Recent Advances in Operations Management and Optimization

In this study, an inexpensive fundamental robotic end effector module was investigated in order to make it easier to conduct robotic grasping research. The three-finger under actuated robotic gripper was designed in three dimensions and produced using a servomotor actuator that is readily available off the market and a small number of 3D printed parts. Following a comprehensive explanation of an under actuated finger, gear train mechanisms, in addition to general gripper assembly design, an illustration and explanation of the grabbing of objects with varied geometries follow. For research and instructional reasons, robotic researchers can utilize the offered open-source gripper design as a base to create custom robotic end effectors. An innovative prototype and a thorough introduction as well as an explanation of the gripper design idea are given. Using a four-bar linkage mechanical system and a single off-the-shelf actuator, it is demonstrated that the proposed robotic gripper with a single actuator satisfies the proposed objectives for its simple mechanical structure, low cost, and relatively high payload compared to similarly sized tendon-driven robotic end effectors.

In the present scenarios, ceramic matrix composites (CMCs) have been mixed under the action of ball milling operations. Generally, the ball milling processes have been implemented by either rotation or reciprocating of the mixing drum. In a rotating type mixing drum, the spherical steel or ceramic balls rotate inside the cylinder under the action of centrifugal forces and generate the problem of sticking the materials on the inner circular wall sections. The problem with the reciprocating mixing drum is that the spherical balls are sedimented in the solution under the action of gravity force. In both cases, the non-uniform and heterogeneous phases of slurry have been observed. The main objective of this research work is to eliminate these critical issues through the concept of kinematics and mechanism, named as oscillating drum ball milling process.

Human subjects are exposed to vibration while performing different activities in both sitting and standing postures. The amplitude and intensity of vibration vary according to road conditions, environmental conditions, time and other related factors. It becomes necessary to find out the effect of vibration on human subjects in terms of transmissibility, natural frequencies and resonant frequency. In the present study, a 4-layer CAD model of a human subject has been used to perform a harmonic analysis, i.e., applying a sinusoidal input with an acceleration of 1 m/s² and a frequency range of 0–20 Hz in the vertical direction. The CAD model of the human subject has been developed using 95th percentile anthropometric data and consists of four different anatomy layers, i.e., organs, skin, muscles and bones. The feet-to-head transmissibility has been evaluated for each posture and compared with the available experimental results in the literature. The transmissibility in sitting posture was found to be more in comparison to standing posture.

Rigid linkage mechanism has narrow accuracy and repeatability. The mechanism offers motion between the joints. A different approach has been made to develop S-type flexure mechanism for high-precision systems. This paper presents the flexural mechanism design with the building blocks. This S-shaped mechanism is developed using a numerical approach to get linear motion. The mechanism is incorporated with a voice coil motor (VCM), optical encoder, and dSPACE 1104 R&D microcontroller board. The S-shaped mechanism is integrated, designed, and developed with system integration and identification of the system. The proportional–internal–derivative (PID) controls the implementation of the mechanism. The system identification is done and performance is evaluated for the natural frequency, damping factor and also evaluated by experimentation for the same. The transfer function is used to build the control system and parameters are tuned with the help of PID. PID implementation in real time imparts the accuracy for positioning at a high scanning speed of 0.5 mm/s.

Health care is the world’s largest and fastest-growing sector- both per revenue as well as employment. Many operation management techniques have been implemented in the healthcare sector to improve the utilization of resources and the performance of systems. Theory of constraints was extensively implemented and resulted in positive outcomes in the healthcare sector in the literature. Identification of constraints is very crucial for system improvements and a dice game was introduced and also witnessed a number of applications in different environments. But for the healthcare industry, it is still novel. In this paper, we tried to identify the constraints in the complex and uncertain environment of the healthcare sector. Hence, this paper will guide working academicians and professionals to understand the concept of the dice game for better improvement of healthcare units.

Agriculture is the foundation of India and can play an important role in the Indian economy. Rice is a commonly consumed staple food, particularly in Asia, which accounts for about 90% of total rice production all over the world. Rice is a key resource in India and essential for all human beings, but farmers are facing more paddy cultivation problems. The two traditional methods of establishing rice are direct seeding, rice, and transplanting. Rice is cultivated manually in direct seeding while rice is cultivated by the computer in transplantation. Farmers face numerous problems in the manual cultivation of paddy, such as backbone pain and wages for workers. Due to greater yield and less weed growth, the transplanting method is more common among farmers compared to direct-seeded rice. A compact, low-cost paddy transplanter was developed and tested to eliminate the above-mentioned drudgery. Field trials were performed in the Bhandara district of Maharashtra. In terms of field efficiency, field capacity, and fuel consumption, the performance of the produced paddy transplanter was found to be very satisfactory compared to the conventional manual transplanting process. After the reduction of the cost of the machine, this study indicates a simple design and the holder will make the job more efficient. In comparison to the manual process of paddy transplantation, the system was found to be farmer-friendly and feasible in terms of time, money, and labor requirements.

With the advent of Industry 4.0, more emphasis is currently being placed on the adoption of digital technologies in all areas. The current study investigates the effect of Lean-Green-Six Sigma practices on organizational performances (i.e. Operational and Financial) in relation to developing nations. A cross-sectional data set was gathered from 120 Indian manufacturing companies, and machine learning techniques were used for data analysis and result interpretation. This study gives an enhanced comprehension of ML advances in embracing Lean-Green-Six Sigma rehearses. The results show that Lean-Green-Six Sigma rehearses emphatically influence the operational and financial performance of the organizations as the adjusted R square value of 70.6 and 68.2% with P values (≤ 0.05) satisfy the hypothesis. The results likewise offer a strategy structure for supervisors, policymakers, and makers to advance Lean-Green-Six Sigma rehearses in organizations. Although a few late studies have attempted to research the force of LGSS rehearses on maintainability execution, be that as it may, not many examinations have dissected the impact of LGSS through the reception of the ML approach with regard to arising economies.

Finding the best routes for numerous vehicles travelling to a collection of places is done using the Vehicle Routing Problem (VRP). In logistics, the distributor's total company performance is significantly impacted by transportation costs. Due to the scale of the Vehicle Routing Problem that needs to be solved, commercial systems employ heuristics. The Vehicle Routing Problem is employed in this study to streamline the distribution network for LPG cylinders. The distributor needs this optimization because the distribution staff's present system, which involves manually determining the delivery order, is inefficient. The goal of minimizing the overall transportation cost is achieved by formulating a Linear Programming Problem (LPP).

IoT adoption is still in its infancy in developing nations like India, despite considerable policy interest and the tremendous potential benefits it offers the healthcare supply chain. The purpose of this article is to identify and examine different barriers that could prevent the healthcare sector from using IoT. Fourteen significant barriers to IoT adoption were identified during the literature review phase. Some barriers were identified during thinking sessions with experts from various fields. The barriers are divided into cause and effect components using the Pythagorean fuzzy DEMATEL technique. As a conclusion, six factors belong to the impact group and eight elements belong to the cause group. ‘High implementation and operating cost’ is identified as the important barrier in the effect category and ‘Complex architecture’ is identified as an important barrier in the cause category as per (D + R) values. ‘Lack of awareness about lot benefits’ is identified as a non-influencing and not affecting barrier. Findings will assist decision-makers in comprehending the causes and effects of IoT acceptance.

India follows China as the world's top fruit producer. A variety of factors faced at various stages along the supply chain cause 30–33% of the produced produce to be wasted annually. One of these factors is the storage and transportation of substandard fruit. In order to classify and grade fruits, this work intends to develop an efficient and highly accurate image processing model. To do this, we created a base Convolutional Neural Network (CNN) model and compared it with the modified pre-trained ResNet models. On the basis of the selected performance criterion, a thorough study of the models and comparison of them was conducted. All of the pre-trained ResNet models outperformed our base 3-layer CNN model, which had 83.8% accuracy, with Resnet18 and Resnet34 achieving the maximum accuracy of 97.30%. The created model can be incorporated into a real-time image processing system to guarantee that quality standards are adhered to across the whole supply chain.

The Indian military base is in the process of rapid modernisation and various methodologies to achieve it have been successfully initiated by the Ministry of Defence (MoD). India is transforming its defence manufacturing capabilities by putting into action G2G collaborations positively. The ‘Make in India’ initiative has been globally recognised today. Indigenisation has been widely acknowledged and executed by many leading defence companies in India, like DRDO, Larsen and Toubro, MKU, Mahindra Defence Systems, Bharat Forge Limited, Tata Advanced System Limited, Ashok Leyland, etc. SMEs in India have also supported this drive up to a certain extent. However, to achieve complete indigenisation and ‘Make for World’, SMEs have a major role to play. In order to provide further momentum to this, this study strongly pitches two methodologies to expedite the process of indigenisation. The methodologies emphasise creating synergy between apex industry associations and other industry associations, thereby providing more opportunities for SMEs to enter the defence market. These proposed methodologies are expected to help the decision/policymakers to expand the indigenisation process at a faster rate. A supporting framework has been established further to deepen the approach towards proposed methodologies. The study is expected to further expand and encourage discussions on faster indigenisation. Finally, the study concludes with future research directions to support combat services.

Automotive supply chain has undergone a shift in customer preferences towards vehicle features. The COVID pandemic increased demand for personal mobility thus affecting the Indian economy and changing customer attitudes towards cars. Also, the critical part shortages caused repercussions throughout global supply chains. Due to this, it is now necessary to analyse vehicle attributes. For each new demand, the supply chain's response should be fresh and distinct. Supply chain companies are working to improve efficiency and cost-effectiveness. BDA can aid SCM by analysing customer preferences. This study aims to enhance responsiveness by using big data and MCDM techniques for analysing vehicle attributes. The findings of this research will help supply chain professionals form strategies for improved responsiveness.

Lean warehousing is a formulation that extends resourcefulness consumption without loss of quality and productiveness. Lean warehouses strive to do more with a minor, thereby streamlining the working operations. Lean warehousing statement that allows businesses to deliver the goods. Lean warehousing is a commercial activity in which the use of time, functional area, and resources in a warehouse are made more efficient through automation and proactive planning, enhancing customer satisfaction. According to the lean intellect, the performance and efficiency of warehouse operations depend on the layout planning, material handling methods, and mode of transportation. In this paper, the contemporary literature on the entire methodology of the lean warehouse structure and related tools is explored, used for reducing inefficiencies and improving work productivity from the significance of customer satisfaction. The proposed gaps would afford an emerging road map for research in existing and newfound unexplored directions in lean warehousing operations.

The inverse kinematics of the 3-link robotic manipulator was solved utilizing the moth-flame optimization (MFO) algorithm in this study. The outcomes were compared to those obtained using other optimization methods, including the grey wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm and whale optimization algorithm (WOA). First, the transformation matrices and D-H values of the robotic arm are generated. The end-effector position equations are then developed using the general transformation matrix. Using the MFO, PSO, GWO, and WOA, this robotic manipulator's end-effector position in the working area is estimated. A fitness function is used to estimate the position error or the distance between the current position and the desired place. By utilizing the fitness function to minimize the position error, the inverse kinematics solutions were produced. These algorithms were examined in this study, two distinct examples were used. Error in position and time to solve were estimated in Case-I for one place in the workspace, whereas Case-II estimated error in position and time to solve for 20 arbitrarily chosen areas of the workspace. By comparing it to case-I, case-II demonstrates the superiority of the MFO method over additional optimization techniques (PSO, GWO, and WOA). The MFO algorithm performs significantly better than PSO, GWO, and WOA algorithms with regard to errors in position error and time to solve, according to the results.

The subsequent use of fossil fuels for automobiles has increased the emission rates, deteriorating the quality of the environment. Increased use of automobiles has caused scarcity of fossil fuels hence giving rise to usage of renewable energy sources. In order to generate electricity to charge electric vehicles, the power has to still be extracted from fossil fuels hence to provide abundant power source, renewable energy is used. This paper focuses on the usage of solar power to charge a prototype of an electric vehicle. Since solar power independently cannot be directed to the power system of the vehicle, a lead acid battery with a charge controller was incorporated into the vehicle in order to store and charge the system.

Biotechnological approaches have emerged as an efficient and promising solution for the management of PW across the globe. The annual production of plastics has drastically increased in the recent few years. Numerous methods are employed for the disposal of PWs such as recycling, incineration, and landfilling. Even though recycling is a good waste management strategy it also has its own drawbacks. During recycling, many toxic pollutants are released directly into the environment, which are very harmful to the whole ecosystem. Additionally, incineration accounts for the release of toxic gases in the environment and landfills require a huge proportion of land which is not a great attribute for proper waste management. Here biotechnology comes into play as it is a more suitable and sustainable approach for proper waste management of plastic polymers without harming the natural ecosystem. These approaches utilize microorganisms such as bacteria, actinomycetes, and fungi to degrade the plastic polymers into monomers which are further used in the recycling process. These microorganisms have the ability to break down complex polymers into simpler molecules which can be used as a source of energy or as a raw material for the production of new plastics. The most commonly used method for PW management is biodegradation where the plastic polymers are degraded by the action of microbes into simpler molecules thus eliminating the PW from the environment. Biodegradation of plastics by microbes cannot occur or occurs at a very slow rate if the prior degradation by agents like UV radiation, water abrasion, photooxidation, and corrosion is not subjected. Additionally, enzymatic degradation is also commonly utilized nowadays where the enzymes extracted from different microbial sources degrade the synthetic polymers which aids in the removal of pollutants from the environment. Using biotechnology for the modification of these enzymes and enhancing their degradational efficiency is the key approach to overcoming the PW problem throughout the world. This review highlights the different biotechnological approaches for the efficient and sustainable degradation of plastic polymers thus contributing to PW management efficiently around the world.

Since the economic liberalization of the Indian market in the early 1990s, the Indian automobile industry has grown abruptly which consists of vehicle and component manufacturers. The global auto companies have urged the domestic sector to adopt supply chain practices. The advancement in technology has made it possible for the organization to expand its business globally without the addition of any surplus labor, space, area, etc. The decision support, automation, predictive and prescriptive outcomes are well transformed by Artificial Intelligence in supply chains. AI has been proven as a fortune in the world of automation for industrial setups. Without its help, tasks such as smart decision-making, research and data analysis, minimizing errors and business continuity would not have been trouble free. The Indian Automobile supply chains have been broadly classified into three categories namely; Passenger Vehicles, Commercial Vehicles and Two/Three Wheeler Vehicles respectively. This research paper aims to find the enablers and drivers that boost up the path of implementation of AI before the Raw material suppliers; component manufacturers-sub assembles, distribution channels (comprising of dealers, retailers, service providers and finally the end users (customers). The enablers and their effects are deeply analyzed for all the three above-stated segments of the Indian automobile supply chains.

Work-related Musculoskeletal Disorders (WMSDs) are on the rise in almost every industrialized country. For countering the effects of WMSDs posture analysis tools are used by ergonomic practitioners. The collection of posture analysis tools is huge, with the likes of RULA, REBA, QEC, OCRA, JSI, and others. Here comes the use of the Fuzzy Analytic Hierarchical Process (F-AHP). Recently, F-AHP is used extensively for the selection of optimal option out of a set of choices. F-AHP is primarily based on the Analytic Hierarchy Process (AHP), it is a Multi-Criteria Decision-Making (MCDM) model, which uses the Fuzzy theory. In this research, F-AHP is used to evaluate five posture analysis tools, viz. RULA, REBA, QEC, OCRA, and JSI. A hierarchical structure was constructed by considering Work posture, Frequency/Repetition, and Load/Weight handled as factors (level 1) and RULA, REBA, QEC, OCRA, and JSI as the choices (level 2). The available epidemiologic evidence and views of experts were prime drivers in conducting this research. MS Excel was used to conduct the requisite calculations of AHP. CR was found to be less than 0.10 in each case, suggesting that there is no anomaly in consistency. This research led to the conclusion that QEC was overall best among the tools selected, followed by JSI, REBA, RULA, and OCRA respectively.

Electric vehicles (EVs) have emerged as the future of the automotive industry. Technological improvements have further reduced the cost of EVs making them more economically viable and an environmentally friendly alternative to IC engine-based vehicles. However, there are several parameters that influence consumers’ decisions to purchase an EV. This article explores these parameters and the impact they have on consumers’ perceptions and expectations of EVs. A survey was conducted for various economic, technological, social, and environmental aspects to understand the perceptions related to electric vehicle acceptance and its expectations. The paper discusses the results of a survey conducted to analyze the various factors that influence consumer perceptions and expectations of electric vehicles (EVs). The survey results indicate that the cost of purchasing an EV, insurance affordability, increasing price of petrol and diesel, mileage (energy consumption), EV incentives, and battery replacement/repair cost have a moderate to very strong impact on consumer decisions. The results also show that the cost of maintenance, time required for maintenance, and ease of maintenance have a moderate impact on consumer perceptions and expectations. The survey results further indicate that the charging time and availability of charging infrastructure have a moderate and low impact, respectively, on consumer decisions. The findings of this survey can be useful for policymakers, manufacturers, and other stakeholders to develop strategies to increase the adoption of EVs.

A bibliometric study of service quality in health care was performed due to its growing importance and to examine service quality trends in existing healthcare literature. The research articles related to the quality of healthcare services were assessed by using the Bibliometrix software. Using the Scopus database, 1202 documents were retrieved and analyzed in the present study. The co-occurrence analysis reported that China and the United States were leading countries in this domain. Also, year-wise trends, most valuable sources, most frequent keywords, and leading authors were identified. This paper recommends valuable insights for policymakers, practitioners, and researchers to learn the potential of service quality in healthcare. This article will influence future needs and trends in healthcare service quality.

Gesture recognition systems are gaining popularity nowadays because they can interface with machines via human–computer interaction. The development of such systems helps to develop a more natural communication between man and machine. These setups made humans directly instruct the machine as per their needs. A vision-based gesture recognition setup has been developed and modelled in the present study. The gesture recognition system has been developed by a Linux processing device with a camera and an output control device. MediaPipe Hands model has been used for gesture recognition. Programming for the used model has been done using Python programming language. The Arduino UNO has also been programmed to convert the gestures into desired outputs. The developed vision-based gesture recognition system was found to work effectively for human hand gestures.

Preventive Maintenance (PM) has been playing a key role in the reliability and sustainability of industrial operations by preventing the failure of various components resulting in a breakdown. Further, this breakdown in industrial operations inhibits the scale and goals of production thereby affecting financial growth. Butter oil is one of the most consumed oils in the world, so its production in processing plants should be monitored and optimized maintenance schedules for the equipment should be adopted which affects the production in a plant. In the present paper, the preventive maintenance (PM) modeling of the butter oil processing plant is carried out using Stochastic Petri nets (SPN). The optimal preventive maintenance schedules are decided under the various constraints of maximizing availability, minimizing cost, and maximizing revenue generation. The programming package Mathematica is used to solve the complex equations of the framework in maximizing the availability, revenue per unit, and minimizing cost. The efficacy of the model is assessed by performance parameters of plant availability and maintenance cost. It is found from investigations that the system availability could be increased from 0.81 to 0.91 and the cost could be reduced from 40 to 32%. Hence, it is concluded that the implementation of preventive maintenance schedules could be useful in achieving production goals.

In today’s golden era of digitization, every industry has been focused on the adaptation of new technologies to boost their market dominance and company revenue. For every industry, supply chain management is the main area of concentration in order to develop brand value and seize local and global markets. Supply chain management (SCM) is progressively becoming a pillar for any firm. It is the top-down and bottom-up centralized control of the flow of goods, information, services, and money. It encompasses all processes, from acquiring raw materials to manufacturing, marketing, regulating supply and demand, and creating final products in response to market needs. Industries may be able to reduce excess costs associated with their products and deliver the products to customers in a more effective and efficient manner by managing proper supply chain operations. The primary customer demand is for a high-quality product at a reasonable price and with prompt delivery. To satisfy customer demand and enhance profit by capturing market share, companies have to focus on digitization in supply chain management. Digitization aids in forecasting demand fluctuations, reducing unnecessary inventory management and managing back orders. Backorders, improper data handling, unexpected delays in delivering products, not being able to forecast demand properly, and the bullwhip effect are all major threats to the overall supply chain profitability. In this study, the main focus is the usage of artificial neural networks to predict backorder in supply chain management to overcome demand fluctuations.

Indeed, the growing population and the escalating number of vehicles on the roads have given rise to a pressing concern in many large cities around the globe: traffic congestion. As urban areas become more densely populated and transportation options expand, the issue of traffic jams has become increasingly prevalent, impacting the daily lives of millions of people. Finding effective solutions to alleviate this problem has become a top priority for city planners and policymakers alike. Over the years researchers have suggested several multi-disciplinary methods such as Intelligent Transportation Systems (ITS), congestion pricing, improvements in public transit, promotion of alternative routes, prediction algorithms, and simulation techniques in literature. Many of these methods are based on real-time data and become more effective with technological advancements. This study is field field-based pilot study carried out in a major metropolitan city in India. The purpose of this study is to predict traffic congestion accurately and implement ground-level solutions with the help of simulation modeling in different scenarios. In this study, a simulation model was developed using a particular dataset, and the proposed solution model was tested and improved. Simulation of alternative routes has proven to be effective in reducing travel times, improving safety, and reducing environmental impacts. Promoting alternative routes has also shown potential to reduce congestion at specific points and improve public health by reducing emissions. Overall, a multi-disciplinary approach that considers various factors and ongoing evaluation is necessary for effective traffic congestion problem-solving.