Journal of Optimization in Industrial Engineering
Volume:16 Issue: 35, Summer and Autumn 2023

The current research was conducted with the aim of designing a Intuitionistic fuzzy model of hesitant decision making in the evaluation of business plans under conditions of uncertainty as an approach for the development of new products while various researches have been conducted on the development of new products based on innovation. According to the previous researches, decision-making in the uncertain environment for choosing exogenous variables of the business development model based on the development of new products was not observed. Also in the evaluation of economic plans, the parameters are usually considered as certain whereas the investigation of uncertainty is considerably important. In this research, the main goal is to investigate and find factors affecting the feasibility of new product development plans and finally to obtain a method for evaluating new product development plans. For this purpose, 12 people were selected from the elite community and experts of the chemical industry using the theoretical purposeful sampling method. The results of Intuitionistic fuzzy analysis have shown that 6 exogenous variables were chosen as key variables in the selection and development of a new product in the organization. In this research, Intuitionistic fuzzy analytic hierarchy method has been used to determine the importance of exogenous variables that experts have applied in determining their importance. The significant importance of exogenous variables are the rate of certainty of investment in product development (0.239), new product acceptance share in the market (0.275), new product development strategy (0.209), attracting funds for applied research factor (0.077), passing standards and requirements (0.136), and funding for product development research (0.061). The dynamic product development model, which is based on the cause and effect relationship needs to be designed and tested in future studies to simulate the current and future decision-making performance..

During the crisis, relief supply chain management (also known as humanitarian supply chain management) has received great attention these days. The core questions facing many humanitarian organizations are: where are their strengths/weaknesses? Are they positioned to be effective in their supply chain system? What challenges do you need to overcome? What do they need to do to take advantage of the technological opportunities offered nowadays? These questions have been addressed them extensively during the past two decades. This paper tries to review and classify some of the papers carried out in key areas of the humanitarian supply chain such as location, certainty and uncertainty, relief teams and injured (patient) classification, machine learning, queue theory, the employed research methods, solution methods, and the type of objective functions. The paper begins first to define what the “humanitarian” ecosystem may include, and which actors play important roles. After, certain critical views of the humanitarian relief supply chain are examined. The critical views of the humanitarian relief supply chain would help researchers to introduce further research orientations and areas to overcome crises in the real world.

The research was to study the relationship between motivation, work environment, and work communication dimensions toward the public health center’s employees’ performance based on the scale of their competence at work. The employees’ lack of motivation caused their achievement and performance to be not optimal. The work environment is not bright enough for employees to be optimal at work. The lack of communication between fellow employees and subordinates made goals not carried out properly, and lack of competence causes non-fulfillment of employee competencies that are appropriate or not by their areas of expertise. The research surveyed 190 respondents, with the Slovin Formula sample being 129 respondents. The model used is to combine quantitative with AMOS v.24 SEM software. Research obtained the following The determination of variable Motivation over variable ability is significantly positive. In the dimension that determines the working environment variable, "competence" is positive. Erratic work communication decisions concerning varying abilities have especially favorable effects. Variable determination of capacity for inconsistent performance is very positive. Work environment variable decisions on uneven performance are incredibly positive. The variables this determine work communication at erratic performance do not significantly positive. Inconsistent determination of variable performance motivation is very positive. All variables are determined to be significant except the Communication variable. The researcher encourages that the communication between employees needs to be improved to achieve effective work performance.

Making future predictions based on past and present data is known as forecasting. In the face of uncertainty, organizations rely on this valuable tool to make informed decisions, develop better strategies, and become more proactive. This study presents a comprehensive comparison of the performance of several classical quantitative forecasting methods, namely, Moving Average, Single Exponential Smoothing, Holt’s Double Exponential Smoothing with a trend, Holt-Winter’s Triple Exponential Smoothing with a trend and seasonality, ARIMA, ARIMAX, SARIMA, SARIMAX, and Multiple Linear Regression method.This research’s aim is to identify the most effective technique for predicting weekly sales of a product, a critical aspect of supply chain management, with the emphasis being placed on the capability of each technique to capture the trend and seasonality components of the dataset. For this, an out-of-sample validation procedure was used; the evaluation of the performance of each technique’s model was conducted using three accuracy metrics: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results revealed that the SARIMAX model outperformed the other techniques, providing the most accurate forecasts for the product’s weekly sales. This paper contributes to the field of industrial engineering by offering insights into the application of these classical quantitative forecasting methods in real-world scenarios, particularly in sales forecasting. The findings of this study can assist businesses and organizations in making up-to-date decisions and developing more effective and successful strategies.

Farmers are the essential factor in the productivity chain. However, they can also be the weakest one since the distribution chain of sales transactions took a long way in Indonesia, involving farmers, collectors, wholesalers, retailers in traditional markets, and consumers. It makes consumers buy an agricultural product at a high price from farmers' uncertain stock. A distribution chain needs an optimization model involving good decision-making from organizations or governments in planning, production, warehouse, and transportation. Simulation-based optimization aims to provide adequate stock for consumers' needs and farmers' profits to minimize production costs. The Proposed Framework Integrated Three Methods is Optimization Simplex using POM QM, Optimization Goal Programming Using POM QM, Pareto Front using Phyton based and publicly available via github.This research focuses on optimization using the simplex method to fulfil the objective function with a maximum limit of more than one variable as the parameters: market demand, stock, harvest season, and price. It is also necessary to do re-optimization using goal programming to minimize the achievement deviation of the goal function and Pareto front as a model of optimizing the solution of multiple problems or Multi-Objective Optimization using linear programming. It is the development of the previous research which aims at finding the answer to the product optimization problem by cutting the distribution channel into four; farmers sell products to KUD (Cooperatives Village Unit), and KUD holds buying and selling process between farmers. Consumers and distributors sell products to consumers, and consumers purchase the product from KUD. The research shows the optimization results of the average price are up to 8913060, and the middle market hole is up to 17741000.

The task at hand involves selecting the most suitable supplier(s), determining the optimal lot size, and allocating the total order quantities among the suppliers based on various selection criteria. However, this can become more complex when taking into account quantity discount offers and transportation selection decisions in the selection and order allocation process. To address this challenge, this paper proposes an integrated approach that combines the Analytic Hierarchy Process (AHP) with a multi-objective mixed integer nonlinear program. The approach is designed for a multi-item, capacitated multi-supplier scenario, where the goal is to select suppliers, determine lot sizes, and allocate orders while taking into account unit quantity discounts and intermodal freight costs. The proposed approach aims to minimize costs and the percentage of rejected items, while maximizing the purchasing value. To solve this problem, an efficient genetic algorithm with problem-specific operators is utilized.

Indonesia is the fourth largest coffee-producing country in the world. Coffee beans produced in Indonesia are not only to meet domestic consumption but also global demand. This study focuses on investigating how optimal the performance of the coffee supply chain in Indonesia is and the factors that will influence it based on various literature findings on coffee in Indonesia. The research method used in this study is a mixed method of qualitative and quantitative methods with a comparative study from related literature in the global context and the determine the supply chain performance weights in one area of sampling. To test the performance of the supply chain, this study conducted research in the area of West Java which is one of the coffee producers in Indonesia. The supply chain performance weight is identified by using a supply chain operation reference approach which consists of various supply chain performance attributes that produce a weight of 71 for the supply chain performance for all attributes identified which makes the performance position in an “average” level. This research contributes to designing a coffee supply chain model and identifies factors that require specific attention, particularly factors that significantly impact supply chain performance.

This study presents an enhanced genetic algorithm (E-GA) to minimize earliness/tardiness costs in the job shop environment. It considers an unrelated parallel machine scheduling problem with a limit on maximum tardiness levels. This problem is motivated by the experience of one of the authors in a job shop supporting the local aircraft industry that requires strict control on delivery times. Current literature does not consider this critical restriction and unsuccessfully tries to deal with them using higher penalty costs. The proposed method uses the design of experiment (DOE) concept while optimizing the GA operators. Furthermore, it improves the initial solution using a hybrid dispatch rule through a strategic combination of construction and improvement heuristics. The model was applied to a local job shop. The results indicate that E-GA provides a schedule with lower cost and reduced computational time compared to existing dispatch rules in the literature and existing algorithms (OptQuest).

Developing a robust platform architecture can give companies a competitive edge and enhance product future generations and customer satisfaction. However, in order to develop a product platform architecture, there is a need for some kind a product variety design that concurrently manages costs and the supply chain process, and focuses on ease of use and improved availability to components. In this research, the design for variety (DFV) approach and two indices, generational variety index (GVI) and coupling index (CI) are used to measure a product architecture. Using the quality function deployment (QFD) and design structure matrix (DSM), design indices for product diversity are identified and ranked. Additionally, the design for variety approach is modeled simultaneously with the concepts of design for cost (DFC), design for availability (DFAv), and design for supply chain (DFSC) to yield a practical mathematical model for the development of the product platform architecture, which aims for product diversity, improved availability, reduced costs, and supply chain management. The case study of the current research is a phased array radar, which is optimized using the latest techniques (genetic algorithm) and MATLAB software to solve the problem. After implementing the model, considering four objectives including total cost, availability, supplier evaluation score (competency) and replaceability (variety), and seven main parameters of the model, sensitivity analysis and other comparisons and results are presented, which analyzes the relationships between objectives, the impressment and affectability of objectives and model parameters on each other. Regarding the comparison of objectives, the results generally show the inverse relationship between the total cost objective and the other objectives, and the direct relationship between the other objectives with each other. Additionally, the results of the sensitivity analysis performed indicate that the availability objective had the highest effect and replaceability (variety) and the evaluation score of suppliers (competency) and total cost also took next place.

The COVID-19 pandemic has catalyzed unprecedented disruptions across global supply chains, necessitating a thorough examination of the implications for organizational performance. This research aims to fill this knowledge gap by investigating the intricate relationships between supply chain dynamism, disruption orientation, relational capital, supply chain resilience, and market performance. Grounded in event systems theory and social network theory, we propose a comprehensive model that details the dynamics shaping organizations' responses to disruptions. This study employs PLS-SEM to analyze data collected from a diverse sample of Korean organizations. The findings underscore the critical role of supply chain dynamism, revealing its positive association with both supply chain disruption orientation and relational capital. Moreover, supply chain resilience is positively linked with both supply chain dynamism and relational capital, highlighting its central importance. This study also unveils the mediating roles of supply chain disruption orientation, relational capital, and supply chain resilience in enhancing market performance. These results not only contribute to theoretical advancements but also offer valuable insights for practitioners. As managers adapt their supply chain strategies in response to the pandemic, our research emphasizes the long-term value of cultivating resilient relationships and embracing disruption as a catalyst for organizational growth. This nuanced understanding contributes to academic knowledge and managerial decisions amid unprecedented disruptions.

In the global supply chain, warehouses play a significant role, yet the construction land for warehouse areas is decreasing. This issue requires the company to discover a method of optimizing the available warehouse area under various policies. This article contains a systematic review of class-based storage articles that becomes essential due to the absence of the latest and comprehensive similar literature. This study aims to analyze various policies for optimizing warehouse functions and provide direction for opportunities for future research in sustainable topics. A systematic review is employed in this study to search for articles from 2004-2023 originating from four journal databases, which are ScienceDirect, Emerald, tnfonline, and Researchgate; to be later organized based on the procedures of systematic literature review (SLR). The research results show various aspects, such as the purpose of conducting the research, the findings in the article, the impact of the results influencing the optimization of warehouse functions, and the gaps in previous studies, which are opportunities for future research to create more complex and comprehensive research results on similar research topics. It is expected that this study could contribute to filling in the theoretical gap by completing the existing literature. Therefore, scientific value can be added by presenting the newest comprehensive literature review.

Simulation is a tool to evaluate the performance existing and proposed under configure conditions of the simulation data. A simulation process can be useful to test theories and understand behavior of the statistical methods. This study aimed to compare SVM, WSVM and EMDWSVM model in order to identify the best model for forecasting time series data based on 10 replicates on 2040 generated data of the SARIMA (3,1,3) (3,1,1) [12] model of Brunei data set. This SARIMA model come from the lowest error between SARIMA models. The simulations were performed with three criteria namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The results of the study show a lowest error value for the EMDWSVM time series model and the performance of all measurements is small then other models. The results also proved that combination of three method EMDWSVM is the advanced forecasting techniques in all the considered situation in providing better forecasting accuracy, the application of an EMD-based combined model particularly with wavelet method reduction approach for tourist arrivals forecasting due to better prediction results and stability than those achieved from single and current hybrid models. Therefore, the modified the existing hybrid model WSVM combined with the empirical mode decomposition (EMD) to decrease the complexity of dataset to improve its prediction accuracy.

The IBS building plays a significant role in human life by providing safe, reliable, cost-effective services, which are environmental and drive economic growth. Significant decisions are taken at maintenance stage of IBS building projects which need effective tools to avoid rework and save time, cost, and increase work efficiency. Indeed, the continuous upgrading of this sector is needed to respond to technological advances, environmental change, and increased customer demands. Integrating Maintenance Management Systems (MMS) is promising since the scope of MMS usually does not extend beyond the footprint of the “defect”; it does not provide defect diagnosis data. Therefore, integrating MMS provides a complete picture of the project. However, this integration is challenging especially in IBS building projects as they are amongst the most complicated projects and numerous parties are involved in making important decisions. This paper reviews the case study regarding integrating MMS systematically, with the aim of analysing the need for this integration and its benefits. The paper highlights a lack of a clear solution for collaboration in the IBS building project lifecycle and indicates the need for research to focus on this issue as well as the possibility of applying integrated MMS as a potential solution to improve collaboration for better decision among project participants.

In this research, the design and manufacture of an e-learning learning system application at SMK N 1 Guntal was carried out using Moodle (Modular Object Oriented Dynamic Learning Environment), a software developed for internet and web-based learning activities that can create and manage courses, check student attendance and performance, managing quizzes or other assignments. This application is designed and created using the flipped classroom learning model or reverse learning where students before doing learning in class students first study the material at home according to the learning given by the teacher. By making e-learning media with this flipped classroom learning model, students can deepen the material provided by the teacher and increase creativity and train student independence. In addition, the application of this learning model certainly requires training and readiness of teachers, educational staff in designing learning materials before having face-to-face meetings in class. By learning using this e-learning application, it is hoped that conventional learning patterns can be improved or improved into learning that is in accordance with the development of the digital era as it is today.

Mutual cooperation between the power distribution companies and industrial consumers is of special importance in promoting efficient load management. Due to this, the purpose of this paper is to study the effects of load management practices on consumers using an optimal control strategy. By using this strategy, the distribution company controls consumption of industrial consumers in two ways: time transfer of load and optimization of consumption. For this study, the distribution company is assumed as the controller and two industrial consumers are considered as the consumers controlled of the problem. First, using this strategy, the load management of consumers is studied static. In static load management, industrial consumers (controlled) present the amount of power consumption needed for the next day to the distribution company (controller) from the day before and determine how the power consumption changes during the day and night. In this case, the controller offers suggestions to minimize the financial loss or increase the benefit for the consumers. Then the controller determines the pattern of power consumption for two industrial consumers during 24 hours a day and obtains the optimal power consumption for each of consumers. In the following, load management is examined dynamic. In this case, unlike static load management, electricity distribution companies determine how and the pattern of power consumption for consumers during 24 hours a day. In this way, according to the electricity market price in the next half hour and also the information provided by the consumers controlled ones from their factories, the controllers get the optimal amount of power consumption for each of the industrial consumers. The controller then encourages consumers to follow this optimal consumption reference by defining incentives. In order to investigate the role of mutual cooperation between distribution companies and industrial units, two different scenarios are considered. One is complete cooperation between the controller and the consumer, and the other is non-cooperation between them. Finally, using the simulation results, the effects of load management in improving power consumption in two scenarios will be investigated in terms of consumer profitability.

Market-sensing capability is effective in influencing SMEs performance. This theme has recently attracted the attention of marketing strategic academics. Hence, this paper aims to provide a theoretically based review of dimensions or market sensing capability. The systematic quantitative literature review method is applied to select and analyze 18 relevant papers published in the period from 2002 to 2021. In doing so, this review market sensing capability is developed to explain how size and type of industry. The dimensions of market sensing capability proposed based on the results of a review of 18 articles are: 1) understanding, analyzing, listening, scanning, encouraging, identifying, anticipating, predicting, establishing, integrating, learning, discovering, acquiring, using, predicting, and creating (i.e., customer, competitor, company). This paper suggests different ways to apply dimension of market-sensing capability in SMEs.

Tuberculosis (TB) remains a significant public health concern in Europe, necessitating effective disease management and resource allocation. Predicting short-term TB incidence rates using machine learning algorithms offers a data-driven approach to aid policymakers and healthcare professionals in making informed decisions. Machine learning (ML) algorithms are essential for prediction tasks due to their ability to establish a relationship for data sequences. In this study, three machine learning algorithms, namely, Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN), are implemented to predict the tuberculosis incidence rates and to compare the efficacy of ML algorithms for tuberculosis incidence rates prediction for 2025, among Europe. Even though all models achieved considerable results, DT obtained superior prediction rates for the future TB incidence rate with MSE, MAE, and R2 of 0.000555, 0.01506, and 0.96430 while RF 0.000882, 0.01781, and 0.94329, and ANN 0.000767, 0.02315, and 0.95066. The prediction results showed that a significant decrease in TB incidence rates is expected for 2025 form 49,752 in 2019 to 38,509 in 2025, except Finland and Malta.

This research introduces an innovative approach to enhance efficiency and effectiveness in traditional shipyard production. It aims to identify and categorize various production waste types and propose performance optimization strategies. The approach integrates the PDCA-CR method with the Waste Assessment Model (WAM), Value Stream Mapping (VSM), and Value Stream Analysis Tool (VALSAT) to effectively categorize waste in ship production processes. Subsequently, waste analysis and performance optimization techniques, such as lean principles and process reengineering, are applied to improve identified processes, enhancing overall performance, productivity, and profitability. The study provides valuable insights into traditional shipbuilding production processes by identifying various waste types, including transportation, excess inventory, unnecessary movement, waiting times, overprocessing, overproduction, and product defects. This analysis enables shipyards to pinpoint areas for improvement and implement optimization methods to boost performance and profitability. While primarily focused on the shipyard industry, the approach''s applicability to other sectors should be explored, along with potential implementation challenges. Practically, it offers shipyards a tool to reduce inefficiencies and improve performance, ensuring competitiveness in the maritime industry. Socially, enhanced production processes can lead to job creation, economic growth, and industry development while promoting environmentally sustainable practices. In summary, this research presents a tailored integrated approach combining waste identification and performance optimization strategies for traditional shipyards, offering a comprehensive framework to enhance production process performance.

The annual capacity additions from renewable sources across the globe are growing at a rapid rate. Considering the efforts made by the international community to mitigate the effects of global warming, it is anticipated that it will continue to rise. As a result, many nations across the globe are increasing their installed capacities by capitalizing on renewable resources, such as wind resources, in order to satisfy the rising demand for electrical power. In the first part of this study, a wind resource assessment is carried out to determine the wind potential at several different places in the Green Mountain Province, which is in the northeastern part of Libya. The sites with valuable annual wind speed frequency distribution and power density are then evaluated further to determine the technological features required for a wind farm to be constructed there. Wind turbines are chosen based on their power characteristic curves, and those curves must match the resultant wind distribution. To determine the system's financial and technical parameters, the data from the most prominent site are analyzed using two different processing technologies, both of which implement the Weibull Distribution Model. The findings indicate the possibility of implementation at least at two locations, with hub heights of 10 and 50 meters, respectively.

The increasing importance of communication and information in today's society has necessitated the use of cutting-edge technologies in the field. This research focuses on the adoption of mobile government services and aims to identify the needs and effective components for successfully providing government services through mobile platforms. By analyzing the causal structure underlying the adoption of mobile government, this study offers insights into the key factors that influence users' attitudes and behaviors towards these services. The research employs a descriptive-survey research method to collect data from experts in the field of information and communication technology as well as university professors. A total of 10 individuals were selected as the sample through a non-probability snowball sampling technique. The data collection process involved conducting semi-structured interviews, allowing for in-depth exploration of the participants' perspectives and insights. To analyze the collected data, the research utilizes the fuzzy cognitive mapping method and USINET software.  Based on previous studies on mobile government success, this research identifies several dimensions that are crucial for the successful provision of government services through mobile platforms. These dimensions include Mobility, Localizability, Security, Perceived Value, Ease of Use, Awareness, Trust, Privacy, Social Influence, and Usefulness. By considering these dimensions, policymakers and service providers can better address the needs and expectations of mobile government users. The findings of the research highlight the significance of users' perceptions of usefulness and ease of use in shaping their attitudes towards adopting mobile government services. Therefore, it is essential for government agencies to prioritize these aspects in the design and implementation of mobile government initiatives.

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.

Given the high costs of investment for transmission network development and the key role of transmission networks in restructured space, using FACTS devices is crucial. In this paper, the optimal Placement of FACTS in transmission networks is investigated. In the proposed method, the objective function is defined for loss reduction a. Average models available in references are used for modeling FACTS devices. The multi-objective harmony search algorithm is used for problem-solving. This algorithm has good speed in the convergence of non-linear problems. In the numerical studies section, the problem is solved in two different scenarios with the presence of different types of FACTS devices. Results of numerical studies indicate that FACTS devices can have a considerable impact on loss reduction.

An intelligent business concept involves the whole potential of the PC and the Internet to develop the company's intelligence. Jack Welch says corporate espionage to as one of the highest competitive advantages. Business intelligence is generally applied in HRM, R & D, distribution, CRM, and, most importantly, knowledge management. The main advantage of the application of these concepts is the existence of knowledge management. Knowledge management lets all employees know what is happening inside the company, and seeing the importance of knowledge management in support of a company to create value and generate competitive advantage, PT. X Indonesia is very aware of the importance of knowledge management held by each employee.

This paper proposes a modified particle swarm optimization (MPSO) algorithm for discrete sizing optimization of truss structures. The original particle swarm optimization (PSO) is a population-based metaheuristic that fluctuates the search agents about the best solution based on Eberhart functions. The efficiency of the PSO in solving standard optimization problems of well-known problems of truss structures has been demonstrated in literature. However, its performance in tackling the discrete optimization problems of truss structures is not competitive compared with the recent existing metaheuristic algorithms. In the framework of the proposed MPSO a number of worst solutions of the current population is replaced by some variants of the global best solution found so far. Moreover, an efficient mutation operator is added to the algorithm that reduces the probability of getting stuck in local optima. The efficiency of the proposed MPSO is illustrated through two benchmark optimization problems of truss structures.

Probability and simulation techniques have been applied to analyze automobile workshop queue performance, but no study has been conducted to identify factors that affect automobile workshop queue performance. It is necessary to identify the factors that influence queue performance to design automobile workshop queue system. This study uses the design of experiments method to investigate the factors that influence queue performance. The number of servers, server area, number of phases, number of workers, and arrival rate are among the numerical factors evaluated. There are two categorical factors to consider: layout type and worker experience. Their effect on queue performance, including queue cost, service time, average customer waiting time, and number of customers, is examined. Additionally, this study seeks to discover appropriate experimental designs. There are three different experimental designs used. The first design is a split plot 2_VI^(7-1) that considers arrival rate as a categorical factor. The second design is a robust design that considers arrival rate as a source of variation. The third design is a full split plot design that considers arrival rate as a numeric factor. According to this study, a full split plot design offers higher accuracy in identifying factors influencing queue performance. The queue performance is significantly affected by the number of servers, phases, workers, arrival rate, and layout. This study paves the way for future studies to determine the optimal point of queue performance.

There has been plenty of debate on the relationship between foreign direct investment (FDI) and economic growth among scholars. There are many diverse perspectives on how FDI and GDP are related. Although the relationship appears to be different, it depends on the host country's capacity for absorption. In order for host country to exploit spillovers from FDI, it needs to have a minimum amount of absorptive capacity. Therefore, the first focus of the research was on the link between FDI and GDP in Malaysia. Then, absorptive capacity which only limited to human capital, financial development and trade openness was included into the relationship between FDI and economic growth in order to determine the effectiveness of absorptive capacity towards the relationship between FDI and economic growth in Malaysia. In order to analysed the relationship between FDI and economic growth with the presence of absorptive capacity in Malaysia, Automated Regression Distributed Lag (ARDL) techniques was adopted to obtain the results. Based on the results, short run and long run analysis was tested by using ARDL and Error Correction Models (ECM). Thus, ARDL bounds testing was tested to test the presence of cointegration in the models. The results show that the outcome between FDI and GDP are inconclusive which shows that there is no direct relationship between FDI and economic growth. Apparently, by the presence of absorptive capacity, the results of FDI and GDP showing both long run and short run effect. These results proved that the relationship between FDI and economic growth in Malaysia can only be determined when there is minimum threshold of absorptive capacity.

Flexible job shop scheduling problem (FJSP) has received a lot of attention in recent years, but the important point is that this field of study can be subject to many assumptions and lots of innovations can be considered. One of these can be reverse flow, which has been overlooked in many studies, while its effect on the cost and time of construction is undeniable. Other areas such as job rotation as well as issues related to sustainability can be of particular importance in this area and have not been reviewed in previous researches. Therefore, the present study seeks to provide a model to optimize the multi-objective flexible job shop scheduling problem concerning the issues of sustainability with reverse flow and job rotation considerations. For this purpose, a multi-objective mathematical scheduling model is developed, the first goal of which is to minimize the construction time and the second goal is to minimize the issues related to sustainability. To solve the model, two methods were used: Sensitivity analysis and meta-heuristic. The whale optimization algorithm (WOA) was employed in the meta-heuristic method. The results of the implementation of WOA indicate the efficiency of the proposed algorithm, while the findings of the sensitivity analysis also point to the effect of research innovations on the objective functions of the problem.

Biomass is a renewable energy source that is easy to find in agricultural countries and can be quickly implemented by co-combusting CFPP in an effort to reduce GHG emissions. However, the integrated optimization of the blending process involving different coal ranks and biomass synergizing has yet to be achieved in order to meet the quality requirements of a number of CFPPs. This study offers an optimization approach for synergizing blending biomass in several coal-fired power plants (CFPPs). The objective is to reduce fuel costs and carbon dioxide emissions by taking into account CFPP's fuel quality requirements as well as constraints on CFPP demand, source supply capacity, and transportation alternatives. The optimization model used is mixed integer linear programming (MILP), which leverages OR-Tools in Google Colab to provide optimal solutions for the allocation of coal and biomass, whereas in the mathematical model, the amount of biomass that can be mixed into coal is limited in the range of 5% to 10%. Case studies conducted on 17 sources of coal, 1 biomass production facility, 3 alternative transportation capacities, and 4 CFPPs show that blending biomass with coal can reduce fuel costs by 2.77% and carbon dioxide emissions by 9.99% when compared to business as usual. This model offers a practical solution to reduce costs while simultaneously tackling climate change in accordance with the objectives outlined in the Paris Agreement

Three-layer plywood is a material produced from processed wood in sheet form. As a wood material in sheet form, plywood is known to have advantages over solid wood. So Three Layer plywood has the potential to be exploited in terms of its shape. Physical exploration of a material is one way to determine the potential of a material. So with physical exploration, materials can be maximized in various forms that can be produced. By using research and development methods, physical exploration studies of this material can be one way to increase knowledge about harness materials. In addition, with this method the material can have various opportunities to be applied to various types of objects. Because three-layer plywood is a material that has several stages and criteria in its manufacture, and reaches the final stage of using the material in the form of the material not being used. The stages and criteria involved in the production of three-layer plywood are beyond the control of the manufacturer to be able to continue producing three-layer plywood. Physical exploration carried out on three-layer plywood material shows that this material has other potential. Because the material is in sheet form, three-layer plywood has the potential to be a processed material that can be recycled into other materials. And the results obtained also prove that three ply plywood material has the potential to be used in forms other than sheets on various objects other than its use in sheet form.

Multi-objective optimization problems arise when more than one objective function is to be minimized over a given feasible region. Unlike the traditional mathematical programming with a single-objective function, an optimal solution in the sense of one that minimizes all the objective functions simultaneously does not necessarily exist in multi-objective optimization problems, and whence, we are troubled with conflicts among objectives in decision-making problems with multiple objectives. Applications of complex programming may be found in Mathematics, engineering, and in many other areas. In earlier works in the field of complex programming problem, all the researchers have considered only the real part of the objective function of the problem as the objective function of the problem neglecting the imaginary part of the objective function, and the constraints of the problem have considered as a cone in the complex space C^n In this paper, a complex non- linear programming problem with the two parts (real and imaginary) is considered. The efficient and proper efficient solutions in terms of optimal solutions of related appropriate scalar optimization problems are characterized. Also, the Kuhn-Tuckers' conditions for efficiency and proper efficiency are derived. This paper is divided into two independently parts: The first provides the relationships between the optimal solutions of a complex single-objective optimization problem and solutions of two related real programming problems. The second part is concerned with the theory of a multi-objective optimization in complex space