In today's financial landscape, individuals face challenges when it comes to determining the most effective investment strategies. Cryptocurrencies have emerged as a recent and enticing option for investment. This paper focuses on forecasting the price of Ethereum using two distinct methods: artificial intelligence (AI)-based methods like Genetic Algorithms (GA), and econometric models such as regression analysis and time series models. The study incorporates economic indicators such as Crude Oil Prices and the Federal Funds Effective Rate, as well as global indices like the Dow Jones Industrial Average and Standard and Poor's 500, as input variables for prediction. To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). Furthermore, regression analysis serves as an additional prediction tool. Additionally, we employ the Autoregressive Moving Average (ARMA) model to assess the relationships between variables (dependent and independent variables). To evaluate the performance of our chosen methods, we utilize daily historical data encompassing economic and global indices from the beginning of 2019 until the end of 2021. The results demonstrate the superiority of AI-based approaches over econometric methods in terms of predictability, as evidenced by lower loss functions and increased accuracy. Moreover, our findings suggest that the AI approach enhances computational speed while maintaining accuracy and minimizing errors.

Concepts for aggregate modeling and simulation are presented and applied for the setting of the parameters of a CONWIP flow control system within the steel products cell at Grand Rapids Chair, a high-mix, low-volume manufacturing environment. Aggregation was accomplished by combining the over 100 types of products into one. This resulted in a single arrival process, routing modeled as random movement through workstations, and a single operation time distribution for each production operation. Aggregation reduced the effort required for model development, data analysis, validation, and simulation. The CONWIP control was shown to be effective in managing the work-in-process. A CONWIP parameter of 50 resulted in an equivalent throughput to that achieved with no limit on work-in-process. Lead time can be reduced by approximately 20% by increasing the CONWIP parameter to 58.

This paper presents a review of clustering and mathematical programming methods and their impacts on cell forming (CF) and scheduling problems. In-depth analysis is carried out by reviewing 105 dominant research papers from 1972 to 2017 available in the literature. Advantages, limitations and drawbacks of 11 clustering methods in addition to 8 meta-heuristics are also discussed. The domains of studied methods include cell forming, material transferring, voids, exceptional elements, bottleneck machines and uncertain product demands. Since most of the studied models are NP-hard, in each section of this research, a deep research on heuristics and metaheuristics beside the exact methods are provided. Outcomes of this work could determine some existing gaps in the knowledge base and provide directives for objectives of this research as well as future research which would help in clarifying many related questions in cellular manufacturing systems (CMS).

With the intention of addressing product quality, machine reliability, and acceptable service level issues in real fabrication systems, this paper studies joint effects of stochastic breakdown, backorder of permissible shortage, rework, and scrap on the optimal stock replenishing policy. A decision model is developed to solve the problem, which consists of mathematical modeling, formulations, and optimization method in order to help analyze the problem, derive the system cost function, and find the optimal replenishment cycle length decision. Applicability of the research results are demonstrated by a numerical example. The proposed decision model enables production managers to not only determine the optimal stock replenishing policy, but also reveal individual impact and/or joint effects of stochastic breakdown, defective rate, backorder of allowable shortage, rework, and scrap on the replenishing decision. With such an in-depth study, diverse system characteristics become available for managerial decision-making.

This study presents a multiproduct fabrication cost-minimization model featuring external providers, commonality, rework, and postponement in the supply chain environment. Customers’ requirements simultaneously emphasize quality, variety, and fast response time in current markets. To satisfy customer needs, most manufacturers in various industries (e.g., clothing, household goods, automotive, etc.) plan their multiproduct fabrication by incorporating a postponement strategy, rework process, and an outsourcing option. Motivated by the viewpoints above, this study offers a decision support system to address customers’ external expectations while optimizing internal operating expenses and machine utilization. We propose a single-machine, two-stage delayed differentiation system under a rotation cycle policy. All needed common parts are made in stage one, and stage two fabricates different end products. An external provider is hired to supply partially needed common parts to shorten uptime. The defective items are inevitably produced in both stages. They are categorized and reworked to maintain the desired product quality. Finally, we derive an optimal cost-minimization rotation cycle for our model and use a numerical example to investigate the collective and individual influences of reworking, postponement, and outsourcing to external providers on the multiproduct fabrication problem. In summary, this study can offer an optimization solution for production planning in various modern industries.

South Korean manufacturers have dealt with supply chain interruptions from the COVID-19 pandemic as many other manufacturers have around the world; however, it seems that some Korean manufacturers are remarkably resilient. Many Korean manufacturers have withstood perilous supply chain dynamism and maintained operational performance without interruption. This study examines the antecedents of supply chain resilience and operational performance to suggest how other manufacturers can develop and maintain continuous uninterrupted operations even amid dynamic supply chains and global disruption. This is an empirical study of South Korean manufacturers utilizing PLS-SEM analysis with mediation effects. This study examines the interrelationships of six variables including, supply chain disruption orientation, management’s intention, digital infrastructure capability, innovation adoption, supply chain resilience, and operational performance. The implications are meaningful for both scholars and practitioners alike. This paper contributes to literature pertaining to both supply chain management and technology assimilation.

Lots of work has been conducted to explore and explain inter-organizational relations between supply chain partners. However, we have noticed that there is no accurate agreement between authors. Therefore, to better understand this disparity, the authors have studied supply chain inter-organizational relationship dynamics in different industries to bring out an analytical framework that allows a better understanding of such an issue. Further, the framework is subjected to expert’s opinion and ranked using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. These case studies instruct professionals and researchers so that they bring up their level of abstraction that remains appropriate to catch this dynamic in order to guide decision-making and future research and studies.

The application of Fused Deposition Modeling (FDM) is restricted due to limited information about the mechanical properties of printed parts. Therefore, it is required to determine the mechanical properties of the FDM properties to avail the full benefit of the FDM process. In the present study, Classic Laminate Theory (CLT) has been employed at the different configurations of layer thickness and raster width. The required elastic constant of material for CLT has been experimentally obtained through FDM printed Polylactic Acid (PLA) unidirectional specimens at 0°, 45° and 90° for different combinations of layer height and raster width. For these different combinations of layer height and raster width, constitutive models were developed to predict the tensile properties of the PLA parts. Tensile strength of the FDM printed bi-directional specimens has been experimentally obtained to validate the proposed CLT model results. The experimental tensile strength data is in good agreement with the data predicted by the proposed CLT model. Higher tensile strength and modulus were achieved with 0° raster angle compared to 90° raster angle. In the case of a bi-directional printed specimen, higher tensile strength was obtained with 45°/-45° raster angle followed by 30°/-60° and 0°/90° raster angle.

Operating in today’s turbulent and competitive world marketplaces, manufacturers must find the best production scheme and delivery policy to meet timely client’s multiproduct requirements and minimize the total manufacturing-shipment expenses. This study proposes a two-stage delayed differentiation model for a multiproduct manufacturer-retailer coordinated supply chain featuring the adjustable-rate for making common parts and a multi-shipment policy for transporting finished goods. The aim is to help present-day manufacturers achieve their operational goals mentioned above. The mathematical techniques help us build a specific model to explicitly represent the problem and derive its overall operating expense. Then, the convexity of the total expense is verified by Hessian matrix equations. The differential calculus helps derive the cost-minimized fabrication-shipment decision. This study offers an example to demonstrate the applicability and capabilities of our proposed model numerically. The following crucial information has been made available to the managers to facilitate their operating decision makings: (1) the problem’s best fabrication-shipment policy; (2) the collective influence of various common part’s completion rates and values on the problem’s total expenses and optimal fabrication-shipment policy; (3) the impact of various adjustable-rates in stage one on utilization and stage one’s uptime; (4) the details of cost contributors to the problem; and (5) the collective impacts of critical features on the problem’s performance.

This research explores the collective impact of overtime, random breakdown, discontinuous issuing rule, and scrap on batch production planning in a supply-chain environment. In today’s global business environment, manufacturing firms encounter numerous operational challenges. Externally, they must promptly satisfy the customers’ various requests, while internally, they must cautiously manage several inevitable issues in the fabrication process. These issues might be concerned with scrap, random breakdown, etc. Resolving such issues is crucial for meeting the due dates of customers’ orders, adhering to the expected manufacturing schedules, product quality, and minimizing the total fabrication-transportation-inventory costs. The study develops a model to characterize the system’s features mentioned above and assist the manufacturers with batch fabrication planning. The model proposes a solution process with an algorithm seeking an optimal runtime for the system. Additionally, it gives a numerical illustration depicting the collective and individual impacts of these special features on the operating policy and other performance indices. This model and the research findings can facilitate manufacturers’ decision-making for green batch fabrication and enhance competitive advantage.