19/SARS-CoV-2 virus. We model the ripple effect of an epidemic outbreak in global SCs considering the velocity of pandemic propagation, the duration of production, distribution and market disruption, and a demand decline. We analyse pandemic supply risk mitigation measures and potential recovery paths. Implications for future research and global SC (re)-designs are also discussed.

The main problems and features of combined approach to the complex objects control and management stability analysis are investigated in the paper. Analytical-simulation scenarios and scenarios of intelligent models and systems execution for complex objects control and management stability analysis are given. The paper describes a particular group of models and modelling systems - hybrid intelligent models and systems that allow in conditions of uncertainty, incomplete initial data and complex interdependence between elements of complex objects to evaluate the implications of realization of various scenarios and risk evaluation. The investigations have shown successful possibility of risks evaluation by the combined implementation of the analytical-simulation models and algorithms, and ANFIS method - the method of hybrid neural-fuzzy modelling.

We present a new approach to femtosecond direct laser writing lithography to pattern nanocavities in ferromagnetic thin films. To demonstrate the concept, we irradiated 300 nm thin nickel films by single intense femtosecond laser pulses through glass substrate. Using a fluence above the ablation threshold, the process is destructive, leading to the formation of an ablation crater. By progressively lowering the laser fluence, the formation of closed spallation cavities below the ablation threshold is achieved. Systematic studies by the electron and optical interferometric microscopies, supported by molecular dynamics simulations, enabled us to gain an understanding of the thermo-mechanical spallation mechanism at the solid–molten interface. We achieved the fabrication of periodic arrangements of closed spallation nanocavities. Due to their topology, closed magnetic nanocavities can support unique couplings of multiple excitations (magnetic, optical, acoustic, spintronic). Thereby, they offer a unique physics playground for emerging fields in magnetism, magneto-photonic, and magneto-acoustic applications.

Brominated flame retardants (BFRs) are commonly used in electronic products, clothing, and furniture to reduce their flammability. They are related to reproductive system dysfunction, liver dysfunction, and fetal development disorders. However, few studies have investigated the relationship between exposure to BFRs mixtures and cognitive impairment in the general population aged 60 and above.

All vital operations of a supply chain network (SCN) are inherently affected by uncertainties and risks in the economic environment. In this study, we extend an existing SCN model by elaborating on stochastic price and demand, and integrating economic uncertainty. Financial performance and credit solvency are of utmost importance when analysing the financial status of an SCN. Each of these components focuses on a different aspect of investment attractiveness under various economic conditions, and in turn, facilitates access to capital for the companies involved. We extend the SCN model with financial performance optimisation approach to a novel model to include the trade-offs between two objective functions: (1) the total equity as the financial performance of the SC, and (2) the Altman’s Z-score as the credit solvency factor of the company. The model ascertains the future profitability, leverage, and liquidity of the SC by determining the optimal levels of production and inventory in each facility of the new network design. We introduce a weighted sum method and a novel genetic algorithm (GA) solution approach to solve the resultant bi-objective mixed integer nonlinear programming problem. We illustrate the representation scheme required for the GA in this paper.

This study presents a novel multi-objective model for a facility location-allocation problem with congestion in a multi-layer network. The proposed model contemplates the existence of impatient customers within a classical queuing system, namely M/M/1. The network is configured by determining the optimal number of facilities in each layer and allocation of customers to the facilities in a hierarchy facilities problem. Regarding the first objective function of the model, we minimize the sum of aggregate costs related to the customers’ waiting times, as well as the costs imposed by the impatience of the customers. In the second objective function, the costs associated with the sum of aggregate idleness of facilities are minimized. The proposed multi-objective multi-layer facility location-allocation (MLFLA) problem is solved by an evolutionary approach according to the Pareto envelope-based selection algorithm II (PESA-II). Parameters of the algorithm are tuned by Taguchi method. Then, the performance of the algorithm is reported by four performance metrics introduced for multi-objective optimization problems. The computational run times of 10 test problems demonstrate that the solution methodology is capable of solving the problem with large network sizes.

In the frameworks of supply chain risk management, dynamics, and resilience, control theoretic approaches can be considered as useful tools to tackle the issues of performance achievement under operational and disruption risks. New analytics technologies in the framework of Industry 4.0, big data analytics and artificial intelligence resulted in the creation of new domains, i.e., cyber physical supply chains and supply chain risk analytics. As such two trends can be observed, i.e., integration of analytics into control theory (so called intellectualization of control) and integration of analytics into supply chain risk management (so called cyber-physical supply chains and risk analytics). This study brings the discussion forward by integrating these two perspectives. It analyses how control theory can enhance the risk analytics in the cyber-physical supply chain. Based on literature and case-study analysis, the frameworks of cyber physical supply chain and risk analytics control are derived. In this setting, further development of interdisciplinary approaches to supply chain optimization and simulation with disruption risk considerations on the basis of control analytics is argued.

We study the coordination effects of production and ordering policies in the supply chain (SC) that experiences capacity disruptions and recovery. Discrete-event simulation methodology is used for analysis with real company data and real disruptions. It has been revealed that disruption-driven changes in SC behavior may result in post-disruption instability caused by backlog and delayed orders the accumulation of which in the post-disruption period we call as “disruption tails”. A transition of these residues into the post-disruption period destabilizes the normal operation mode resulting in further delivery delays and non-recovery of SC performance. The results provide evidence that coordinated contingency policies need to be applied during the disruption period to avoid the disruption tails.

This paper aims at delineating major features of the two new perspectives in supply chain (SC) disruption risk management, i.e., ripple effect and resileanness. The methodologies to mitigate the SC disruptions and recover in case of severe disruptions are discussed. It observes the reasons and mitigation strategies for the ripple effect in the SC and presents the ripple effect control framework that is comprised of redundancy, flexibility, and resilience. Even though a variety of valuable insights has been developed in the given area in recent years, new research avenues and ripple effect taxonomies are identified for the near future. The special focus is directed towards the supply chain risk analytics for disruption risks and the ripple effect in digital supply chains. In particular, the digital SC twin framework is presented.

The work addresses the properties of electro-transport in highly ion-conductive polymer electrolytes based on Nafion-115 membranes in the NH4+ionic form plasticized with dimethyl sulfoxide (DMSO). The ionic conductivity and the activation energy of the conductivity were found to exhibit three types of behavior as a function of the plasticizer. The first region (when the ratio of moles of DMSO molecules per mole of NH4+ (n) < 6) is characterized by low conductivity values (10[?]7- 10[?]4 S cm[?]1) and high energy barriers (0.76 and 0.43 eV for n = 0 and 2.6, respectively). In the second region (n = 6-12), the electrolyte has a high conductivity and a low activation energy of conductivity ([?]0.1 eV). With an increase in the DMSO content, the conductivity appreciably increases and reaches [?] 0.4 mScm[?]1. In the third region (n [?] 12) the transport parameters are comparable to those of the second region, but the conductivity is practically independent of the DMSO content. The observed behavior was explained on the basis of the results obtained with the help of IR spectroscopy, differential scanning calorimetry, small-angle X-ray scattering and quantum-chemical modeling.

Interrelations of product perishability and supply chain resilience are studied. The major distinguishing features in a supply chain with perishable products that may affect the resilience are the risks of goods write-off. Constraints on product perishability typically result in safety stock reductions and transportation frequency increase. Consideration of the production capacity disruption risks may lead to safety stock increase. The developed simulation model was run with data of a fast moving consumer goods company and revealed novel managerial insights and practical recommendations on inventory, on-time delivery and service level control. More specifically, the evidence for adjustment of production and inventory control policies during the disruption time is provided. Otherwise the delayed and backlog orders are accumulated over the disruption time. They transit as residues into the post-disruption period and do not allow full performance and operation dynamics stabilization even after full capacity recovery. An example of such a contingency production-inventory control policy is presented.

Uncertainty, feedback cycles and dynamics are major challenges in production and logistics systems, supply chains, and Industry 4.0 networks. From this perspective, control theory is an important research avenue in operations and supply chain management. We briefly survey the applications of control theory to engineering and management problems in supply chains and operations areas during the period 1960-2017. Our analysis is based on bridging the fundamentals of control and systems theory to supply chains and operations management. Although operations and supply chain systems resemble control systems, they have some peculiarities which do not allow a direct application of control theoretic methods. The required modifications and possible limitations are discussed. In this setting, further development of interdisciplinary approaches to supply chain optimization is argued.

Will President Vladimir Putin introduce direct federal control over the Northern Caucasus region? Whether or not he does will have a bearing on the future structure of the government of [...]

The paper analyzes the current state of production of composite materials in the Russian Federation. It is conceded the use of composites in aircraft design of various aircraft manufacturers. Concluded the feasibility of using composite materials in various industries. Peculiarities and prospects of prepreg application in the production of various parts are revealed.

In this article, we develop an optimal control model of supply chain reconfiguration that has been previously investigated with the help of mathematical programming approach. The developed model can be used to represent the dynamics of a continuous-flow supply chain system with a hybrid discrete-continuous state space. Dynamics of the supply chain reconfiguration can be modelled more detailed using the aggregate optimal parameters determined in the mathematical programming representation. The results of this study allow to distribute parameters and variables between mathematical programming and optimal control models in order to exploit their advantages in a joint manner.

Performance impact of severe disruptions in the reverse part of an automotive closed-loop supply chain is studied with the use of a discrete-event simulation model implemented in any Logistix software. A hybrid case study-simulation methodology is applied in this research to analyse the six-echelon closed-loop supply chain of Tesla from positions of resilience. Based on the secondary data, an example for the German market has been created and investigated. More specifically, the results help to show how a disruption in the reverse supply chain may affect the financial and operational performances of the company. Different recovery policies have been simulated in order to analyse how each might recover the supply chain from disruption and restore its operation and performance.

The focus of this study is on tactical-level capacity management which refers to the determination of in-house workforce production capacity in an optimal cost-efficient manner in a multi-period mode. Two options for capacity planning are investigated for rapid prototyping with the help of a mixed-integer linear optimisation model: smoothing the capacity utilisation over time vs. a higher level of capacity availability. A sensitivity analysis based on real empirical data in Siemens AG is used for the revealing the impact of workforce and capacity flexibility on operational performance. The results suggest that the number of workers decreases if production orders have not to be completed within only one week. On the contrary, if the binary restriction on the order fulfilment within one week is released, the production orders can be split into different weeks and this results in a higher smoothing rate and thus in a higher capacity utilisation degree.

Sourcing strategy analysis in the settings of supply chain (SC) flexibility in regard to single vs. dual sourcing has been a well-explored area over the last two decades. In recent years, single vs. dual sourcing analysis has been increasingly introduced in SC disruption management. Since most of the decision-support models for SC sourcing strategy adaptation in the case of disruptions presume real-time information and coordination, the issues of big data and business intelligence needs to be included into the consideration. A SC simulation model with consideration of capacity disruption and big data along with experimental results are presented. Based on both literature analysis and modelling example, managerial insights are derived. A set of sensitivity experiments allows us to illustrate the model's behaviour. The analysis suggests recommendation on using single sourcing, capacity flexibility and dual sourcing for different combinations of demand and inventory patterns. The paper is concluded by summarising the most important insights and outlining future research agenda.

Abstract

3D composite materials are characterized by complex internal yarn architectures, leading to complex deformation and failure development mechanisms. Net-shaped preforms, which are originally periodic in nature, lose their periodicity when the fabric is draped, deformed on a tool, and consolidated to create geometrically complex composite components. As a result, the internal yarn architecture, which dominates the mechanical behaviour, becomes dependent on the structural geometry. Hence, predicting the mechanical behaviour of 3D composites requires an accurate representation of the yarn architecture within structural scale models. When applied to 3D composites, conventional finite element modelling techniques are limited to either homogenised properties at the structural scale, or the unit cell scale for a more detailed material property definition. Consequently, these models fail to capture the complex phenomena occurring across multiple length scales and their effects on a 3D composite’s mechanical response. Here a multi-scale modelling approach based on a 3D spatial Voronoi tessellation is proposed. The model creates an intermediate length scale suitable for homogenisation to deal with the non-periodic nature of the final material. Information is passed between the different length scales to allow for the effect of the structural geometry to be taken into account on the smaller scales. The stiffness and surface strain predictions from the proposed model have been found to be in good agreement with experimental results. The proposed modelling framework has been used to gain important insight into the behaviour of this category of materials. It has been observed that the strain and stress distributions are strongly dependent on the internal yarn architecture and consequently on the final component geometry. Even for simple coupon tests, the internal architecture and geometric effects dominate the mechanical response. Consequently, the behaviour of 3D woven composites should be considered to be a structure specific response rather than generic homogenised material properties. [ABSTRACT FROM AUTHOR]

This article deals with the nature of the relationship of interpersonal relations and Internet addiction. The influence of deviant behavior on the interpersonal relationships of older adolescents is shown. The modern state of the problem of Internet addiction is considered. Opportunities for the application of different methods of psychological prevention of Internet addiction in adolescence are identified. The results of psychological prevention of Internet addiction of older adolescents are considered.

The notions of positive and negative identity are disclosed. Negative impact on the identity of the deviant behavior of adolescents is shown. Current state of deviant behavior is considered. The possibilities of using various psychotherapeutic methods in the work to create a positive identity in adolescents are defined. The results of psychological correction of negative identity in adolescents with deviant behavior are considered.

This paper introduces the structure dynamics control concept and a dynamic model to coordinate activities in cyber-physical supply chains in smart manufacturing concepts such as Industry 4.0 where a coordination of services and physical flows is needed. Continuous nature of services in the supply chain makes them similar to continuous production systems. Dynamic coordination of information services, information resources, and business process activities is considered on the scheduling level and modeled as an optimal control problem with uncertainty considerations.

Recent research underlines the crucial role of disruption events and recovery policies in supply chains. Despite a wealth of literature on supply chain design with disruption considerations, to the best of our knowledge there is no state-of-the art review on supply chain with disruptions and recovery considerations. We analyse state-of-the-art research streams on supply chain design and planning with both disruptions and recovery considerations with the aim to relate the existing quantitative methods to empirical research. The paper structures and classifies existing research streams and application areas of different quantitative methods subject to different disruption risks and recovery measures. We identify gaps in current research and delineate future research avenues. The results of this study are twofold. Operations and supply chain managers can observe which quantitative tools are available for different application areas. On other hand, limitations and future research needs for decision-support methods in supply chain risk management domains can be identified.

This paper studies an operational challenge recently faced by a high-tech manufacturing company that is specialized on both rapid prototyping (ETO: engineer-to-order) and serial manufacturing (MTO: make-to-order) of the printed circuit boards. We focused is on tactical-level process capacity management which refers to the determination of in-house workforce production capacity while the dynamic capacity allocation between rapid prototyping and serial production on the operative level is executed in an optimal cost-efficient manner in a multi-period mode. We present a linear programming approach for a single work station case. Subsequently, we extend the analysis towards multiple-line case and describe a concept of an agent-based simulation.

The ability to execute supply chains (SC) operations (in other words, to survive) despite the disturbances caused by a disruption is considered as SC design reliability. In the scope of this research is the analysis of what SC elements will survive (i.e., be in operation) after a disruption. In this study, we take the managerial perspective of the integrated SC and suggest the genome concept and its dual analogue to quantify the SC structure reliability. This method allows determining the upper bound and the approximate lower bound of the SC reliability. This result can be used by SC managers to compare different SC designs regarding the reliability. Subsequently, we extend the structure reliability analysis towards the trade-off 'reliability vs. efficiency'. In particular, it is shown that different forms of SC design structures with similar efficiency may significantly differ regarding reliability.

Flexible flow shop problem is represented as the dynamic interpretation of scheduling based on a natural dynamic decomposition of the problem and its solution with the help of a modified form of continuous maximum principle blended with combinatorial optimisation. A theoretical analysis of optimal scheduling approach with uniform alternative machines is considered. The scheduling approach is based on a dynamic non-stationary interpretation of the job execution and a temporal decomposition of the scheduling problem. The optimality conditions as well as the structural properties of the model and the algorithm are investigated. Advantages and limitations of the proposed approach are discussed. The results of this study can be used as an extension to the existing mathematical programming models subject to the following issues: dynamics and non-stationarity of the operation execution, non-stationarity of the machine structures and capacity parameters, and representation of continuous flows. In addition, the formulation of the scheduling model in terms of control makes it possible to attract additional tools from mathematics of functional spaces such as stability, robustness, controllability, adaptability, etc. to the schedule analysis and real-time adjustment.

AbstractThis study demonstrates the feasibility of creating stable, preconsolidated, yet permeable preforms for liquid resin infusion (RIFT) manufacture of composite materials. While being one of the cheapest and simplest methods of composites manufacture, RIFT with flexible tooling is known for high risk of dimensional and internal defects due to insufficient consolidation of textile preforms. Achieving the quality of a rigid mold solution, at the cost and simplicity of a flexible mold process, is the principal challenge of the RIFT.The approach presented suggests stabilizing a compliant preform through pointwise and highly controlled integration of a binder, and its consolidation prior to liquid molding. The printed resin creates a stiff skeleton, securing material for resin infusion and curing. This study explores the feasibility and efficiency of novel binding techniques, and the effects it may have on preform properties. Successful implementation of the concept for a multiply woven preform is demonstrated and the concept potential is discussed.

This study aims at analysing recent research on supply chain design with disruption considerations in terms of the ripple effect in the supply chain. It develops different dimensions of the ripple-effect and summarizes recent developments in the field of supply chain disruption management from a multidisciplinary perspective. We observe that the analysis of how to achieve planned economic performance in a real-time, uncertain and perturbed execution environment is a vital and up-to-date issue in many supply chains. Te ripple effect can be the phenomenon that is able to consolidate research in supply chain tion management and recovery similar to the bullwhip effect regarding demand and lead time fluctuations. This may build the agenda for future research on supply chain dynamics, control, continuity, and disruption management, making supply chains more robust, adaptable, and profitable.

We consider the ripple effect in the food supply chain and recovery policies on the example of Australia. Ripple effect describes the impact of a disruption on supply chain economic performance and disruption-based scope of changes needed in the supply structures and parameters to preserve the resilience. First, we describe the ripple effect in general and one example of the ripple effect in the food supply chain in Australia. Second, we present two models for a proactive and a reactive approach to the recovery policies in the food supply chain under conditions of the ripple effect. The preliminary results of this study can be used in future for comparing proactive and reactive approaches to tackling the ripple effect from resilience and flexibility views.

In this contribution, an original service-oriented form of structure dynamics control (SDC) and a model for dynamic scheduling of services for Industry 4.0 supply networks is developed. Problem of design and dynamic scheduling of services in cyber-physical systems is stated and solved with the help of SDC approach. The proposed service-oriented description makes it possible simultaneously to (i) determine the volume of information services needed for physical supply processes, (ii) determine this volume in monetary form, and (iii) interconnect each service with information systems needed for its realization. The results provide a base for performing service scheduling according to actual physical process performance and their adaptation (re-configuration) as well as for monetary estimation of investments into information systems and return on investments. Copyright © 2015 IFAC