Sustainable supplier selection and order allocation (SSSOA) is paramount to sustainable supply chain management. It is a complex multi-dimensional decision-making process augmented with the triple bottom line of sustainability. This research presents a multi-phase decision framework to address a SSSOA problem for the multi-echelon renewable energy equipment (Solar PV Panels) supply chain. The framework comprises of fuzzy Multi-Criteria Decision-Making techniques augmented with fuzzy multi-objective mixed-integer non-linear programming mathematical model. The various economic, environmental, and social objectives were optimized for a multi-period, multi-modal transportation network of the supply chain. The results show that among the various sustainable criteria selected in this study, product cost, environmental management system, and health and safety rights of employees are the most important for decision-makers. The results of the mathematical model highlighted the impact of multimodal transportation on overall cost, time, and environmental impact for all periods. An analysis of results revealed that transfer cost and customer clearance cost contribute significantly towards overall cost. Furthermore, defect rate was also observed to play a critical role in supplier selection and order allocation.

The electrical discharge machining (EDM) process is one of the most efficient non-conventional precise material removal processes. It is a smart process used to intricately shape hard metals by creating spark erosion in electroconductive materials. Sparking occurs in the gap between the tool and workpiece. This erosion removes the material from the workpiece by melting and vaporizing the metal in the presence of dielectric fluid. In recent years, EDM has evolved widely on the basis of its electrical and non-electrical parameters. Recent research has sought to investigate the optimal machining parameters for EDM in order to make intricate shapes with greater accuracy and better finishes. Every method employed in the EDM process has intended to enhance the capability of machining performance by adopting better working conditions and developing techniques to machine new materials with more refinement. This new research aims to optimize EDM’s electrical parameters on the basis of multi-shaped electrodes in order to obtain a good surface finish and high dimensional accuracy and to improve the post-machining hardness in order to improve the overall quality of the machined profile. The optimization of electrical parameters, i.e., spark voltage, current, pulse-on time and depth of cut, has been achieved by conducting the experimentation on die steel D2 with a specifically designed multi-shaped copper electrode. An experimental design is generated using a statistical tool, and actual machining is performed to observe the surface roughness, variations on the surface hardness and dimensional stability. A full factorial design of experiment (DOE) approach has been followed (as there are more than two process parameters) to prepare the samples via EDM. Regression analysis and analysis of variance (ANOVA) for the interpretation and optimization of results has been carried out using Minitab as a statistical tool. The validation of experimental findings with statistical ones confirms the significance of each operating parameter on the output parameters. Hence, the most optimized relationships were found and presented in the current study.

Dye-sensitized solar cells are gaining interest in the aerospace industry, extending their applications from solar-powered drones to origami-style space-based solar power stations due to their flexibility, light weightiness, and transparency. The major issue with its widespread commercial use is the employment of expensive Pt-based counter electrodes. In this study, an attempt has been made to replace the Pt with Polyaniline (PANI)/Molybdenum sulfide (MoS₂) nanocomposite. The nanocomposites i.e., PANI-0.5wt% MoS₂, PANI-2wt%MoS₂, PANI-5wt%MoS₂, and PANI-7wt%MoS₂and PANI-9wt%MoS₂, have been synthesized and compared with standard Pt-based CE. Scanning electron microscopy, transmission electron microscopy, and X-ray diffraction methods have been utilized to study both surface morphology and structural composition. Fourier transform infrared has also been used to identify redox-active functionalities. Electron impedance spectroscopy and cyclic voltammetry have been employed to study electron transfer and catalytic activity. Finally, I-V testing has been conducted using a sun simulator. A maximum efficiency of 8.12% has been observed with 7wt% MoS₂ in the PANI matrix at 6 µm thickness, which is 2.65% higher compared to standard Pt-based CE (7.91%). This is due to high electronic conduction with the addition of MoS₂, improved catalytic activity, and the high surface area of the PANI nano-rods.

High-speed machining is considered to be a promising machining technique due to its advantages, such as high productivity and better product quality. With a paradigm shift towards sustainable machining practices, the energy consumption analysis of high-speed machining is also gaining ever-increasing importance. The current article addresses this issue and presents a detailed analysis of specific cutting energy (SCE) consumption and product surface finish (Ra) during conventional to high-speed machining of Al 6061-T6. A Taguchi-based L16 orthogonal array experimental design was developed for the conventional to high-speed machining range of an Al 6061-T6 alloy. The analysis of the results revealed that SCE consumption and Ra improve when the cutting speed is increased from conventional to high-speed machining. In particular, SCE was observed to reduce linearly in conventional and transitional speed machining, whereas it followed a parabolic trend in high-speed machining. This parabolic trend indicates the existence of an optimal cutting speed that may lead to minimum SCE consumption. Chip morphology was performed to further investigate the parabolic trend of SCE in high-speed machining. Chip morphology revealed that the serration of chips initiates when the cutting speed is increased beyond 1750 m/min at a feed rate of 0.4 mm/rev.

Synchromodality is the key to finding sustainable solutions for logistics, especially across larger networks. The era of the COVID-19 pandemic has brought special attention to the disruptions in demand and supply across the world and has accentuated the need for sustainable transportation networks to handle such anomalies in supply chains. The proposed research develops a mathematical model for an intermodal transportation network and investigates the model on one of the largest and most widely discussed supply chain projects of the One-Belt-One-Road (OBOR) initiative. The proposed bi-objective model focuses on time and cost functions with rail, roads, and ships as modes of transportation. A detailed analysis was performed on various mode alternatives and links to evaluate their performance. The study provides an insightful understanding of the network with several suggestions. In contrast to roads and trains, container ships depict a fourfold increase in fuel consumption for an average ship weighing 4500 TEUs with the increase in shipping speed. It was concluded that increasing port capacity and reducing custom clearance time can have a major impact on lead times, and this is directly influenced by a country’s ease of doing business. Moreover, with its several branches, the OBOR initiative can provide a robust supply chain with increased logistical capacity.