Polypyrrole/multiwall carbon nanotube nanocomposites electropolymerized on copper substrate

Abstract Polypyrrole/multiwall carbon nanotube (PPy/MWCNT) nanocomposites were successfully synthesized by electropolymerization of MWCNTdispersed pyrrole solution on the surface of copper electrodes. The obtained nanocomposites were characterized with scanning electron microscopy (SEM), linear sweep voltammetry (LSV) and thermal gravimetric analysis (TGA). Polypyrrole structures which embraced the MWCNTs led to the formation of nanocomposite striated parallel walls. MWCNTs acted as appropriate substrates for electrodeposition of polypyrrole particulate structures and high yield synthesis of PPy was observed on them. Smooth PPy/MWCNT nanocomposite films were obtained on Cu electrodes by decreasing the potential scan rate. Thermogravimetric analysis showed that MWCNTs increased the thermal stability of polypyrrole

Facility location and distribution planning in a disrupted supply chain

Most facility location models in the literature assume that facilities will never fail. In addition, models that focus on distribution planning assume that transportation routes are disruption-free. However, in reality, both the transportation routes and the facilities are subject to various sorts of disruptions. Further, not many supply chain models in the literature study perishable products. In this paper, we address issues of facility location and distribution planning in a supply chain network for perishable products under uncertain environments. We consider demand uncertainty along with random disruptions in the transportation routes and in the facilities. We formulate a mixed-integer optimisation model. Our model considers several capacitated manufacturers and several retailers with multiple transportation routes. We investigate optimal facility location and distribution strategies that minimise the total cost of the supply chain. We demonstrate the effectiveness of our model through an illustrative example and observe that a resilient supply chain needs to have a different design when compared to a disruption-free supply chain. The effects of various disruption uncertainties are also studied through statistical analysis

Hybrid meta-heuristic algorithms for a supply chain network considering different carbon emission regulations using big data characteristics

Big data (BD) approach has significantly impacted on the development and expansion of supply chain network management and design. The available problems in the supply chain network (SCN) include production, distribution, transportation, ordering, and inventory holding problems. These problems under the BD environment are challenging and considerably affect the efficiency of the SCN. The drastic environmental and regulatory changes around the world and the rising concerns about carbon emissions have increased the awareness of customers regarding the carbon footprint of the products they are consuming. This has enforced supply chain managers to change strategies to reframe carbon emissions. The decisions such as an optimization of the suitable network of the proper lot sizes can play a crucial role in minimizing the whole carbon emissions in the SCN. In this paper, a new integrated production–transportation–ordering–inventory holding problem for SCN is developed. In this regard, a mixed-integer nonlinear programming (MINLP) model in the multi-product, multi-level, and multi-period SCN is formulated based on the minimization of the total costs and the related cost of carbon emissions. The research also uses a chance-constrained programming approach. The proposed model needs a range of real-time parameters from capacities, carbon caps, and costs. These parameters along with the various sizes of BD, namely velocity, variety, and volume, have been illustrated. A lot-sizing policy along with carbon emissions is also provided in the proposed model. One of the important contributions of this paper is the three various carbon regulation policies that include carbon capacity-and-trade, the strict capacity on emission, and the carbon tax on emissions in order to assess the carbon emissions. As there is no benchmark available in the literature, this study contributes toward this aspect by proposing two hybrid novel meta-heuristics (H-1) and (H-2) to optimize the large-scale problems with the complex structure containing BD. Hence, a generated random dataset possessing the necessary parameters of BD, namely velocity, variety, and volume, is provided to validate and solve the suggested model. The parameters of the proposed algorithms are calibrated and controlled using the Taguchi approach. In order to evaluate hybrid algorithms and find optimal solutions, the study uses 15 randomly generated data examples having necessary features of BD and T test significance. Finally, the effectiveness and performance of the presented model are analyzed by a set of sensitivity analyses. The outcome of our study shows that H-2 is of higher efficiency