This decade has observed an upsurge in the eco-friendly materials because of the development of composites using natural fibers. These composites are made from renewable resources and are gaining popularity for their high performance in engineering applications. Industries are increasingly interested in using materials that are sustainable and resource-efficient. This research proposes a new innovative hybrid composite developed using coir and kenaf fibers, carbon nanotubes acting as a nanofiller, and a matrix made up of epoxy resin, detailing how they are fabricated, tested, and optimized based on different weight percentages. The weight percentages considered for CNT nanoparticles are 0, 1, 2, and 3 wt.%, coir, and kenaf fibers are considered in weight percentages of 12, 13, 14, and 15, whereas thickness is regarded as 2,3,4 and 5 mm. This research evaluates the mechanical features of this hybrid composite fabricated using a vacuum bag molding process. The different composite samples are tested using mechanical tests and subsequently optimized using the design of experiment (i.e., Taguchi method) and analysis of variance (ANOVA) method to arbitrate the best weight percent combination of the innovative hybrid composite. On the basis of the optimization results, the best composite sample obtained includes, 3 wt% of CNT, 15 wt% of kenaf, 15 wt% of Coir, and 4mm thickness of the sample, as it yields the highest tensile modulus and strength among all the hybrid composite samples. The outcomes from the research indicate that the hybridization of kenaf fibers into coir fibers, along with CNTs as fillers in the hybrid composite has enhanced the overall tensile strength, and flexural strength of the hybrid composite in comparison to the coir composite and kenaf composite alone, depicting the superiority of natural fiber hybrid composite over synthetic fiber hybrid composite.

In the current decade, a number of industries have moved their attention towards emerging sustainable technologies in order to better support socio-economic and environmental considerations. The present research investigates a unique hybrid composite developed by the amalgamation of natural kenaf-coir fibers, with resin of epoxy, incorporated with titanium carbide (TiC) nanoparticles. This study also presents the development process involved in manufacturing the composites, along with mechanical testing and optimization of these composite samples. The nanofillers of TiC are utilized in wt. percentages of 0%, 3%, 4%, and 5%, while coir and kenaf fibers are incorporated at 0%, 3%, 4%, and 5% by weight, and the thickness of the samples is varied at 2, 3, 4, and 5mm. The mechanical attributes of composites are evaluated using a vacuum bag molding process, with subsequent testing and optimization performed through Taguchi and ANOVA analysis to discover the optimal sample combination. The findings indicate that the most effective composite formulation includes 4% TiC, 5% kenaf, 5% coir, and a thickness of 3 mm, which provides the highest tensile modulus and strength among all tested samples. The integration of kenaf fibers with coir fibers and TiCs as fillers significantly improves the tensile and flexural attributes of the hybrid composite in contrast to composites made with coir or kenaf fibers alone.

In recent years, nano-filler-based hybrid composites have gained significant attention from the research community; The nano-filler-based hybrid composites can have potential applications in numerous sectors. Nano-fillers are bringing a leading development in material science and natural fibers-based composites. The present study considers the impact of various weight percentages of nano-titanium oxide (NTiO2) fillers (2%, 4%, and 6%) on the elastic features of novel hybridized banana-hemp fiber-reinforced epoxy composites. The proposed composite is analyzed for its elastic properties like longitudinal and transverse elastic modulus, axial Poisson's ratio, and axial shear modulus using homogenized micromechanical models, namely, Mori-Tanaka (M-T) model, Generalized self-consistent (G-SC) model and Modified Halpin-Tsai (M-HTS) model. The composite is modeled using one layer of banana fiber, one layer of NTiO2 and epoxy, and one layer of hemp fiber. All three layers of the composite are arranged in the sequence of banana fiber at 450, a layer of NTiO2 and epoxy at 00, and hemp fiber at 450. The proposed composite's vector sum deformation and strength are examined by employing the ANSYS APDL application. The results obtained in this study are compared with the experimental work mentioned in the literature. The composite reinforced with six weight% NTiO2 has the highest mechanical strength, and the modified Halpin-Tsai (M-HTS) model is the most effective in calculating the elastic features of the proposed composite. In addition to the above, the hybridization effect for the proposed composite is also estimated to analyze the tensile failure strain of banana and hemp fiber in the proposed hybrid composite structure.

The advent of Industry 4.0 has revolutionized manufacturing, integrating advanced technologies to enhance efficiency and sustainability. However, the transition to sustainable green manufacturing presents numerous challenges. This paper analyzes these challenges using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). By incorporating expert opinions and fuzzy logic, various obstacles are evaluated and prioritized in the implementation of green manufacturing practices in the context of Industry 4.0. The analysis reveals that market uncertainty in the economic landscape ranks as the top challenge, followed by high costs of implementation, maintenance, security, and integration. Uncertain benefits and trade-offs are also found as significant barriers. Key factors include the need for substantial investments, cybersecurity concerns, integration difficulties, and the complexities of predicting returns on investment. From the study, it is also evident that the impact of Industry 4.0 on supply chains and emissions from Electronics manufacturing is also a critical issue. The study provides actionable insights and strategic recommendations for policymakers and industry leaders to facilitate the adoption of sustainable green manufacturing practices in the era of Industry 4.0.

This paper aims to improve the ABC analysis method used for inventory management by applying the Pythagorean Fuzzy TODIM approach. ABC analysis is one the well-known and widely used inventory classification techniques which divides inventory items into three categories according to their importance and value. However, the traditional ABC analysis does not consider the imprecision and vagueness of real-world inventory data, which can lead to inaccurate results and poor inventory management decisions. The proposed approach enhances the traditional ABC analysis by incorporating fuzzy numbers to be considered in real-world inventory data. The improved ABC analysis helps companies to optimize inventory levels, reduce costs, improve customer service, and increase overall operational efficiency. To check for the reliability and effectiveness of the developed model under different scenarios sensitivity analysis is conducted. Additionally, the comparative analysis among other existing models further demonstrates the model's accuracy. The model prepared shows that the Pythagorean Fuzzy TODIM approach is superior to the conventional ABC analysis in terms of reliability and dealing with the uncertain inventory data. Overall, this paper provides a novel and effective approach to inventory management and offers valuable insights for practitioners and researchers in the field.

With the inception of high-speed internet data services and ever-growing technical advancement in manufacturing technology, the integration of production systems and the internet of things to produce different types of jobs via cloud computing has become possible. The internet-enabled advanced automatic production systems can be referred to as the cyber-physical production systems (CPPS). The use of CPPS via cloud computing and the internet of things (IoT) can offer high productivity and high flexibility for the production of jobs in a dynamic production environment with varying specifications. The aim of this paper is to present a generalized synergic framework between different production facilities locating at different geographical locations to realize an energy-saving and efficient cyber-physical production system for the production of different types of jobs in the context of the industry 4.0 and beyond. In addition to the above, the study also identifies a need to address large scale multi-objective optimization issues to make the best decisions for different combinatorial production scenarios by using CPPS that are functioning in smart production facilities at different geographical locations.

The computer numerical control (CNC) machines are chiefly used for the production of jobs with high accuracy and high speed. The CNC machining centers perform the machining operations according to the given program instructions which are commonly programmed by a CNC programmer. In this paper, a procedure to develop an automatic CNC program for machining of different types of holes by using different machine learning algorithms is developed. The machine learning algorithms namely support vector machine (SVM) and restricted boltzmann machine algorithm (RBM) with deep belief network (DBN) are used for the au-tomatic development of CNC machining programs of different types of holes. Initially, the position and other parameters of machining operations are identified and thereafter the CNC machining program is developed by using the MATLAB application. The automatically de-veloped CNC programs are tested on a CNC simulator. It is found that the application of RBM machine learning algorithm with DBN outperforms the SVM machine learning algo-rithm for the development of automatic CNC machining program for the machining of blind holes, through holes, counterbores and countersink operations.

The real-time scheduling of automatic guided vehicles (AGVs) in flexible manufacturing system (FMS) is observed to be highly critical and complex due to the dynamic variations of production requirements such as an imbalance of AGVs loading, the high travel time of AGVs, variation in jobs, and AGV routes to name a few. The output from FMS considerably depends on the effi-cient scheduling of AGVs in the FMS. The multi-objective scheduling decisions for AGVs by nature inspired algorithms yield a considerable reduction throughput time in the FMS. In this paper, investigations are carried out for the multi-objective scheduling of AGVs to simultaneously balance the workload of AGVs and to minimize the travel time of AGVs in the FMS. The multi-objective scheduling is carried out by the application of nature-inspired grey wolf optimization algorithm (GWO) to yield a balanced workload for AGVs and also to minimize the travel time of AGVs simultaneously in the FMS. The output yield of the GWO algorithm is compared with the results of benchmark problems from the literature. The resulting yield of the proposed algorithm for the multi-objective scheduling of AGVs is observed to outperform the existing algorithms for scheduling of AGVs.

Sustainability in project operations such as financial, social and environmental sustainability is one of the most prominent issues of the present times to address. The increased focus on sus-tainable business operations has changed the viewpoint of researchers and corporate community towards the project management. Today sustainability in business operations along with sustain-ability of natural and environmental resources are of paramount significance which has further caused a huge impact on conception, planning, scheduling and execution of the project manage-ment activities. In this paper, a literature review between 1987 and 2018 on different issues af-fecting the sustainability in project management is carried out. The present study also identifies and discusses the future possibilities to apply computational procedures in order to estimate and optimize the sustainability issues in the management of projects, for example the computational evolutionary algorithms can be applied to formulate the multi-objective decision-making problem after considering critical factors of sustainability in the projects and then yielding optimized solu-tions for the formulated problem to achieve sustainability in the projects. A new integrated framework with the inclusion of feedback function for assessment of each decision and actions taken towards the sustainability of the projects is also identified and presented.

Use of Automated guided vehicles (AGVs) is highly significant in Flexible Manufacturing Sys-tem (FMS) in which material handling in form of jobs is performed from one work center to an-other work center. A multifold increase in through put of FMS can be observed by application of multi load AGVs. In this paper, Particle Swarm Optimization (PSO) integrated with Memetic Algorithm (MA) named as Modified Memetic Particle Swarm Optimization Algorithm (MMP-SO) is applied to yield initial feasible solutions for scheduling of multi load AGVs for minimum travel and waiting time in the FMS. The proposed MMPSO algorithm exhibits balanced explora-tion and exploitation for global search method of standard Particle Swarm Optimization (PSO) algorithm and local search method of Memetic Algorithm (MA) which further results into yield of efficient and effective initial feasible solutions for the multi load AGVs scheduling problem.