A design model and a production–distribution and inventory planning model in multi-product supply chain networks

Supply chain network (SCN) design implicates decision-making at a strategic level. That includes selecting the right suppliers and determining the number and the location of plants, distribution centres and retailers. An apt design model of the supply chain is imperative for the proper function of the supply chain and consequently for making better operational decisions in an attempt of a continuous improvement. In this paper, we propose two models. The first model is a mixed-integer linear programming model which is concerned with the SCN design problem, whereas the second operational model is a mixed-integer non-linear programming model in respect to the production–distribution and inventory planning problem in a supply chain network. The number of customers and suppliers as well as their demand and capacities are assumed to be known in both models. Two steady-state genetic algorithms were implemented in MATLAB in order to solve both the design and the operational model. The results were compared with GAMS. Some examples were devised in order to demonstrate potential ways of use for the designer of the supply chain network, as well as for the supply chain manager.     

A new yield surface distortion model based on Baltov and Sawczuk’s model

In the present study, a new distortion yield surface model is proposed to represent compatible results with experimental observations. The proposed yield surface model is determined numerically during tension–torsion loadings by considering a kinematic hardening model and monotonic loading paths. The experimental results of yield surface determination (Khan et al. in Int J Plast 26:1432–1441, 2010; Naghdi et al. in ASME J. Appl Mech 25:201–209, 1957) represent the nosed and flattened regions in the loading and reverse loading directions, respectively. But, the Baltov and Sawczuk’s yield surface model can only predict nosed or flattened shape in both loading and reversed loading directions, depending on the sign of their model constant. Thus, the elliptic Baltov and Sawczuk’s yield surface is modified by changing the sign of this parameter continuously from loading to reverse loading direction. Relations and convexity of the new model are obtained and discussed. The new model is able to predict properly the shape of the yield surface. The experimental results are in a satisfactory agreement with the new yield surface distortion model predictions.     

Coefficient of restitution and linear–dashpot model revisited

With the assumption of a linear–dashpot interaction force, the coefficient of restitution, , can be computed as a function of the elastic and dissipative material constants, k and γ by integrating Newton’s equation of motion for an isolated pair of colliding particles. If we require further that the particles interact exclusively repulsive, which is a common assumption in granular systems, we obtain an expression which differs even qualitatively from the known result . The expression allows to relate Molecular Dynamics simulations to event-driven Molecular Dynamics for a widely used collision model. This research was supported by German Science Foundation and by the G.I.F., the German-Israeli Foundation for Scientific Research and Development.     

δ and austenite phases evolution and model in solution treatment of superalloy GH4169

Abstract

The δ phase evolution and its effect on the austenite grain growth in the solution treatment of superalloy GH4169 were investigated at the solution temperatures of 1233, 1253, 1273 and 1293 K and the holding time of 30, 45 and 60 min. The δ phase is dissolved and transformed from short bars to fine bars or particles gradually with the increasing solution temperature. The holding time affects the morphology and the amount of δ phase slightly. The austenite grain grows with the increasing solution temperature and holding time. It grows slowly at the solution temperatures ranging from 1233 to 1373 K, but sharply from 1273 to 1293 K. The phenomenon suggests that the δ phase generates a pinning effect on the austenite grain growth. The kinetic analysis about the austenite grain growth was carried out at the solution temperatures ranging from 1233 to 1373 K. The grain growth exponent n value is calculated to be 1·2 which confirms the pinning effect of δ phase on the austenite grain growth, and activation energy Q value is calculated to be 394·1 kJ mol^?1. Finally, a model with an average difference 9·22% for the austenite grain growth was proposed.     

“String and bead” model of copper modified polycarbosilane: synthesis and applications

Journal of Materials Science - The synthesis of metal modified polycarbosilanes is currently an area of significant activity. These polymers can be processed to advanced materials such as ceramic...     

Percolation conductivity model in A site-deficient La0.8−xoxNa0.2−xoxMnO3 manganites and universal model of magnetic field-dependent resistivity and magnetoresistance behavior

Journal of Materials Science: Materials in Electronics - The electrical and magnetoresistance behaviors of La0.8?xoxNa0.2?xoxMnO3 [with x?=?0 (N0) and 0.1 (N0.1)]...     

Creating resilient and sustainable manufacturing businesses – a conceptual fitness model

Over recent years, UK manufacturing industry has experienced turbulence in its business performance. Lower cost products, and responsive and flexible processes, are now essential in order for a company to capture new markets and to become economically resilient. Business resiliency is a term used frequently to describe a company’s ability to adapt and cope with disturbance. This has led to the generation of many frameworks and models aimed at guiding companies towards improved business performance. However, these frameworks are primarily strategic in nature and do not necessarily focus on creating resilience at an operational level in manufacturing companies. The authors employ a mixed research approach initially undertaking a literature review and then a screening survey in to 25 manufacturing companies in order to identify the key business resiliency techniques employed. Following this, a focus group goes on to detail a new manufacturing resiliency model called the fit operational model. The model’s effectiveness is then assessed and adjusted as a result of being implemented in a subject company.     

Age-structured predator–prey model with habitat complexity: oscillations and control

In this article, we study a predator–prey interaction in a homogeneously complex habitat where predator takes a fixed time to develop from immature to its mature stage. The age-structure of the predator and its interaction with the prey is framed in a system of delay differential equations. The objective is to study the role of habitat complexity and the maturation delay of the predator on the overall dynamics of the model system. Different interesting dynamical behaviours can be obtained by regulating two key parameters, namely the degree of habitat complexity and the maturation delay. It is observed that the system becomes unstable from its stable condition when the maturation delay crosses some critical value. The periodic solutions bifurcated from the interior equilibrium is found to be supercritical and stable. Synchronization of population fluctuations is, however, possible by increasing the strength of habitat complexity. The predator population goes to extinction and the prey population reaches to its maximum, irrespective of the length of maturation delay, when the habitat complexity crosses some upper critical value. The qualitative dynamical behaviours of the model system are verified with the data of Paramecium aurelia (prey) and Didinium nasutum (predator) interaction.     

An anisotropic viscoelastoplastic model for composites—sensitivity analysis and parameter estimation

Because of their potential in achieving many performance enhancements, composite material systems (e.g. fiber-reinforced composites) are presently called upon to operate under wide range of stresses, temperatures, and loading rates. This in turn requires the development of general material models to capture the significant effects of anisotropy on both elastic and inelastic responses. The starting point in the present contribution is the development of a class of such viscoplastic models. Furthermore, a number of robust, computationally efficient, algorithms are also presented for the development of an overall strategy to estimate the material parameters characterizing these complex models; i.e. rate-dependent plastic flow, non-linear kinematic hardening, thermal/static recovery, anisotropic viscoelastic and viscoplastic flow. The entire procedure is automated through an integrated software namely, COnstitutive Material PARameter Estimator, COMPARE, to enable the determination of an ‘optimum’ set of material parameters by minimizing the errors between the experimental test data and the predicted response. The key ingredients of COMPARE are (i) primal analysis, (ii) sensitivity analysis, (iii) a gradient-based optimization problem and a (iv) graphical user interface. The estimation of the material parameters is cast as a minimum-error, weighted multi-objective, non-linear optimization problem with constraints. Detailed derivations of the direct differentiation sensitivity expressions are presented. In addition, numerical comparisons of the sensitivities obtained by the more traditional finite difference approaches are given to assess accuracy. Results generated by applying the developed algorithms for anisotropic, strain-controlled tensile (with comparison to typical experimental data) and constant-stress creep tests are presented to demonstrate the ability of the present models to accurately capture time-dependent anisotropic material behavior.     

Partitioning and interpolation based hybrid ARIMA–ANN model for time series forecasting

Time series data (TSD) originating from different applications have dissimilar characteristics. Hence for prediction of TSD, diversified varieties of prediction models exist. In many applications, hybrid models provide more accurate predictions than individual models. One such hybrid model, namely auto regressive integrated moving average – artificial neural network (ARIMA–ANN) is devised in many different ways in the literature. However, the prediction accuracy of hybrid ARIMA–ANN model can be further improved by devising suitable processing techniques. In this paper, a hybrid ARIMA–ANN model is proposed, which combines the concepts of the recently developed moving average (MA) filter based hybrid ARIMA–ANN model, with a processing technique involving a partitioning–interpolation (PI) step. The improved prediction accuracy of the proposed PI based hybrid ARIMA–ANN model is justified using a simulation experiment. Further, on different experimental TSD like sunspots TSD and electricity price TSD, the proposed hybrid model is applied along with four existing state-of-the-art models and it is found that the proposed model outperforms all the others, and hence is a promising model for TSD prediction.     

Dynamical theoretical model and variational principles for coupled temperature–diffusion–mechanics

In this paper, firstly, the acceleration of the temperature and concentration are assumed to need the extra increment of the heat and energy. The inertial entropy and the inertial chemical potential were proposed by Kuang (Acta Mech 203:1–11, 2009, Acta Mech 214:275–289, 2010). Secondly, the expressions of dissipative energy produced by the variation of the temperature and concentration are derived by using the second law of thermodynamics. Finally, several variational principles for coupled temperature–diffusion–mechanics are established, and their corresponding governing equations and boundary conditions are naturally presented. Meanwhile, some numerical simulations are carried out to describe the coupled reciprocity, which shows concentration diffusion with a finite velocity.     

The usage of ‘theory’ and ‘model’ in scientific conceptualization

The results of a survey are reported and interpreted, pertaining to the way scientists use the terms theory and model to conceptualize the scientific information and ideas they deal with. Initially the assessment of the importance of a new theoretical approach in science is done to a large extent on the basis of the conceptual structure of the new approach, but later the predictive power plays an increasingly more important role. It is suggested that results like those in this study might perhaps be used to critically evaluate new theoretical approaches in science even at the time of their conception.     

Dynamic ductile crack growth and transition to cleavage – a cell model approach

The fracture behavior of ferritic steel in the transition regime is controlled by the competition between ductile tearing and cleavage. Many test specimens that failed by catastrophic cleavage showed significant amounts of ductile tearing prior to cleavage fracture. The transition from ductile tearing to cleavage has been attributed to the increase in constraint and sampling volume associated with ductile crack growth. This work examines the role of dynamic ductile crack growth on the fracture mode transition by way of a cell model of the material. The cell model incorporates the effects of stress triaxiality and strain rate on material failure characteristics of hole growth and coalescence. Loading rate and microstructure effects on the stress fields that evolve with rapid (ductile) crack growth are systematically studied. The stress fields are employed to compute the Weibull stress which provides probability estimates for the susceptibility to cleavage fracture. A center-cracked panel subjected to remote tension is the model problem under study. The computational model uses an elastic-viscoplastic constitutive relation which incorporates enhanced strain rate hardening at high strain rates. Adiabatic heating due to plastic dissipation and the resulting thermal softening are also accounted for. Under dynamically high loading rate, our model shows the crack speed achieves its peak value soon after crack initiation and quickly falls off to slower speeds with further crack growth. Remarkably, the Weibull stress follows a similar pattern which suggests that the transition to the cleavage fracture is most likely to occur, if at all, at the peak speed of ductile crack growth. Key words: Dynamic fracture, ductile tearing, crack growth, transition regime, cleavage fracture, cell model, finite element.     

Electrochemical copolymerization of N-methylpyrrole and 2,2′-bithitiophene; characterization,micro-capacitor study,and equivalent circuit model evaluation

N-methylpyrrole (N-MPy) and 2,2′-bithiophene (BTh) were electrocopolymerized in 0·2 M acetonitrile–sodium perchlorate solvent–electrolyte couple on a glassy carbon electrode (GCE) by cyclic voltammetry (CV). The resulting homopolymers and copolymers in different initial feed ratios of [N-MPy]₀/[BTh]₀ = 1/1, 1/2, 1/5 and 1/10 were characterized by CV, Fourier-transform infrared reflectance attenuated transmittance (FTIR–ATR), scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDX) and electrochemical impedance spectroscopy (EIS). The capacitive behaviours of the modified electrodes were defined via Nyquist, Bode-magnitude, Bode-phase and admittance plots. The equivalent circuit model of R(C(R)(QR)(CR)) was performed to fit theoretical and experimental data. The highest low-frequency capacitance (C _LF) were obtained as C _LF = ～ 1·23 × 10 ^?4 mF cm ^?2 for P(N-MPy), C _LF = ～ 2·09 × 10 ^?4 mF cm^?2 for P(BTh) and C _LF = ～ 5·54 × 10 ^?4 mF cm^?2 for copolymer in the inital feed ratio of [N-MPy]₀/[BTh] ₀ = 1/2.

Hardness of model dental composites – the effect of filler volume fraction and silanation

The relationship between structure and mechanical properties for dental composites has often proved difficult to determine due to the use of commercially available materials having a number of differences in composition i.e. different type of resin, different type of filler, etc. This makes a scientific study of any one variable such as filler content difficult if not impossible. In the current study it was the aim to test the hypothesis that hardness measurements of dental composites could be used to monitor the status of the resin–filler interface and to determine the efficacy of any particle silanation process. Ten model composites formulated from a single batch of resin and containing a common type of glass filler were formulated to contain varying amounts of filler. Some materials contained silanated filler, others contained unsilanated filler. Specimens were prepared and stored in water and hardness (Vickers') was determined at 24 h using loads of 50, 100, 200 and 300 g. Composites containing silanated fillers were significantly harder than materials containing unsilanated fillers. For unsilanated products hardness was independent of applied load and in this respect they behaved like homogeneous materials. For composites containing silanated fillers there was a marked increase in measured hardness as applied load was increased. This suggests that the hardness–load profile could be used to monitor the status of the resin–filler interface. ©1999 Kluwer Academic Publishers     

To select or to combine? The inventory performance of model and expert forecasts

Demand forecasting is a crucial input of any inventory system. The quality of the forecasts should be evaluated not only in terms of forecast accuracy or bias but also with regards to their inventory implications, which include the impact on the total inventory cost, the achieved service levels and the variance of orders and inventory. Forecast selection and combination are two very widely applied forecasting strategies that have shown repeatedly to increase the forecasting performance. However, the inventory performance of these strategies remains unexplored. We empirically examine the effects of forecast selection and combination on inventory when two sources of forecasts are available. We employ a large data-set that contains demands and (statistical and judgmental) forecasts for multiple pharmaceutical stock keeping units. We show that forecast selection and simple combination increase simultaneously the forecasting and inventory performance.     

A distribution free newsvendor model with consignment policy and retailer’s royalty reduction

Reducing the system cost and achieving significant profit are the key factors for every successful business sector. A consignment contract under distribution-free approach may be a fruitful combination to achieve a profitable business. This model deals with a single-period newsvendor problem with a consignment policy. The consignment policy is an agreement between any two parties, named as the consignor and the consignee. Under Stackelberg approach, firms act as leader and follower. Both parties carry some parts of the holding cost instead of one. A new policy for paying the fixed fee to the consignee is introduced. This paper considers no specific probability distribution for customer’s demand except a known mean and standard deviation. An efficient approach is proposed to reduce the retailer’s cost and building a sustainable consignment contract. The solution of this model is obtained using distribution free approach. A comparison between the traditional supply chain policy and the consignment policy is established. The price-sensitivity on demand is analysed. Some numerical examples and graphical representations are given for both traditional and consignment policy. Result proves that consignment policy is dominating over the traditional policy and a significant reduction of retailer’s royalty is found.