No abstract

No abstract

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.

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.

The need for automated production plans has evolved over the years due to internal and external drivers like developed products, new enhanced processes and machinery. Reconfigurable manufacturing systems focus on such needs at both production and process planning level. The age of Industry 4.0 focused on mass customization requires computer aided planning techniques that are able to cope with custom changes in products and explores intelligent algorithms for efficient scheduling solutions to reduce lead time. This problem has been categorized as NP-Hard in literature and is addressed by providing intelligent heuristics that focus on reducing machining time of the products at hand. However, as 70% of the lead time is consumed in non-value added tasks, it is fundamental to provide modular solutions that can reduce this time and handle part variety. To address the subject, this paper focuses on the generation of automated process plans for a single machine problem while focusing on reducing time lead time. Two evolutionary algorithms (EAs) have been proposed and compared to answer complex problem of process planning. A modified genetic algorithm (GA) has been proposed in addition to cuckoo search (CS) heuristic for this discrete problem. On testing with selected benchmark part ANC101, significant improvement was seen in terms of convergence with proposed EAs. Moreover, a novel Precedence Group Algorithm (PGA) is proposed to generate quality input for heuristics. The algorithm produces a set of initial population which significantly effects the performance of proposed heuristics. For the discrete constrained process planning problem, GA outperforms CS providing 10% more feasible scheduling options and three times lesser run time as compared to CS. The proposed technique is flexible and responsive in order to accommodate part variety, a necessary requirement for reconfigurable systems.