نشریه تصمیم گیری و تحقیق در عملیات
سال نهم شماره 2 (تابستان 1403)

This research aims to construct a portfolio based on risk-adjusted performance and distribution-based returns and determine the efficiency using the Data Envelopment Analysis (DEA) approach. In this study, the role of return distribution in the efficiency of risky assets is also examined to form a diversified portfolio consisting of assets with varying degrees of performance.

In this study, the diversified portfolio performance of 28 firms during 1398-1402, based on the risk-adjusted value and conditional risk-adjusted value obtained from the probability distributions of returns, was compared with the minimum-variance Markowitz portfolio performance in terms of the Sharpe ratio. After estimating the maximum likelihood parameters of the model, the risk values for each stock were calculated based on the empirical return distribution, the Cauchy distribution, and the normal distribution. These risk values were then used in the data envelopment analysis to calculate the efficiency scores of each company.

The diversified portfolio with stock performance degrees outperforms the minimum-variance Markowitz portfolio in terms of risk-adjusted and conditional risk-adjusted values. The probability distribution of returns leads to different results in calculating stock risk-adjusted value/conditional value, with the empirical return distribution and normal distribution providing a more desirable performance (in terms of the Sharpe ratio) compared to the Cauchy distribution and sample ratios.

In the literature, an efficient portfolio is usually formed by calculating asset weights in the stock basket so that the Sharpe ratio reaches its maximum value. In the current study, this hypothesis is challenged in favor of the proposed method, which estimates portfolio weights based on the efficiency of risky assets.

Iranian universities have undergone many changes in recent years, which have led to more attention being paid to innovative development and changes in the main function of universities. In an era when university managers face new challenges such as fleeting changes and opportunities, uncertainty and disorder, having a strategic approach can help managers focus on distant horizons and identify opportunities and advantages as well as coherence. The activities of the university helped to clear the path of collective goals. For this reason, most universities have prepared a strategic plan to recognize and solve problems, identify strengths and weaknesses, make optimal use of opportunities and situations, and master the threats that endanger the university's existence. One of the main sources for realizing the strategic plan is financial resources, which the university often faces with a lack of funds. In addition, sudden economic shocks add to the instability and make it necessary to use the university's financial resources effectively.

A mathematical optimization model of the type of integers is presented for the management and optimal allocation of financial resources according to the performance and effectiveness of universities' scientific activities.

This model has been implemented to realize nine different activities in the strategic plan of Allameh Tabatabai University, separated by faculties. These nine activities include 1) publishing a book, 2) publishing an internal article, 3) publishing an external article, 4) holding a theory session, 5) holding a meeting to solve problems and a promotional session, 6) implementing educational plans, 7) holding internal and external conferences, 8) holding internal and external workshops and meetings, and 9) other activities.

Implementing this model and performing sensitivity analysis on its important parameters will bring acceptable and effective results for the support of faculty officials as well as managers of the university's strategic plan.

Most research in the field of designing and planning bioethanol supply chains has been based on deterministic models, which do not consider dynamic environmental conditions and thus do not provide reliable outputs. Classic robust models did not have this weakness, but due to their excessive conservatism, they increased supply chain costs, making them unattractive to investors. Therefore, the aim of this study is to design and optimize the biomass-to-bioethanol supply chain network using data-driven robust optimization methods and disjunctive uncertainty sets.

The methodology of this study is a multi-methodology approach based on mathematical modeling and machine learning algorithms. Initially, uncertainty sets for the non-deterministic model parameter were created using K-means and SVC methods. Then, a data-driven optimization model was designed to optimize the biomass-to-bioethanol supply chain network, addressing the issues of previous classic approaches.

The findings of this study are presented in two categories: strategic and operational decisions. The strategic section focuses on determining the optimal locations for biomass cultivation, preprocessing centers, and refineries. In the operational section, the optimal amounts of biomass sent to preprocessing centers and refineries were determined.

This study, by producing robust solutions without the conservatism of traditional robust optimization approaches, can significantly attract public and private sector investors. Additionally, using a three-objective model based on a sustainable development approach that simultaneously considers economic, social, and environmental components, enhances the comprehensiveness of this research, providing more realistic and detailed results.

The purpose of this research is to examine auditors' decision making in the face of ethical dilemmas in small and medium sized audit firms that are trusted by the stock exchange. This research seeks to investigate the relationship between the effective factors (such as auditor experience, audit firm size) on auditors' ethical decisions and to evaluate the role of these factors on audit quality and accuracy.

The statistical population in this study consists of the trusted auditing firms of the stock exchange, which includes 77 firms that have been studied by the full number method. The data of this research have been collected through the design of questionnaires among the auditors of small and medium audit firms trusted by the stock exchange. The collected data were analysed using SPSS20 statistical software and qualitative analysis techniques.

Four hypotheses were stated for this research. The results of the research show that audit experience and ethical decision making on audit problems (dilemmas) have a meaningful relationship. The results show that the size of the audit firm and ethical decision-making in audit problems (dilemmas) do not have a significant relationship. Also, there is no significant relationship between the ethical decision-making of audit problems (dilemmas) and the pressure received by the auditor from the client (employer) or the auditing firm.

 This research can help improve the quality of audits in small and medium enterprises and increase public confidence in financial reports. Also, the results of this research can help policy makers and regulators to establish more effective rules and regulations to support the independence and professional ethics of auditors through a better understanding of the ethical challenges faced by auditors.

Organization of the order selection process is one of the most important issues in warehouse management, and combining several customer orders in one order can increase the efficiency of warehouse operations and better use of resources and labor. It also reduces the time of the order selection process and the distance traveled.

In this research, we have presented a method to solve the problem of order batching and collectors routing. A meta-heuristic based on the genetic algorithm is proposed in this research. For a more accurate comparison, in addition to the category number of common items, we also considered the percentage of common items in each order.

The proposed method in this research has been compared with the combination of Nearest Neighbor (NN), Largest Gap, and S-shape algorithms. The test results on the random data sets have shown that the genetic algorithm provides fast and effective solutions. By evaluating the sensitivity analysis of the parameters, it was observed that the distance covered by the combined genetic method is better than the S-shape+Largest Gap+NN method.

 In this article, the genetic algorithm is used for the problem of classification of joint orders and routing of collectors in warehouses at the same time.

One of the issues faced by the managers of production and commercial organizations, which generally requires making challenging decisions with high risk and fast, is removing the product. Removal of a product for various reasons such as its life cycle status, entry of similar products and loss of market, lack of technical and economic justification for continuing its production and supply or the decision to enter newer products, product diversity, competition, activities of companies in the innovative environment. The weakness of the product is placed on the agenda of an organization. This research aims to "investigate the factors that determine the decision-making speed in the process (adoption and implementation) of the decision to remove the product in the cosmetics industry.

This was applied research using a descriptive-analytical design, and a survey method; the study was causal in nature. The questionnaire validity using validity and factorAnalysis and reliability was approved by reported of total Cronbach's alpha coefficient (0/834). The study population included the managers and administrators of Zarrin Cellulose Saveh Company in 2016. Suitable descriptive and inferential statistics, and SPSS and PLS, were used for data analysis.

 The findings suggest that structural-temporal characteristics, decision-specific factors and environmental have a significant effect on the speed of Product Elimination Decision-Making (PEDM) process. The PEDM decentralization at the tactical level, centralization of PEDM power, polychronicity, and environmental turbulence have a positive and significant effect on the speed of PEDM process. The polychronicity reinforces the positive effect of decentralization at the tactical level on the speed of PEDM process. The product significance in the company's image and history and environmental complexity has a negative and significant effect on its speed. The speed of PE decision process for products in their birth and growth stage is more than those in their maturity and decline stage. The environmental complexity weakens the positive effect of polychronicity on the speed of PEDM process. Environmental turbulence weakens the negative effect of environmental complexity on the PE decision-reaching speed, but has no effects on the PE implementation speed.

The output of this research is to determine the effective factors in the speed of decision-making in the process (adoption and implementation) of the product removal decision in the cosmetics industry.

The COVID-19 pandemic has led to a significant crisis in society's health, industries, and businesses. In this regard, the medical devices industry has played an important role in crisis management and providing healthcare and has faced several major challenges in supplying raw materials, production activities, and distribution activities due to the disruptions caused by the pandemic. One of the critically important issues in the medical devices supply chain is supplier selection. Hence, this research investigates the supplier selection problem considering the emerging concepts that have dramatically attracted the attention of researchers after the COVID-19 pandemic, namely viability and Industry 5.0.

In this study, a hybrid fuzzy decision-making approach is developed to investigate the viable supplier selection problem considering the Industry 5.0 dimensions. In this regard, in the first stage, according to the literature and experts, the main indicators of the research problem are extracted, and their weights are calculated using the fuzzy best-worst method. In the next stage, the feasible suppliers are evaluated by employing the fuzzy VIKOR method. Also, to show the robustness and validation of the proposed approach, its results are compared with those of traditional approaches.

In this study, a list of indicators, including six aspects and 34 criteria, is provided for the research problem based on its nature, and their importance has been computed. Based on the outputs, the general metric is the most important aspect, and the human-centricity metric is the least significant. Also, the results show that in addition to the general criteria, such as cost and quality, other criteria, such as reliability, technical capability, pollution control, risk reduction, and service, also play a significant role in the process of selecting suppliers. The results indicate that managers in today's competitive and industrial markets should redirect their attention from traditional criteria to those contributing to the sustainability and improvement of their systems' performance.

 Reading the results of this research can help Industrial and organizational managers evaluate the potential suppliers of their companies based on the viability and Industry 5.0 dimensions and select the best ones, which can significantly improve the performance and efficiency of their businesses.

Fuzzy multi-choice problems are widely used in the real world in the fields of industry, agriculture, science, technology, etc. Therefore, studying and solving such problems is essential. This study introduces some methods to solve fully fuzzy multi-choice linear programming problems.

To solve the fully fuzzy multi-choice linear programming problems, we use the linear least squares polynomial to convert the multi-choice linear programming problem to a mixed integer linear programming problem. Also, we convert the problem from the fuzzy mode to the crisp mode by using the defuzzification methods (Roubens ranking function.

To investigate the efficiency of the suggested method, we solve and compare two fully fuzzy multi-choice linear programming models using the proposed methods. In general, the resulting algorithms are simple and very inexpensive to implement, and they are more efficient than those of previous studies.

In this article, all parameters and coefficients of the problem are triangular fuzzy numbers, and the right side of the problem's constraints are the fuzzy multi-choice parameters. The number of fuzzy multi-choice parameters in the previous articles has been considered only two fuzzy choices. However, in this article, the number of these parameters can be arbitrary. Also, this article uses the fuzzy linear least squares method to approximate the fuzzy multi-choice parameters.

Financial statements are the main decision-making bases of capital market actors, which are affected by internal and external factors. Uncertainty in other markets is one of the most important factors affecting the financial statements of listed companies. As a result, the current research aims to model the spillover of uncertainties of parallel markets on the types of profit management.

The present research is practical. The research period is a 10-year period with seasonal data between 2011 and 2021. The VAR-MGARCH model has been used to investigate the spillover of uncertainties of parallel markets on the types of profit management.

Based on the results of the VECH, CCC, BEKK, and DCC models used to extract the uncertainty of the studied variables, the VECH models had higher accuracy. The spillover effect between different markets was observed based on the results of multivariate GARCH models. As a result, the uncertainty of one market strengthens the uncertainty between other markets. Based on the results of the vector autoregression model, the uncertainty of variables has a more substantial effect on accrual profit management than actual profit management. The variance analysis results show that oil price uncertainty has the highest contribution in the interpretation of real profit management change and exchange rate uncertainty in the interpretation of accrual profit management change.

 Uncertainties of parallel markets reinforce each other and increase the level of profit management in the investigated companies.

Economic policymakers consistently leverage monetary policies as a pivotal factor in achieving targeted economic goals, utilizing them as an influential driver for economic growth. On one hand, the advent of modern monetary tools and the widespread adoption of electronic money and payment systems can significantly influence the money supply, banking reserves, monetary base, the velocity of money circulation, and the multiplier effect. This, in turn, can shadow the impact of monetary policies on economic variables such as inflation and economic growth. On the other hand, banks play a crucial role in the development, expansion, and adoption of novel payment instruments, potentially affecting macroeconomic policies and variables. Hence, this study aims to examine the impact of electronic money and innovative payment instruments on monetary aggregates, taking into account the intermediary role of banks.

Therefore, in this study, to study the subject, panel data technique, simultaneous equations, vector regression model and data from 1370 to 1398 were used.

Based on the results, the expansion of electronic money and payment instruments lead to increased liquidity and inflation. Also, banks play a significant role in these variables by controlling the amount of payment instruments and innovations in them. On the other hand, the spread of e-money causes to achieve the targeted effects through exchange rate monetary policy and legal reserve rate. With less changes in these rates, but in order to achieve the goals of monetary policy applied through interest rates on account, more changes should be considered in these rates.

The output of this research is to provide a solution for implementing optimal monetary policy in view of the increasing use of new payment instruments and can help monetary policy makers in implementing effective policies.

This research aimed to identify the behavioral Models of stakeholder groups in corporate governance within Iran's Social Security Organization and its affiliated companies. In other words, it explored corporate governance from a behavioral perspective.

To conduct the research, a theoretical behavioral model was developed based on a review of the literature. This model identified six common behavioral indicators among stakeholders. Subsequently, a questionnaire with 36 questions was designed and distributed to experts in the field of corporate governance. A total of 207 responses were received, and the instrument's reliability was confirmed with a Cronbach's alpha of 0.933. Furthermore, exploratory factor analysis using the principal component method was employed to explain the factors of the model, which represented a classification of stakeholder groups. To validate these findings, confirmatory factor analysis was used to evaluate the proposed theoretical model.

The results yielded a CMIN value of 2.694, which is below the critical value of 3. The root mean square error of approximation (RMSEA) was 0.097, falling within the acceptable range of 0.05 to 0.1. The Akaike Information Criterion (AIC) was 1812, closer to the saturated model index (1404) compared to the independent model (5660), indicating a good fit of the model.

Overall, six stakeholder groups were identified in the corporate governance of companies affiliated with the Social Security Organization: institutional stakeholders, government, political stakeholders, social networks, economic stakeholders, and shareholders. Additionally, six common behavioral indicators were identified among stakeholder groups: conflict resolution, building trust in the board of directors, political bargaining, power-politics relationships, goal setting, and decision-making processes. To assess the impact of each behavioral indicator, a parametric t-test and mean comparisons were used. The results showed that institutional stakeholders, the government, and political stakeholders had a significant and greater impact on governance in the Social Security Organization compared to others. Moreover, government interventions in goal setting, political bargaining, building trust in the board of directors, and conflict resolution had a significant and above-average impact on the governance of the studied companies. On the other hand, political groups had a significant and above-average impact on the corporate governance of companies affiliated with the Social Security Organization in terms of decision-making processes, goal setting, and power-politics relationships. The evaluation of behavioral indicators among stakeholder groups revealed that the shared focus of these groups was primarily on three indicators: goal setting, decision-making, and power-politics relationships.

In this research, a multi-product and multi-period closed-loop supply chain design problem with three objectives of profitability, social responsibility, and reliability under uncertain conditions has been considered.

Triangular fuzzy numbers have been used for non-deterministic parameters, and a robust probabilistic programming approach with the Me scale has been used to deal with fuzzy constraints. The proposed approach eliminates the need for iterative consideration by decision-makers by providing unlimited choices from the optimism-pessimism spectrum. The mathematical model developed in this research is of mixed integer linear programming type, which is implemented in GAMS software to solve it and find Pareto optimal solutions.

The accuracy of the overall performance of the proposed model has been evaluated with four examples (based on the coefficients of the objective functions) from a case study in the aluminium industry. The sensitivity analysis of the demand parameter showed that the proposed model achieved more economic profit, less social responsibility, and less reliability with increasing demand.

The variability of the justified decision space in the Me criterion has helped to solve the supply chain network design problem more flexibly and closer to reality through the possibility of exchange between the objective function and the risk-taking level of the managers.

The cost-time tradeoff in project scheduling is a significant challenge that has garnered substantial attention. This research aims to establish a balance between time compression and delays in activity execution to optimize resource utilization and facilitate activity scheduling based on existing constraints. To achieve this, a bi-objective mathematical model is developed to support decision-makers in selecting the optimal schedule for project execution.

In this study, a bi-objective mathematical model is proposed to balance cost and time, incorporating a nonlinear cost function and the time value of money. The model is then solved using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to analyze the effects of time compression and activity delays on the outcomes.

 The proposed model's results demonstrate its ability to optimize project resource usage by considering current constraints and capacities. This enables decision-makers to adjust activity scheduling to achieve the best balance between cost and time. Furthermore, the model assumes costs are nonlinear and calculated based on the time value of money. This approach allows for scheduling decisions that account for the time value, reducing delay-related costs and enhancing overall project efficiency.

 This research presents a novel approach by introducing a bi-objective decision-making model that integrates the time value of money in project scheduling, marking a new step in optimizing the cost-time tradeoff. Unlike previous studies that focus solely on reducing either cost or time, the proposed model provides a comprehensive solution by accounting for nonlinear complexities and the time value of money. This model assists project managers in gaining better insight into cost-time impacts, optimizing resource allocation, and ultimately improving project performance by reducing delays.