A new newsvendor policy model for dual-sourcing supply chains by considering disruption risk and special order

Newsvendor model is one of the most important issues in inventory models. In this paper, we investigate a newsvendor model without lead time, which have difference between distributer and wholesale/retailer. At the end of day, the residual products of newsvendor sold to a secondary market at a unit salvage value. Also, the amount of orders that cannot be met, should be paid the penalty for each unit. In addition, in each one of channels, the percent of these orders cannot be met by the distributer. Then, the newsvendor provides the difference between the amount that ordered to distributor and the amount that met in the occurrence of interruptions risk as a special order from the manufacturer, more expensive than the price of distributor. The limitations of the study are the procurement budget that used for special order. Finally, the model is applied in a real case as a numerical example to determine order amount that maximize profit and is solved by Maple 15. The Kuhn–Tucker method was used to illustrate the optimal points that have necessary condition. Also, the hessian matrix was used to illustrate the optimal points that have sufficient condition for optimization. Consequently, the considered points are global optimum. The main factor in the disruption risk that effect on the ordering amount and profit, are including the probability of appearing of disruption \((p_i)\) and a percent of ordering amount which are met in the case of appearing of disruption \((y_i)\). Therefore, the analysis of sensitivity has been done on two parameters of \(p_i\) and \(y_i\) by using contour curves. According to result of solved problem, the change of disruption appearance reduced. Finally, the proposed method besides being simple is so exact which is sensible in the solved problems.     

Polyurethane rheology modifiers for organic compositions

A need exists for rheological modifiers that provide thickening for organic (oil) phases in the personal care industry. Despite such a significant need, most commercially available oil thickeners have several deficiencies, notably poor efficiency and clarity in formulations. To address this gap, we have developed a novel polyurethane‐based oil thickener (Oil‐Thickener PU) that provides significant thickening (gelation at <1 wt %) and high clarity in a range of natural oils and synthetic emollients. In this work, the structure, thickening mechanism, and viscoelastic properties of neat Oil‐Thickener PU film and Oil‐Thickener in sunflower oil gel are investigated in depth. Results show that the Oil‐Thickener PU provides thickening by forming a physically crosslinked network in oil via hydrogen bonding, resulting in a gel that is shear and temperature sensitive. Given its efficiency and uniqueness, the Oil‐Thickener PU is projected to have use in applications where a thickened organic phase is required. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46372.     

Prediction of the effectiveness of rolling dynamic compaction using artificial neural networks and cone penetration test data

Rolling Dynamic Compaction(RDC),which is a ground improvement technique involving non-circular modules drawn behind a tractor,has provided the construction industry with an improved ground compaction capability,especially with respect to a greater influence depth and a higher speed of compaction,resulting in increased productivity. However,to date,there is no reliable method to predict the effectiveness of RDC in a range of ground conditions. This paper presents a new and unique predictive tool developed by means of artificial neural networks(ANNs) that permits a priori prediction of density improvement resulting from a range of ground improvement projects that employed 4-sided RDC modules;commercially known as"impact rollers". The strong coefficient of correlation(i.e. R0.86) and the parametric behavior achieved in this study indicate that the model is successful in providing reliable predictions of the effectiveness of RDC in various ground conditions.     

Sterilizing tissue-materials using pulsed power plasma

This paper investigates the potential of pulsed power to sterilize hard and soft tissues and its impact on their physico-mechanical properties. It hypothesizes that pulsed plasma can sterilize both vascular and avascular tissues and the transitive layers in between without deleterious effects on their functional characteristics. Cartilage/bone laminate was chosen as a model to demonstrate the concept, treated at low temperature, at atmospheric pressure, in short durations and in buffered environment using a purposed-built pulsed power unit. Input voltage and time of exposure were assigned as controlling parameters in a full factorial design of experiment to determine physical and mechanical alteration pre- and post-treatment. The results demonstrated that, discharges of 11 kV sterilized samples in 45 s, reducing intrinsic elastic modules from 1.4 ± 0.9 to 0.9 ± 0.6 MPa. There was a decrease of 14.1 % in stiffness and 27.8 % in elastic-strain energy for the top quartile. Mechanical impairment was directly proportional to input voltage (P value < 0.05). Bacterial inactivation was proportional to treatment time for input voltages above 32 V (P < 0.001; R _Sq  = 0.98). Thermal analysis revealed that helix-coil transition decelerated with exposure time and collagen fibrils were destabilized as denaturation enthalpy reduced by 200 μV. We concluded by presenting a safe operating threshold for pulsed power plasma as a feasible protocol for effective sterilization of connective tissues with varying level of loss in mechanical robustness which we argue to be acceptable in certain medical and tissue engineering application.     

Multilevel resistive switching and synaptic plasticity of nanoparticulated cobaltite oxide memristive device

Multilevel resistive switching(RS)is a key property to embrace the full potential of memristive devices for non-volatile memory and neuromorphic computing applications.In this study,we employed nanopar-ticulated cobaltite oxide(Co3O4)as a model material to demonstrate the multilevel RS and synaptic learning capabilities because of its multiple and stable redox state properties.The Pt/Co3O4/Pt memris-tive device exhibited tunable RS properties with respect to different voltages and compliance currents(CC)without the electroforming process.That is,the device showed voltage-dependent RS at a higher CC whereas CC-dependent RS was observed at lower CC.The device showed four different resistance states during endurance and retention measurements and non-volatile memory results indicated that the CC-based measurement had less variation.Besides,we investigated the basic and complex synap-tic plasticity properties using the analog current-voltage characteristics of the Pt/Co3O4/Pt device.In particular,we mimicked the potentiation-depression and four-spike time-dependent plasticity(STDP)rules such as asymmetric Hebbian,asymmetric anti-Hebbian,symmetric Hebbian,and symmetric anti-Hebbian learning rules.The results of the present work indicate that the cobaltite oxide is an excellent nanomaterial for both multilevel RS and neuromorphic computing applications.     

Multi agent architecture for dynamic incremental process planning in the flexible manufacturing system

Due to rapid changes of markets and pressures of competitions, industries are adopting their production ways to support diversity of customer’s needs and increase of new product developments. This paper deals with development of an agent-based architecture of dynamic systems for process planning in the manufacturing systems. In consideration of alternative manufacturing processes and machine tools, the process plans and the schedules of the manufacturing resources are generated incrementally and dynamically. A previously proposed negotiation protocol is customized and improved to generate suitable process plans for the target products real-timely and dynamically, based on the alternative manufacturing processes. The alternative manufacturing processes are presented by the process plan networks and the suitable process plans are searched and generated to cope with both the dynamic status and disturbances of the manufacturing systems. We initiatively combine the heuristic search algorithms of the process plan networks with the negotiation protocols, in order to generate suitable process plans.     

A statistical analysis on the mechanical properties of nanocomposite modified asphalt mixtures

This study aimed to characterize the rate of change that takes place in the mechanical properties of polymer-nanocomposite asphalt (PNMA) mixtures due to altering asphalt binder grade and styrene-butadiene-styrene (SBS) type. Laboratory testing program included Marshall stability, resilient modulus, and wheel-tracking tests. Test results were compared and analyzed statistically by means of two-way analysis of variance and Tukey test. Variance analysis indicated the significant effect of both binder type and SBS grade on mechanical properties of asphalt mixtures. Interaction between binder type and SBS grade is also meaningful in all the experiments. The results also show that the binder type is the primary affecting factor, followed by the SBS grade.