Journal of Industrial Engineering and Management Studies
Volume:10 Issue: 2, Summer-Autumn 2023

In today's world, software tools play an important role in speeding up software development, reducing development costs and human efforts, as well as increasing reliability. In software development by tools, choosing a suitable tool will be a difficult task because many of them are available with different capabilities. On the other hand, little research has focused on the classification of these tools and their comparison. This paper aims to perform a literature review of software development tools and to propose architectures for the requirement of the Organization of Small Industries and Industrial Towns of Iran (OSIITI), in Iran. We did a survey over more than 50 software development and programming tools. The results of this survey identified ten classes, namely (a) Database Tools; (b) Integrated Development Environment; (c) Software Development Frameworks; (d) Data Science Tools; (e) Source Control Tools; (f) DevOps Tools; (g) Unified modeling Language (UML) Tools; (h) Cloud Tools for Software Development; (h) Prototyping Tools; and (j) Notifications Programs. For each class, we collected the most software tools that are currently used with their major features. After that, two architectures, based on layered and service-oriented patterns are proposed for OSIITI. The ten specified classes, along with the tools in each class, are very useful for organizations like OSIITI who want to develop software, for both small and large projects.

A multi-level sustainable supply chain is related to a system that includes all activities necessary to transfer and supply materials and services from the producer to the consumer. In this system, the focus is on providing materials and services based on a number of objectives, such as reducing costs, increasing quality, and preserving the environment. Due to the increase of uncertainty in the supply chain, organizations need to use resources for the prediction of internal uncertainties, needs, and supply, thereby minimizing vulnerability and elevating the tolerance of their supply. Understanding the uncer-tainties and the parameters causing factors causes the problem of risk management to be raised in some cases. Therefore, main contribution of current study is multi-objective planning for a sustainable, multi-level, multi-period model, consid-ering the determined conditions and boom as uncertainty scenarios, has been specifically considered. The most important goal of the research is to determine the best units of each level (suppliers, factories, ...) of chain networks according to the points and criteria determined in the model and network, design and determine the best communication routes (network) between the selected units Each level is optimal with other levels as well as determining the volume of transported goods in these routes. For this purpose, a mathematical model has been developed, which is solved through the limited epsilon method and NSGA-II meta-heuristic algorithm. Data comparing the mathematical model and NSGA-II meta-heuristic algorithm show the calculated errors of 0.022, which considering that it is less than 0.1, the calculation error is acceptable and can be compared to the results of the error methods. The sensitivity analysis on the probability of the boom scenario showed the value of the objective function can change between 7398.51 and 3245.73. Finally, the sensitivity analysis of the probability of recession scenario showed the value of the objective function can change between 3291.64 and 9364.35. The findings of this research show that using the multi-objective planning model in the sustainable supply chain, taking into account the boom and bust of the market, can create significant improvements in the performance and profitability of the supply chain.

In today's era, organizations recognize the challenges of meeting the evolving needs and preferences of customers. Simply improving products and individual performance is insufficient to satisfy customer requirements. Instead, organizations have embraced a collaborative strategy, utilized efficient supply chains and leveraged each other's expertise and resources to enhance customer satisfaction. This approach has been made possible by technological advancements. The literature review identifies two research gaps: insufficient consideration of inherent uncertainty in construction projects and inade-quate attention to the multi-objective and multimodal nature of construction project models. To address uncertainties in construction projects, this study employed the Chance-Constrained Programming approach. Uncertainty-related parame-ters were identified and integrated into an optimization model. The primary objective of this study is to minimize project implementation delays. To achieve this, we employ exact algorithms for small and medium-scale problems and utilize NSGAII for large-scale scenarios. Our research emphasizes the critical importance of efficient project timing, cost optimi-zation, and proactive delay management for achieving successful project outcomes. The study reveals critical insights into the impact of resource allocation on the first objective function. The findings show 20% increase in resources for the first activity (i) raises the objective function to 310 units, while a 30% reduction in activity i's completion time lowers it to 188 units. These findings offer valuable benchmarks for decision-making and project optimization. Managers can use these insights to enhance decision-making, optimize resource allocation, and ensure timely project completion while maintaining quality and cost control.

Supply chain risk management is a preventive approach to risk management in the supply chain to avoid possible unex-pected consequences and to manage the blood supply chain (BSC) and achieve the maximum effectiveness and efficiency of this chain, risk management of the BSC is inevitable. This research aims to propose a mathematical model to reduce the risk of the BSC in the conditions of the COVID-19 pandemic. One of the most important contributions of this research is to consider the uncertainty in the demand parameter in the conditions of the COVID-19 pandemic and to provide a ro-bust planning model to overcome it in order to properly manage and control its risks. For this purpose, in this research a scenario-based multi-objective model is proposed with the aim of reducing the risk of the BSC in the conditions of the COVID-19 pandemic. In order to test the model, the problem is investigated in different sizes and using actual data and the results are presented, and sensitivity analysis is carried out on the changes in the parameters. Baron solver in GAMS 24.9 software is used to solve the proposed mathematical model. The proposed model determines the product sent from the blood center to the hospital, the amount of product produced in the blood center, the amount of blood collected from donors, the number of collection centers, the amount of blood stock in the blood center and hospital with the aim of reduc-ing cost and risk and increasing reliability. In this research, a scenario-based non-linear integer multi-objective model is proposed considering the level of supply and with the aim of reducing the risk of the BSC by reducing the cost and in-creasing the reliability of the BSC in the conditions of the COVID-19 pandemic, which can be used for risk management of the BSC in critical conditions of blood supply, such as the COVID-19 pandemic. Finally, to measure the sensitivity of the presented model performance to the change in the parameters, the sensitivity analysis on the behavior of the model in terms of the change in the shortage cost, the number of blood collection facilities and the objective functions is presented. The sensitivity analysis on the shortage cost parameter showed that with the increase in the shortage cost, the shortage rate decreased and this leads to an increase in the total cost.

In order to survive and succeed in today's ever-changing business world, both established organizations and startups must be able to adapt and innovate. A key factor in this is the concept of open innovation, which has revolutionized how organizations acquire knowledge by facilitating collaboration and interaction between different entities. For startups, who are new players in the market, it is crucial to remain constantly vigilant and adaptable in order to thrive. The lean startup methodology has gained popularity as a means to efficiently develop products and businesses. Investment plays a crucial role in the sustainability and growth of startups, and investors assess various factors when making investment decisions. However, previous studies have often analyzed these factors statically, without considering their dynamic interactions over time. This paper aims to explore the dynamics of startup ecosystems and the factors influencing investment deci-sions. It adopts a qualitative research approach, using expert opinions and existing literature to identify and analyze causal loops that impact the willingness to invest in startups. The study constructs a dynamic model that illustrates the relationships and feedback mechanisms among different variables, including learning, synergy, economic factors, financial risk, and startup value. The model reveals that multiple variables influence the willingness to invest, and their interactions create a complex dynamic system. Through scenario analyses, the paper suggests strategies to enhance investment readi-ness and attract investors. These scenarios include increasing cooperation to foster synergy, improving startup value through innovation and efficiency, and managing economic factors and financial risks. Sensitivity analysis demonstrates how changes in variables like cooperation can impact the willingness to invest. The research underscores the importance of understanding the interplay of these factors in a dynamic ecosystem to make informed investment decisions and foster startup success.

Warranty and maintenance contracts play an important role in product life cycle.  Different failures which occur during useful life cycle of products in additional reducing reliability, make expense for consumer and service agent. This study considers warranty periods and maintenance services costs from service agent and consumer viewpoints under two-dimensional warranty policy. By regarding agent service and consumer decisions, the interactions between them are modeled during the base warranty and extended warranty periods. Maintenance policies are performed as preventive maintenance (PM) in specific interval with fixed level, and corrective maintenance (CM) is carried out as home and road repairs. Finally, for making equilibrium between profits of consumers with different usage rates and agent services, preventive maintenance number and warranty services price are investigated by the Stackelberg equilibrium. A real case study from a truck after sales services agent of Iran is presented to illustrate the application of the proposed model.

The oil and gas industry is probably the most important industry in the world. By growing demands of energy, the need for executing oil and gas projects becomes more than ever. Mega projects in this industry have certain characteristic such as being investment intensive, multi objective, owners, investors, vendors and contracts, risk and uncertainties and etc. Nowadays, knowledge-based organizations play important role in oil and gas industry. Due to the expansion and growth of project-oriented knowledge-based organizations, one of the important issues in these organizations is the optimal selection of the project portfolio. The problem is how to choose the optimal project portfolio. In this research you will find how to establish an optimal project portfolio and with respect to organization constraints. At the end, the methodology is applied as a case study in TEC company- an active project-oriented knowledge-based organization in upstream oil and gas industry in Iran.

Forecasting the stock price index volatility is considered a strategic and challenging issue in the stock markets, and it is momentous for traders and investors in the decision-making process. Hence, the presentation of an efficient model for forecasting the stock price index volatility is a crucial and hard task because stock market data and price fluctuations have high volatility and nonlinearity characteristics. To beat this challenge, this paper proposes a new hybrid model by applying artificial intelligence algorithms to forecast the stock price index. It incorporates four phases to provide a dynamic and exact model: (1) Select popular and key technical indicators as input variables (2) Apply Adaptive Neuro-Fuzzy Inference System (ANFIS) for designing a substructure to provide a high-quality and quick solution (3) Use Modified Particle Swarm Optimization (MPSO) to enhance predictive accuracy by simultaneously and adjusting the length of each interval in the discourse universe and the appropriate degree of membership (4) Employ Parallel Genetic Algorithm (PGA) to solve complex issues with computational weight optimization and adjusting the decision vectors employing genetic operators. The stock market data of “Tehran Stock Exchange (TSE)” from 01/01/2011 to 31/12/2021 are utilized to examine the functionality of the proposed model. In comparative assessments, the overall performance of the ANFIS-MPSO-PGA model based on 5 criteria achieved 81.45%, which was significantly better than other methods.

Scheduling and timetabling for university system have been a source of attention and an important challenge for the people in charge of administrations. The regulations and infrastructures are very diverse between universities, making it impossible to come up with a universal model for all. We, in this research, focused on coming up with an algorithm to help with timetabling of class courses for Islamic Azad university of Robat Karim. Our goal was to define an algorithm that could improve teacher satisfaction, and overall efficiency of the university timetabling. Instead, we managed to come up with an efficient algorithm.This research considers different factors such as teacher satisfaction, knowledge and skillset, categorizes students based on undergraduate versus post graduate degree, their research background, their scores and finally student satisfaction as well. This multi-objective mathematic model accounts for all the rules, regulations, and limitations of the university setting while following challenging confinements that guarantee the feasibility of the solution. Using metaheuristic algorithm of Whale and Genetic, while avoiding any breach of the soft limitations, we managed to come up with a system that provides the most satisfaction between the teachers and students. In our research, we compared Whale and Genetic algorithm with 4 other metaheuristic algorithms. We concluded that the results of Whale and Genetic algorithm are superior to other algorithms in regards to: Improved function goals, less run time, more Pareto front averages, more efficient solutions and results.