This paper uses econometrics models to estimate factors affecting the offshore fishing households’ incomes in the Southern Central Coast of Vietnam. We estimate two basic models: one with total income as the dependent variable and the other with per capita income as the dependent variable. Since heteroscedasticity is in present, the study employs Ordinary Least Squares (OLS) estimations with Robust Standard Errors (OLSR). The models are further divided into a model with intercept dummies and the one with slope dummies. The estimation results for the intercept dummies indicate that fishing technology, number of days per trip, type of fishery, the residential characteristics, household size, number of dependents, captains' experience, fishing ground, consumer market, and the role of officers for fishing stimulation are the main factors having significant impacts on fishing household’s income. Surprisingly, ship capacity, income diversification, career passion, householder’s educational background, and consumer market have no significant impact on fishing household’s income. The results for the intercept and slope dummies, both, indicate that the Southern areas enjoy more benefits than the Northern areas in terms of income and utilizing the stimulation policies. The empirical results allow us to suggest some policy recommendations for central and provincial government aiming at improving offshore fishing household’s income in the Southern Central Coast of Vietnam.

We propose and develop a new G-optimality criterion using the concept of weighted optimality criteria and certain additional generalizations. The goal of the weighted G-optimality is to minimize a weighted average of the maximum scaled prediction variance in the design region over a set of reduced models. A genetic algorithm (GA) is used for generating the weighted G-optimal exact designs in an experimental region for mixtures. The performance of the proposed GA designs is evaluated and compared to the performance of the designs produced by our genetic algorithm and the PROC OPTEX exchange algorithm of SAS/QC. The evaluation demonstrates the advantages of GA designs over the designs generated using exchange algorithm, showing that the proposed GA designs have better model-robust properties and perform better than the designs generated by the PROC OPTEX exchange algorithm.

The majority of structural failures are attributable to errors in construction. This problem exists in all countries, but it is more frequent in developing communities. This study focuses on construction errors of structures in Tehran, the capital city of Iran. In this study, eighty-eight buildings have been investigated during the construction phase. These buildings have been categorized into ten types and have been distributed in twenty-two suburbs. Results showed that the buildings of Tehran can suffer from at least forty-nine major construction problems. In addition, for the first time, this research has introduced the following three terms in relation to prioritizing of construction errors: Relative Importance Factor (RIF), Priority Index (PI) and Structural Importance Index (SII). As a part of the conclusions, the results showed one hundred percent of investigated buildings are affected dramatically by the “use of untrained workers” and “lack of sampling or wrong sampling” too. In this regard, the RIF and PI of each “Lack of sampling or wrong sampling” and “use of untrained workers” are 100 and 1, respectively. Also, suburb 3 has the best construction conditions while suburb 10 has the worst.

Manufacturing, in general, creates a finished good from a set of simpler supplied parts. Supplied parts are installed into higher assemblies, higher assemblies move into even higher assemblies, and eventually this terminates at the finished good. Delays or variation during the manufacturing process ripple all the way to the finished good, possibly from different branches of the build and possibly magnifying any individual effect. There is extensive literature regarding Lean Manufacturing and it provides strategies and business philosophy to deal with variation, however it offers little in the way of quantitative analysis on the effects of that variation upon the whole. Digital Twins and discrete event simulations can and have been used to model the impact of variation in its totality. Various papers on Digital Twins have explored how to model manufacturing, but very little on generalized behavior. (i.e. How schedule slips at the subassemblies impacts the delivery dates / quantities at the finished good level). This paper explores the analytical quantitative effects of input/sales variation through the manufacturing cycle and the resultant effect on the finished good manufacturing schedule/cycle. We demonstrate that even small random variations/interruptions propagate up the build chain, get reduced in magnitude and end up producing predictable reductions in the average build rate of the final product. Additionally, it is shown that the more supplied parts that comprise a finished good the greater the expected reduction in average build rate.

Already faced with tight competition and low profit margins, the airline industry is going through major changes in the wake of the current pandemic resulting in travel restrictions and slump demands, prompting airlines to curtail services and investments in every aspect of business. To that end, developing a comprehensive method of improving airline performance measures is crucial. However, this type of problem is complex to solve due to a large number of factors, requiring a systematic approach. It entails taking into account a multitude of conflicting, or sometimes interrelated criteria, hence becoming an inherently multiple criteria decision making problem. This study is aimed to assess the competitiveness of airlines and evaluate their financial and operational performances in relation to such criteria. We test FAHP, TOPSIS, and a hybrid method of combining FAHP and TOPSIS methods. In particular, regarding the hybrid method, FAHP is employed to determine the influential weights of criteria that are utilized in TOPSIS for preference values among alternatives. We demonstrate the applicability of the proposed methods to solving a MCDM problem of airline performance assessments using real data sets. Further, this study focuses on examining the relationship between financial and operational performance criteria, as well as gleaning insights for airlines to build an evaluation system that would aid in understanding their strength and weakness in the performance metrics. The computational experiment results of our hybrid FAHP-TOPSIS model support the efficacy of incorporating fuzzy values concerning influential weight criteria. By judiciously distributing criteria weights that are specific to the airline industry, our proposed model captures preference scores reflective of industry-related and concurrent measures. This modeling framework can help airlines better evaluate the systematic influential relation structure among criteria in critical financial and operational dimensions.

This paper assesses the effectiveness in scheduling independent jobs with earliness/tardiness costs and variable setup times applying the Overall Equipment Effectiveness (OEE). The OEE is a common metric for measuring the manufacturing productivity. We defined a mixed-integer linear programming formulation of the parallel machine scheduling problem with four different objective functions in order to compare different scheduling configurations. Real data, from a plastic container manufacturing company located in the Basque Country (Spain), were used to validate this approach. A sensitivity analysis was performed with different production capacities and earliness/tardiness costs in order to evaluate the trade-offs between economic performance (i.e., costs) and the partial rates of OEE (i.e., quality, performance and availability). The objective of this study is to propose a guideline to help management make decisions regarding the measurement and improvement of scheduling effectiveness through contemplating earliness, tardiness and variable setup times.

Facing stiff competition in worldwide markets, capability of meeting timely demands of multiproduct and satisfying customer’s desired product quality are essential to present-day manufacturers. Motivated by achieving the aforementioned goals, this research intends to find most economic common cycle length for a multiproduct finite production rate (FPR)-based inventory system, wherein, imperfect production process with expedited fabrication rate and random scrap is assumed. Extra setup and unit costs are associated with the adjusted rate, and imperfect products are screened and scrapped. A mathematical model is cautiously constructed to examine and resolve the problem. A numerical illustration is employed to exhibit the applicability of the proposed method. Except finding the most economic common cycle time for the problem, core contribution of this study also is associated with the individual and combined impact(s) of important factors to the problem, and hence, enabling management of manufacturing firms to make efficient/cost-effective decision and gain competitive advantages.

The study applies a postponement strategy to a multi-item fabrication-shipment decision making in a vendor-buyer coordinated environment with multi-delivery, quality reassurance, and overtime. To cope with the recent client demand trend asking for rapid response, quality, and diversified goods, today’s manufacturers require a multi-item production-shipping scheme to satisfy customers’ needs in cost-saving, quality, and timely matter. In our model, we first produce all needed mutual components and postpone manufacturing of finished goods in the second phase. To expedite mutual parts’ fabrication time, overtime is used. Product quality is reassured through screening the defeats and reworking repairable defectives in both fabrication phases. To decide the optimal fabrication-shipment policy, we build a math model and apply the cost minimization technique to the problem. Upon deriving the optimal policy, we utilize an example to demonstrate how our model works and its capability in exposing various previously inaccessible information to the problem. These detailed results can facilitate managerial decision-making and boost the performance of such a specific multi-item postponement fabrication-shipment system in cost-saving, product quality, and timely response.

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.