The maximum tardiness reflects the worst level of service associated with customer needs; thus, the principle that seru production reduces the maximum tardiness is investigated, and a model to minimize the maximum tardiness of the seru production system is established. In order to obtain the exact solution, the non-linear seru production model with minimizing the maximum tardiness is split into a seru formation model and a linear seru scheduling model. We propose an efficient cooperative algorithm using a genetic algorithm and an innovative reinforcement learning algorithm (CAGARL) for large-scale problems. Specifically, the GA is designed for the seru formation problem. Moreover, the QL-seru algorithm (QLSA) is designed for the seru scheduling problem by combining the features of meta-heuristics and reinforcement learning. In the QLSA, we design an innovative QL-seru table and two state trimming rules to save computational time. After extensive experiments, compared with the previous algorithm, CAGARL improved by an average of 56.6%. Finally, several managerial insights on reducing maximum tardiness are proposed.

A preparatively convenient method is proposed for the synthesis of new 1-oxo-3,4-dihydro-1H-pyrrolo[2,1-с][1,4]oxazine-8- 4a-e and 4-oxo-4H-pyrrolo[2,1-c][1,4]benzoxazine-3-carboxylic acids 5a-f that is based on the interaction of methyl (2-oxomorpholin-3-ylidene)ethanoates 1a-e and methyl (2-oxo-2H-1,4-benzoxazine-3(4H)-ylidene)ethanoates 2a-f with 2-bromo-1,1-diethoxyethane. Obtained acids were transformed into the corresponding tert-butyl carbamates 6a-e, 7a-f and N-alkyl(aryl)carboxamides 11a-i, 12a-d. By treating tert-butyl carbamates 6a-e, 7a-f with hydrogen chloride, 8-amino-3,4-dihydro-1H-pyrrolo[2,1-c][1,4]oxazin-1-ones 8a-e and 3-amino-4H-pyrrolo[2,1-c][1,4]benzoxazin-4-ones 9a-f were obtained. By acylation of amines 8a-e, 9a-f with acetic anhydride, benzoyl chloride, methanesulfonyl chloride, and p-toluenesulfonyl chloride, corresponding N-acetamides 13a,b, 14, N-benzamides 15, 16, N-methanesulfonamides 17, 18, and N-p-toluenesulfonamides 19, 20 were synthesized. In total, 30 new derivatives of 1-oxo-3,4-dihydro-1H-pyrrolo[2,1-c][1,4]oxazinones and 27 new 4-oxo-4H-pyrrolo[2,1-c][1,4]benzoxazinones were obtained.

Corporate social responsibility (CSR) has gained research attention as the result of the Sustainable Development Goals (SDG), which focus on responsible consumption and production (SDG-12). However, previous studies mainly focused on Carroll’s pyramid of CSR and other facets of CSR practices need to be further explored. The objective of the study is to examine the impact of CSR practices on customer purchase intention in the clothing industry by integrating the Triple Bottom Line (TBL) model and ISO26000 best practices with the Signalling theory as the theoretical foundation. Through an online survey, a total of valid 182 responses were obtained via purposive sampling. Multiple regression was utilized for data analysis. The results show that economic CSR had the strongest impact on customer purchase intention, followed by environmental, social, and customer issues. Surprisingly, there was no significant impact between fair operating practices and customer purchase intention. The current study is unique by integrating the TBL framework with the customer-related ISO26000 CSR practices in a single framework and utilizing the Signalling Theory as the underpinning theory to explain the relationships, which is novel in an emerging market. The results provide insightful implications to the clothing company's management to emphasize the (social, economic, environmental, and customer issues) CSR attributes to guarantee business sustainability and attract consumers in this competitive marketplace.

Diabetes mellitus is a serious health disease that affects people all over the world. The number of persons identified with diabetes mellitus rises each year. α -Glucosidase is a digestive enzyme used to control diabetes mellitus. The searching for new potent α-glucosidase inhibitors capable of delaying carbohydrate digestion in the human body is an important strategy towards control of diabetes mellitus. In this work, a series of quinoline-based Schiff base derivatives already identified as α-glucosidase inhibitory activity was studied by using 2D/3D-QSAR approach. The best HQSAR/A-B-C-H-Ch-DA and CoMSIA/SEDA models were constructed using thirteen molecules in the training set, resulting in favorable values of Q2 (0.834 and 0.607), and high values of R2 (0.985 and 0.912), respectively. The generated HQSAR/A-B-C-H-Ch-DA and CoMSIA/SEDA contour plots were precious for designing and enhancing the α-glucosidase inhibitory activity of quinoline-based Schiff base molecules. Considering these results, two novel α-glucosidase compounds were designed to possess significant activity. The newly suggested molecules showed good outcomes in the preliminary in silico ADME/Tox evaluations. Molecular docking results revealed that the new designed inhibitors have a good stability in the active pocket of the studied receptor compared to voglibose, clinically used as an α-glucosidase inhibitor. MD simulation and MM-GBSA results confirmed the molecular docking outcomes. Finally, DFT analysis was useful in determining the most electrophilic and nucleophilic centers of the two designed α-glucosidase inhibitors.

Production management operating in recent competitive marketplaces must satisfy the client desired quality and shorter order lead-time and avoid internal fabricating disruption caused by inevitable defects and stochastic equipment failures. Achieving these operational tasks without undesirable quality goods, missing due dates, and fabrication interruption help the management minimize operating expenditures. Motivated by assisting manufacturing firms in the situations mentioned this study explores a manufacturing system that features quality reassurances through reworking or removal of defectives, correction of probabilistic failures, and partial overtime and outsourcing options for reducing uptime. This study finds the function of system operating expenditures through model building, mathematical formulations, optimization approaches, and algorithm proposition, shows its convexity, and derives the optimal batch time for the studied manufacturing model. Finally, this study offers numerical illustrations to confirm our work’s applicability and disclose its capability to provide various profound crucial system information that helps the management make strategic operating decisions.

In an increasingly advanced digital era, micro, small, and medium enterprises (MSMEs) face new challenges and opportunities in enhancing their sales performance. The use of innovative technologies, such as ChatGPT and Customer Relationship Management (CRM), is key to improving operational efficiency and strengthening MSME competitiveness. This study aims to analyze the impact of integrating ChatGPT and CRM on MSME sales performance with operational efficiency as a mediating variable. The research employs a quantitative approach using SEM-PLS methodology to explore the relationships between relevant variables. The study was conducted on 100 MSMEs in Subang Regency, Indonesia, using an online questionnaire as the data collection tool. The findings indicate that the integration of ChatGPT and CRM significantly affects MSME sales performance in Subang Regency, with operational efficiency as a mediating variable. First, ChatGPT has been shown to have a significant positive impact on MSME sales performance. This technology facilitates the adoption of new technologies, enhances customer interaction, and enables better service personalization, which directly impacts increased sales volume, sales growth, and revenue. Second, effective CRM implementation also demonstrates a significant positive influence on MSME sales performance. Good customer data management, customer satisfaction, and customer loyalty contribute to increased sales volume, sales growth, and revenue. Third, operational efficiency proves to play a significant mediating role in the relationship between ChatGPT and CRM integration and MSME sales performance. Improvements in operational efficiency through reduced processing times, optimized resource use, and cost reduction support increased sales volume, sales growth, and revenue.

In an increasingly advanced digital era, micro, small, and medium enterprises (MSMEs) face new challenges and opportunities in enhancing their sales performance. The use of innovative technologies, such as ChatGPT and Customer Relationship Management (CRM), is key to improving operational efficiency and strengthening MSME competitiveness. This study aims to analyze the impact of integrating ChatGPT and CRM on MSME sales performance with operational efficiency as a mediating variable. The research employs a quantitative approach using SEM-PLS methodology to explore the relationships between relevant variables. The study was conducted on 100 MSMEs in Subang Regency, Indonesia, using an online questionnaire as the data collection tool. The findings indicate that the integration of ChatGPT and CRM significantly affects MSME sales performance in Subang Regency, with operational efficiency as a mediating variable. First, ChatGPT has been shown to have a significant positive impact on MSME sales performance. This technology facilitates the adoption of new technologies, enhances customer interaction, and enables better service personalization, which directly impacts increased sales volume, sales growth, and revenue. Second, effective CRM implementation also demonstrates a significant positive influence on MSME sales performance. Good customer data management, customer satisfaction, and customer loyalty contribute to increased sales volume, sales growth, and revenue. Third, operational efficiency proves to play a significant mediating role in the relationship between ChatGPT and CRM integration and MSME sales performance. Improvements in operational efficiency through reduced processing times, optimized resource use, and cost reduction support increased sales volume, sales growth, and revenue.

This research focuses on production releasing and routing allocation problems in re-entrant mixed-flow shops. Since re-entrant mixed flow shops are complex and dynamic, many studies evaluate release plans by developing discrete event simulation models and selecting the optimal solution according to the estimation results. However, a high-accurate discrete event simulation model requires a lot of computation time. In this research, we develop an effective multi-fidelity optimization method to address product release planning problems for re-entrant mixed-flow shops. The proposed method combines the advantages of rapid evaluation of analytical models and accurate evaluation of simulation models. It conducts iterative optimization using a low-fidelity mathematical estimation model to find good solutions and searches for the optimal solution via a high-fidelity simulation estimation model. Computational results of large-scale production releasing and routing allocation problems illustrate that the proposed approach is good at addressing large-scale problems in re-entrant mixed-flow shops.

A number of 3-aryl-5,6-dihydroimidazo[2,1-b][1,3]thiazoles were synthesized by cyclocondensation of imidazolidine-2-thione with phenacyl bromides, and their antimicrobial and antioxidant activity was evaluated. Bioscreening confirmed the moderate antibacterial activity against the reference strains of bacteria Staphylococcus aureus, Escherichia coli and Proteus vulgaris and excellent antifungal activity against Candida albicans. It was found that 3-(4-chlorophenyl)-5,6-dihydroimidazo[2,1-b]thiazole 4h (MIC = 15.625 μg/ml) has twice the antifungal effect compared to the control drug Furacilin. The study of the antioxidant activity of the synthesized compounds proved their ability to inhibit 60–97% of DPPH radicals. The best antiradical effect was found for 4-(5,6-dihydroimidazo[2,1-b]thiazol-3-yl)phenol 4e (I = 97%). A probable mechanism of its action was proposed involving the formation of a radical cation by the SET process. For the most active antioxidants 4e-g, reactivity and electrostatic surface potential were evaluated using the DFT method, and molecular docking was studied on the human peroxiredoxin 5 protein model.

This study aims at exploring the effect of marketing 5.0 as a whole construct on marketing performance and the moderating role of customer resources between these two variables. Moreover, the study aims at examining the effects of marketing 5.0 dimensions, i.e., predictive marketing, contextual marketing, augmented marketing, and agile marketing on marketing performance as well as the moderating role of customer resources in the effect of each dimension on marketing performance. Collecting data by a closed-end questionnaire from a sample consisting of 186 managers and sales persons in clothing shops, the results pointed out that there is a statistically significant effect of marketing 5.0 on marketing performance and there is a statistically significant moderating effect of customer resources between marketing 5.0 and marketing performance. Furthermore, the results revealed that three dimensions of marketing 5.0, i.e., predictive marketing, contextual marketing, and augmented marketing, exerted significant effects on marketing performance. As well, customer resources significantly moderated the effects of predictive marketing and augmented marketing on marketing performance. Such results contribute to marketing performance literature through highlighting the importance of both marketing 5.0 and customer resources together in enhancing marketing performance.