Manufacturers today need to optimize their fabrication runtime decision by meeting short customer order due dates externally and managing the potentially unreliable machines and manufacturing processes internally. Outsourcing and overtime are commonly utilized strategies to expedite fabricating time. Additionally, detailed analyses and necessary actions on inevitable product defects (i.e., removal of scraps) and equipment breakdowns (such as machine repairing) are prerequisites to fabrication runtime planning. Motivated by assisting today’s manufacturers decide the best batch runtime plan under the situations mentioned above, this study applies mathematical modeling to a hybrid fabrication problem that incorporates partial overtime and outsourcing, inevitable product defects, and a Poisson-distributed breakdown. We develop a model to accurately represent the problem’s characteristics. Formulations and detailed model analyses allow us to find the cost function first. Differential equations and algorithms help us confirm the gain function’s convexity and find the best runtime decision. Lastly, we use numerical illustrations to show our study’s applicability by revealing in-depth crucial managerial information of the studied problem.

The study examines the combined effect of rework, multiple shipments, postponement, and overtime producing common-component on a multiproduct vendor-client incorporated system. Clients’ product demand trend turns to diversity, quality, and rapid response in the current supply-chain environment. Under such a stiff competitive environment, today’s manufacturers must effectively plan their multi-item fabrication to boost utilization and product quality, minimize total relevant costs, and meet short given order lead time. By considering the commonality of the finished goods, required quality, and completion lead time, this study presents an exact model featuring rework of defects, multiple shipments, postponement, overtime producing the mutual component, and satisfying the market needs. Through the techniques of explicitly modeling, formulating, and system cost minimization, this study simultaneously derives the optimal cycle-time and shipping frequency for the studied problem. A numerical example helps show how our model works for any given parameter values and how the variation in single and multiple factors of the problem affects the crucial system performances (e.g., total uptimes, each relevant cost, utilization, total cost, etc.) A wide variety of today’s industries (e.g., automotive, household goods, etc.) and their related supply chains can utilize our decisional model to reveal in-depth managerial insights for planning their fabrication and shipments.

Expressive suppression is the process of decreasing, increasing, or maintaining emotional intensity over a period of time. It is employed as an immediate response to emotional stimuli and can result in negative psychological outcomes. This emotional regulation process can be harmful in the workplace, especially when one considers time pressures and the standard corporate hierarchical structure in modern America, which potentially also introduces stress into situations due to the “superior/subordinate” relationship. This study analyzed the correlation between self-reported high usage of expressive suppression and perceived stress and found that a correlation does exist between the emotion regulation technique and perceived stress. The study also reviewed other workplace situations and found that being in the presence of a superior also plays a role in this dynamic. The study highlights the stress impact of employing expressive suppression and the need for further research into factors within the workplace that impact an individual’s emotional regulation and impact overall stress.

Manufacturing firms operating in today’s competitive global markets must continuously find the appropriate manufacturing scheme and strategies to effectively meet customer needs for various types of quality of merchandise under the constraints of short order lead-time and limited in-house capacity. Inspired by the offering of a decision-making model to aid smooth manufacturers’ operations, this study builds an analytical model to expose the influence of the outsourcing of common parts, postponement policies, overtime options, and random scrapped items on the optimal replenishment decision and various crucial system performance indices of the multiproduct problem. A two-stage fabrication scheme is presented to handle the products’ commonality and the uptime-reduced strategies to satisfy the short amount of time before the due dates of customers’ orders. A screening process helps identify and remove faulty items to ensure the finished lot’s anticipated quality. Mathematical derivation assists us in finding the manufacturing relevant total cost function. The differential calculus helps optimize the cost function and determine the optimal stock-replenishing rotation cycle policy. Lastly, a simulated numerical illustration helps validate our research result’s applicability and demonstrate the model’s capability to disclose the crucial managerial insights and facilitate manufacturing-relevant decision making.

Condition-based maintenance (CBM) of industrial machines depends on the continuous, real-time monitoring of the machine’s operational condition via smart sensors attached to different components on the machine. The problem of regularly spaced missing data, which can occur due to a variety of hardware or software issues, is one that is often overlooked in the literature surrounding CBM in industrial machines. Such missing data can cause issues in interpreting the true operational state of the machine, which can reduce the effectiveness of CBM processes. In this paper, we examine the capabilities of five data imputation techniques for handling this regular missing data and examine the impact these techniques have on machine learning (ML) classification algorithms for machine fault diagnosis. We examine the following techniques: simple mean imputation, mean imputation with outliers removed, best and worst-case imputation, and previous day imputation. Each of these methods is configured with the specific parameters that they will only consider data from the previous 24 hours, to ensure that the data is recent, and adequately represents the current status of the machine. The efficacy of each method at accurately reconstructing the missing data and the impact they have on ML classification is recorded in the results. The models are evaluated on a real-world dataset and are evaluated on a variety of common performance metrics.

Welding is widely used in all industries, and its demand is drastically increasing. Today, all sectors of engineering need quality welders to meet their standards. Welders need years of experience and knowledge to meet those standards. They require lots of training, equipment, tools, and safety standards to master the welding skill. The cost of training is very expensive as they will be practicing every day with different materials. The purpose of this project is to train them in a new merged-up environment of the virtual and real world. As a result, this method would reduce training costs and enhance the safety of the users. This method is a medium that helps users to have a better understanding of welding operations and gives them the confidence to perform proficiently in reality along with the elimination of risk, liability, and injury.

Modern Smart Cities are highly dependent on an efficient energy service since electricity is used in an increasing number of urban activities. In this regard, Time-of-Use prices for electricity is a widely implemented policy that has been successful to balance electricity consumption along the day and, thus, diminish the stress and risk of shortcuts of electric grids in peak hours. Indeed, residential customers may now schedule the use of deferrable electrical appliances in their smart homes in off-peak hours to reduce the electricity bill. In this context, this work aims to develop an automatic planning tool that accounts for minimizing the electricity costs and enhancing user satisfaction, allowing them to make more efficient usage of the energy consumed. The household energy consumption planning problem is addressed with a multiobjective evolutionary algorithm, for which problem-specific operators are devised, and a set of state-of-the-art greedy algorithms aim to optimize different criteria. The proposed resolution algorithms are tested over a set of realistic instances built using real-world energy consumption data, Time-of-Use prices from an electricity company, and user preferences estimated from historical information and sensor data. The results show that the evolutionary algorithm is able to improve upon the greedy algorithms both in terms of the electricity costs and user satisfaction and largely outperforms to a large extent the current strategy without planning implemented by users.

Transnational producers facing the present-day competitive global supply-chain environments need to pursue the most appropriate manufacturing scheme, quality screening task, and stock shipping plan to satisfy customer’s timely multi-item requirements under minimum overall product fabrication-delivery expenses. This study develops a producer-retailer incorporated multi-item two-stage economic production quantity- (EPQ-) based system with delayed differentiation, expedited-rate for common parts, multiple deliveries plan, and random scrap. It aims to assist current manufacturing firms in achieving the aforementioned operating goals. Mathematical methods help us build an analytical model to explicitly portray the studied problem’s features and derive its overall system expenses. Hessian matrix equations and optimization approaches help us prove convexity and derive the cost-minimized fabrication- delivery decision. This study gives a simulated example to illustrate the research outcome’s applicability and the proposed model’s capabilities numerically. Consequently, diverse crucial information becomes obtainable to the manufacturers to facilitate various operating decision makings as follows: (i) the cost-minimized fabrication-delivery policy; (ii) the behavior of system’s overall expenses and operating policy regarding mean scrap rate, and different relationships between common part’s values and completion-rate; (iii) the system’s detailed cost components; (iv) the system’s overall expenses, utilization, and common part’s uptime concerning different common part’s expedited rates; and (v) the collective effects of critical system features on the overall expenses, uptime, and optimal cycle length, etc.

Concrete is the most significant source of construction in the construction industry of the world. However, concrete causes excessive production of cement, which is one of the key contributors of carbon dioxide emissions to the environment. To minimize the use of cement in concrete, various innovative materials are being added in concrete to make it sustainable. In this study, a comparative study between three fibers Carbon Fiber, Glass Fiber, and steel fibers was done to determine which one is the most suitable fiber. For this purpose, testing on specimens was done for tensile and compressive strength at 0.5% addition of each fiber. Testing was done after 3, 7, 14, and 28 days of curing. The results spelled out that the highest compressive strength of 37.53 MPa of cube specimens was found in carbon fiber after 28 days, and Glass fibers exhibited the lowest gain in strength at about 32.335 MPa. Carbon Fiber gained 28% more strength than the control mix. On the other hand, tensile strength was also found highest in carbon fibers i.e. for the cubes the maximum difference between different fibers inducted concrete samples is 28% approximately, and for cylinders it is 27%, respectively. On the other hand, the highest tensile strength of concrete was also gained with the carbon fiber at about 3.61 MPa. The same was found lowest in glass fiber at 3.12 MPa. Carbon fiber got about 44 % improvements in tensile strength.

New Product Development Process (NPD) is a key aspect of launching new and innovative products in the market. Many products fail in the market because of technical risks, financial risks and product development time risks. It is very important to understand the overall risk factors associated with different stages of product development so that risks can be mitigated effectively. This paper presents a methodology to understand the risk associated with the initial stages of NPD. Design flexibility is higher in initial design stages requiring minimum redesign efforts and costs. It is a great opportunity to deal with risk factors and uncertainties in initial design stages than the later design stages. Product development costs in initial stages are around 5 to 10 percent but impact is 70 to 80 percent so exploration assessment in initial stages of NPD can be hugely beneficial. Stage-wise risk assessment will also provide the details of risk associated with each stage, which will be helpful in implementing appropriate mitigation strategies. Since transition from one stage to another stage of NPD is independent of the previous stage, different NPD stages can be easily expressed by the transition state of the Markov process. In this paper, the Markov process has been used for the risk assessment of initial stages of NPD, keeping mitigation strategies in mind. The three initial stages of NPD considered in this study include the concept design, detailed design and integration & testing stages. This paper also explores a method by integration of quality function deployment (QFD) and Markov process, to understand risk patterns associated with several complete design solutions (CFDs). By using QFD, the mapping between customer requirements can be reflected into risk assessment of complete design solutions (CFDs). This methodology has been demonstrated by a case study on Coffee Maker.