جستجوی مقالات مرتبط با کلیدواژه « simulation » در نشریات گروه « صنایع »

One of the most important areas of research to address the deterioration of metal surfaces in various settings is corrosion inhibition. The effectiveness of amoxicillin as a corrosion inhibitor on Fe (111), Al (110), and Cu (110) surfaces was investigated in this work using simulation techniques and Density Functional Theory (DFT). Using extensive computer simulations, we identified the key chemical reactions and energy processes that control the inhibitory pathway. Our results highlight the critical roles that the electrophilic site O22 and the Fukui nucleophilic site C2, S7 play in mediating the inhibitory pathway. Furthermore, the binding energies of -50.65, -38.97, and -43.24 for the Fe (111), Al (110), and Cu (110) surfaces, respectively, demonstrate the high affinity of amoxicillin for these substrates. In addition, the energies of -5.608 HOMO and -1.873 LUMO provide information on the electrical properties that support the inhibition process. When taken as a whole, these findings provide light on the molecular mechanisms by which amoxicillin inhibits corrosion, providing important information for the development of new materials and corrosion-resistant techniques. Interestingly, the Fe-amoxicillin interaction shows more potential than the Al-Cu interaction. In conclusion, the molecule amoxicillin had a mild inhibition in the order Fe(111) >Al(110)> Cu(110) also inhibiting a physisorption on both surfaces of Fe(111), Al(110), and Cu(110).

The new challenge for business managers is to model and simulate an efficient and effective perishable foods supply chain network that is resilient enough to deal with different disruptions. Therefore, this research aims to model a resilient supply chain for unnecessary perishable foods using an agent-based simulation to deal with future disruptions. To confirm the strategies and model, the statistical population and sample include 7 prominent university professors and 11 managers of various departments of companies producing perishable foods (sales department; production department; planning and warehouse department; laboratory and quality control department; and commercial department). NetLogo software has been utilized to test the agent-based model. The simulation environment in this study includes the behavior and interactions between the members of the supply chain of unnecessary perishable foods and the consumers in Shiraz City. The simulation results indicate that the use of strategies such as consumer behavior tracking, discount, awareness of product safety, robotics, the use of blockchain among the levels of distributors and retailers, and the activation of several supporting suppliers, leads to a resilience supply chain of unnecessary perishable foods under different disruptions. In addition, among the different scenarios, the 30% discount and 40% robotics have been the most effective in the resilience of the supply chain of unnecessary perishable foods under different disruptions.

The purpose of this study is to determine how the Bangladeshi garment industry's sewing defect reduction and productivity enhancement are affected by the Plan, Do, Check, Act (PDCA) and 5S (Sort, Set in order, Shine, Standardize, Sustain) techniques. One of the main reasons for production delays and increased costs is sewing defects. By reducing defects, improving  Overall Equipment Effectiveness (OEE), and methodically using integrated PDCA concepts, the study aims to streamline and expand the production flowline while increasing throughput. To continuously evaluate and improve the sewing process, the 5S method is also employed. Tools like cause-effect diagrams and Pareto charts were used to identify the defect correctly. The OEE was used to evaluate the actual efficiency. The integration of 5S and PDCA as a lean methodology was utilized to minimize the defect rate and maximize quality to improve efficiency. For this purpose, data was collected from some renowned factories in Bangladesh. This mixed-integrated methodology is used in the study to integrate quantitative defect analysis with worker satisfaction along with efficiency surveys. The findings should offer valuable insights to the RMG industry in Bangladesh, as producers seek sustainable methods to increase productivity and enhance product quality while mitigating the impact of sewing errors on their production procedures. OEE was increased by almost 3-4% through this research.

One of the most widely utilized and reasonably priced materials in the building and construction industry today is mild steel. However, one unfavorable consequence of the abovementioned materials' destruction has been corrosion. Different types of inhibitors have been used adequately to minimize of control the corrosion rate but failed. Using Materials Studio software, the adsorption of carbamodithionic  acid on an iron surface was investigated by molecular dynamic (MD) modeling and quantum chemical calculations to check its inhibition performance. Results from molecular dynamic simulations showed that the molecule CDA efficiently adsorb on the iron surface at an appropriate geometrical optimization.The results of the molecular dynamic simulation showed that the S4=C1-S3 present in the ring, the heteroatoms (Nand S) and the pi-bonds are the active sites of the molecule.The ring, the nitrogen and sulfur heteroatoms, and the double bonded carbon atoms in the molecule are the active sites of the molecules. According to the quantum chemical calculation based on the natural atomic charge, the frontier molecular orbital, and the Fukui index values. The efficiency of the hetero atoms in terms of the reactivity of the bonds shared with the carbon atom is also demonstrated by bond length evaluated. The molecule has high global softness, EHOMO and lower energy gap values of 2.454, -5.196 and 4.907eV respectively. It can be employed in minimizing corrosion of mild steel in corrosion environments due to its adsorption potentials Additionally, at the temperature under consideration, the molecule's adsorption capacity closely matches the binding energy of the molecule.

Agent-based modelling and simulation (ABMS) is one of the topics which has been extensively studied by researchers in the field of marketing and consumer behavior. However, no such analysis has been conducted on using Agent-based modelling and simulation in marketing and consumer behavior. An extensive bibliometric analysis, as well as a thorough visualization and science mapping, was carried out in this field from 1995 to 2022, in response to capturing recent ABMS development in this field. A total of 1210 documents from the WOS and Scopus databases were analyze d using bibliometrix R-Tool and VOSviewer. The results showed the 20 documents with the most citations were in the area of energy consumption (55%) and innovation diffusion behavior (20%). The USA has the most publications in this field, with the production of 188 documents. The “EXPERT SYSTEMS WITH APPLICATIONS” is a productive journal publishing in this field. Generally, the major journals that publish research on the use of ABM in marketing and consumer behavior are multidisciplinary or interdisciplinary. 6 clusters were identified based on the analysis of the most frequent keywords: Cluster 1 (multi-agent systems and consumer behavior), Cluster 2 (agent-based simulation and SCM), Cluster 3 (ABM and energy consumption), Cluster 4 (AMB and innovation diffusion), Cluster 5 (complex system and Simulation) and Cluster 6 (ABM and TAM). Prediction is one of the goals that has attracted the most attention of ABMS researchers among many goals such as optimization, description, self-organization, and adaptability, and there are many recent works in this field. These results show that many topics that were of interest in the past, such as the ontology of ABMS, are no longer of much interest to researchers, and the attention of researchers has been directed toward issues such as the diffusion of innovation, energy consumption, and pricing in recent years. This topic can determine the appropriate approach for other researchers to research in this field.

Sales and operations planning (S&OP) is considered as an important tool at the planning strategic level. Its models vary depending on industries. The Asian model is known to be very developed. Having several parameters, the Asian model proves to be an effective tool, precisely for the study of capacity. However, after several searches made in various databases, we did not find any concrete model actually used in industry and whose parameters are presented and which defines the analysis logic to better align supply and demand. In this article, we will carry out various simulations on the basis of the data of a model of sales and operations planning used in a wire harnesses factory, in order to explain the decision-making process during S&OP meetings. The parameters of the model and the various constraints that were facing the sales and operations planning team are presented and discussed as well as the financial consequences of certain decisions. As a result of this study, we can notice that S&OP is indeed a powerful tool that makes it possible to detect in advance the various constraints whose resolution concludes in an optimal alignment between customer demand and factory capacity.

Selection of the most influential factors to improve the performance of organizations has consistently been a significant task for project managers. These underlying factors aim to prevent the failure of the project and to improve the performance of employees. The success of the organization's projects is directly correlated to customer satisfaction, time, cost, and product quality at the time of project completion. In this paper, after reviewing the literature on the elements influencing the project's success, the extent to which each factor affects the project's success is accessed. A practical data evaluation method to predict the most underlying item is a machine learning algorithm, a perfect contributory method for project managers to examine the influential factors. After identifying the component with the highest effect on the project success, validating the selected items in a real-world practice paves the way for assessing that factor's effectiveness. In this study, after selecting the Agile approach as the most notable, the simulation models were utilized to measure the proportion of organizational performance improvement. Agile Management, which is considered in the actual case, signifies implementing the Scrum method and all the definitions and phases related to this method in the organization. The analyzed Agile practice (Scrum) for the case study decremented the project cost and time substantially and enhanced the service and product quality.

Today, attention to the social and environmental aspects in addition to the economic aspect has become one of the main concerns of global organizations defending the environment and human societies, and urbanization. Also, profitability is raised as a key component in the robustness of various sectors including agricultural production. In this research, we investigate the impact of some policies and environmental aspects such as land use, pruning decisions, and research and development (R&D) on the profitability of citrus production, in the long run, using the system dynamics (SDs) model. The main contribution of this study is considering several key assumptions simultaneously in an integrated dynamics model such as the solar effect, R&D policy, pesticide effect, harvesting condition, and prune effect which is neglected or less noticed in the literature. For validation, the model’s behavior is compared with collected historical observations. Statistical analysis shows that the simulated model is consistent with historical patterns. To further investigate, the Monte Carlo simulation for sensitive variables of the proposed model is implemented and finally, the model under different scenarios is examined. Various simulations have shown that changes in maximum economic yield, citrus price, and R&D policy are three important and effective agents to achieve the best performance in this sector. Also, the obtained results can help agricultural managers and the application of these interventionist policies can lead to an increase in producers’ income and citrus production.

The aim of this study was to investigate the effect three factors (inspection & grading, sourcing policies and resource policies) on the cycle-time performance of a remanufacturing system under three different quality scenarios. The objectives were to analyse (i) the main effect of factors on the remanufacturing cycle-time under the given three quality scenarios, (ii) the interaction effect between these factors on the remanufacturing cycle-time under the given three quality scenarios; and (iii) the factors and corresponding levels that lead to shortest remanufacturing cycle-time. Simulation technique was used to model and simulate the remanufacturing system. Design of experiment method was used to design a mixed two-level and three-level full factorial for running the simulation experiments. Analysis of variance (ANOVA) was used to analyse the output results from the simulation experiments. The ANOVA results show all three factors have significant effect on the remanufacturing cycle-time, regardless of the quality scenarios. The ANOVA results also suggest that sourcing policies has the most predominant effect when the quality scenario is average. Despite the different quality scenarios, the interaction between sourcing policies and resource policies have significant effects on the remanufacturing cycle-time, with predominant effect when the quality scenario is average. The implications for remanufacturing industry are there must be (i) inspection & grading policies, (ii) sourcing policies and (iii) resource policies, as these factors affect the remanuafacturing cycle-time. This work is novel because it considers three factors simultaneously and carries out the research by using simulation, design of experiment and ANOVA.

Project managers analyze the factors that affected projects' success, signifying performing a project within the scopes (Time, Quality, and cost) defined in the initial step. The implication of each factor on project success is essential since several of them have been specified in this area. Employing all of them is not feasible, and it may impose outrageous expenses on the organizations. Therefore, this article aims to identify the factors that impact project accomplishment and pinpoint the most contributing factors to facilitate the project's implementation. The main contribution of this paper is representing a framework by combining Machine Learning algorithms and simulation models to detect the effectiveness of leading organizational factors on project accomplishment, beneficially leading to extracting the accurate analysis.   A logistic regression algorithm was employed to build a predictive model. The predictive model was created based on independent variables to predict whether the software project would be successful or fail. Also, Gamification was determined as the most influential factor on the objective by the Logistic regression feature importance method. Then, Gamified and non-Gamified models were compared by the Simulation method and showed Gamification made a 36.26% improvement in the rojects cycle time and a 15% enhancement in the quality of employers' performance by decreasing the projects' bugs. For validating the simulation results, some projects were implemented in the real case study, and the model results clarified the Gamification potential in improving employee engagement leading to better work progress tracking and higher performance quality.

Calcium carbonate is one of the most essential minerals with multiple applications in diverse food, pharmaceutical, chemical, plastic, and paper industries that is generated by the press, mangle, deposition, and classification. In the simulation process mentioned in this research study, a mathematical model was provided to obtain dryer yield information. According to different methods of preparation and purification of this material, spray dryers are at the end of the process as dry goods market or required properties. For this reason, the first Kinetic of the material drying obtained through the experiment and the momentum equations of suspended particles in the air, particle path differential equations in three dimensions, Reynolds number, heat, and mass transfer coefficients and then calculate the mass and energy balance for presenting a mathematical model has combined, for discover the accuracy of the model, the results compared with practical experiments. A concurrent spray dryer used in the laboratory and a computer programming language (MATLAB) were presented to compare the results of its execution with the experimental results to assess the software’s accuracy.

In this article, an approach to optimize opportunistic maintenance policies was presented to examine the use of opportunities created in preventive maintenance activities. After the operation, maintenance, and repair, a component never gets back to the status of a new one. Hence, assuming that the replacement case is not approached, a maintenance activity is referred to as an imperfect type. In this article, assuming the existence of the imperfect maintenance type, an opportunistic approach based on age threshold values of components is proposed. The maintenance activities in this research focus on the hybrid flow shop problem. Different threshold values are also introduced in this article for failure conditions for a machine. A harmony search algorithm is used to provide optimized values for this proposed approach. The simulation approach is used to calculate the average cost of maintenance. The cost analysis indicates that the proposed approach is better than the corrective policy widely in literature; otherwise, the proposed approach with about 25 percent saving is the best performance.

In this paper, optimal control problem is applied to Human Papillomavirus (HPV) and Herpes Simplex Virus type 2 (HSV-2) coinfection model formulated by a system of ordinary differential equations. Optimal control strategy is employed to study the effect of combining different intervention strategy on the transmission dynamics of HPV-HSV-II coinfection diseases. The necessary conditions for the existence of the optimal controls are established using Pontryagin’s Maximum Principle. Optimal control systems were performed with help of Runge-Kutta forward-backward sweep numerical approximation method. Finally, numerical simulation illustrated that a combination of all controls is the most effective strategy to minimize the disease from the community. The results shows that the size of infectious population are minimized by using different control strategies.

Home health care (HHC) aims to assist patients at home and to help them to live with greater independence, avoiding hospitalization or admission to care institutions. The patients should be visited within their availability periods. Unfortunately, the uncertainties related to the traveling and caring times would sometimes violate these time windows constraints, which will qualify the service as poor or even risky. This work addresses the home health care routing and scheduling problem (HHCRSP) with multiple hard/fixed time windows as well as stochastic travel and service times. A two-stage stochastic programming model recourse (SPR model) is proposed to deal with the uncertainty. The recourse is to skip patients if their availability periods will be violated. The objective is to minimize caregivers’ traveling cost and the average number of unvisited patients. Monte Carlo simulation is embedded into the genetic algorithm (GA) to solve the SPR model. The results highlight the efficiency of the GA, show the complexity of the SPR model, and indicate the advantage of using multiple time windows.

A high degree of uncertainty is incurred during the early product design and process planning stages of a bicycle. Consequently, this research presents an optimization procedure for the design of critical components of a bicycle frame and planning of their corresponding processes using simulation and fuzzy goal programming (FGP). For this frame, the reliability, dependability, mass, and fatigue factor were the main quality responses. Initially, the critical bicycle’s frame components with their corresponding design parameters and tolerances were identified via technical knowledge. Designed experimentation based on the Taguchi’s array was conducted by simulation with twenty replicates for various combinations of design parameters and tolerances of the key frame components. Then, satisfactory regression models were formulated to relate each quality response with design parameters and tolerances and then inserted in the optimization model. The design parameters and tolerances and processes' means and tolerances were expressed in terms of fuzzy membership functions and their relevant goals and constraints were included in the optimization model. Finally, the objective functions were minimizing the negative and positive deviation from desired goals and maximizing process capability indices. Results showed that the FGP optimization procedure effectively achieved the desired targets of the bicycle’s quality responses and process capability indices. In conclusion, the proposed procedure can be used for optimal concurrent product and process design in a wide range of industrial applications.

In this paper, optimal control theory is applied to Human Papillomavirus (HPV) and Human immunodeficiency viruses (HIV) coinfection model given by using a system of ordinary differential equations. Optimal control strategy was employed to study the effect of combining various intervention strategies on the transmission dynamics of HPV-HIV coinfection diseases. The necessary conditions for the existence of the optimal controls were established using Pontryagin’s Maximum Principle. Optimal control system was performed with help of Runge-Kutta forward-backward sweep numerical approximation method. Finally, numerical simulation illustrated that a combination of prevention, screening and treatment is the most effective strategy to minimize the disease from the community.