International Journal of Applied Operational Research

Design of an analytical model based on fuzzy data envelopment analysis approach for evaluating the sustainability capability of supply chain systems against various risks

M. Rostamy Malkhalifeh

In today’s competitive business environment, the supply chain plays a crucial role in maintaining an organization’s competitive advantage. However, environmental uncertainties, unpredictable delays, and various risks pose significant challenges to the sustainability of these systems. This study aims to present an analytical model based on Fuzzy Data Envelopment Analysis (FDEA) to assess the supply chain system's sustainability capabilities against different types of risks. The proposed model seeks to enhance supply chain flexibility and resilience under dynamic environmental conditions by utilizing fuzzy data. To achieve this goal, supply chain risks were initially identified and categorized into three levels: strategic, tactical, and operational. Subsequently, FDEA was employed to evaluate the impact of these risks on supply chain performance. The research findings indicate that increasing environmental uncertainties, over reliance on specific suppliers, reduced inventory levels, and inefficiencies in demand forecasting are key factors contributing to decreased supply chain sustainability. The results further suggest that adopting multidimensional risk management approaches and leveraging strategic management theories such as the resource based view (RBV) and dynamic capabilities can effectively mitigate risks and enhance supply chain flexibility. Additionally, a comparative analysis of the proposed model with traditional risk management approaches demonstrated that applying FDEA improves risk assessment accuracy and enhances decision making efficiency within organizations. Ultimately, this study underscores the importance of utilizing advanced decision making tools in supply chain management and recommends that organizations continuously evaluate their current status and adopt advanced analytical methods for managing potential risks. The proposed model not only provides a systematic, data driven approach for assessing supply chain sustainability but also serves as a practical tool for managers in developing risk mitigation strategies and optimizing supply chain processes.

Prioritization of stakeholders in hospitals of Guilan University of medical sciences using a combined analysis network process and DEMATEL

S. B. Salimi

The presence of different stakeholders with different interests and expectations in project implementation is one of the issues that presents project managers with serious challenges in managing stakeholders properly. The present study aims to provide useful information for stakeholder management to the heads of hospitals at Guilan University of Medical Sciences by prioritizing stakeholders.
The statistical population of the study includes; heads of hospitals at Guilan University of medical sciences and academic experts, 19 of whom participated in the research implementation process. To screen the components extracted from the theoretical foundations of the research, the fuzzy Delphi method was used, and to structure and analyze the status of the components and prioritize the stakeholders, the analytical network and DEMATEL processes were used. In addition, the snowball sampling method and questionnaire tool were used to collect data.
Using the Delphi method, 10 key factors were identified from 30 components related to stakeholder engagement, including participation, communication, motivation, and alignment of goals. Scoring 30 stakeholders based on these factors revealed that 12 stakeholders, such as employees, patients, and management, received the highest scores. The most important relationships identified through the DEMATEL technique were then analyzed in Super Decision software to prioritize the stakeholders.
According to the network analysis process, key stakeholders identified include management, contracted physicians, the University of Medical Sciences, employees, and the Ministry of Health and Care. This highlights the importance of engaging these stakeholders to achieve the hospital's goals.

A hybrid two-phase approach for the solid transportation problem under certainty conditions based on goal programming and multi-parametric models

G. Shakouri

The solid transportation model is one of the most useful models in linear programming literature. Hence, this study focuses on a development of solid transportation programming with fuzzy cost coefficients and fuzzy-flexible supply and demand constraints and transportation capacity, which aims to minimize costs. Considering the available resources (capacity of supply centers), the capacity of the vehicle is generally considered as the minimum capacity, and the demand is generally considered as the maximum capacity. To adapt to real conditions, a flexible fuzzy hybrid model is studied for the solid transportation model with supply and demand constraints and flexible fuzzy vehicles. Generally, for such models, supply and demand restrictions and vehicles must first be converted into a real form, and then the associated problem is solved in a deterministic way by using the existing real problem techniques. Furthermore, a combined Goal programming and parametric approach is proposed to obtain the best satisfactory solution. Finally, an example is examined to analyze this approach.

Efficiency evaluation and ranking of Iranian accounting Journals: A –DEA based approach

M. Akbaryan Fard

Data Envelopment Analysis (DEA) as a data-oriented non parametric method is commonly used for evaluating the efficiency of a group of decision-making units (DMUs) with multiple inputs and outputs. This paper aims to analyze the possibility of measuring the performance of Accounting Journals by DEA models and propose a ranking for a set of Journals. This study constructed traditional radial DEA models where the outputs are number of included articles, number of Volume and number of issues. Three indicators are also introduced as inputs, namely, Published Articles, time to accept and acceptance rate. The data set for study is included internal Iranian Accounting Journals in the year of 2024. The results are illustrated in the set of 31 journals from the internal Iranian web sites. Employing the input/output oriented standard DEA models can be a useful method to amend the best operating Journal in the selected sample. To sum up, running super-efficiency models prevail the top-ranked journal among the selected sample.

A novel mixed-integer linear programming model for identifying the most efficient DMU in data envelopment analysis

A. Ghomashi

Finding the most efficient decision-making unit (DMU) in Data Envelopment Analysis (DEA) can provide deeper insights into the performance of efficient units. Over the years, several methods have been proposed to improve the ability of DEA models to distinguish between DMUs, often by aiming for stronger ranking capabilities. In this study, we present an enhanced model based on mixed-integer linear programming (MILP) to identify the most efficient DMU. The model is designed so that only one DMU can achieve an efficiency score equal to one, while all others receive scores strictly less than one. This structure enhances the model’s ability to fully rank all units, while using fewer constraints compared to traditional full-ranking models. To demonstrate its effectiveness and compare it with two well-known models, the proposed model is applied to two real-world examples from the literature. These findings show that proposed model clearly outperforms of the reviewed models—not just in theory, but in practice too.

Designing a dynamic model for managing the resources and expenditures of the social security organization using grounded theory and multi-criteria decision-making (MCDM)

S. Balaei Pakdehi

This study aims to develop a dynamic model for managing the resources and expenditures of Iran’s Social Security Organization by integrating grounded theory and multi-criteria decision-making (MCDM) approaches. The research identifies and ranks causal, contextual, and intervening variables affecting the organization’s financial balance. Methodologically, the study is applied in purpose and descriptive-survey in nature. The statistical population is divided into two groups: the qualitative phase includes interviews with experts and policymakers, while the quantitative phase involves senior, middle, and expert-level managers of the organization. A purposive and snowball sampling method was used to select 15 experts. The qualitative findings led to the identification and categorization of key variables, while the quantitative phase applied the fuzzy COCOSO method to rank the factors and outcomes. Results indicate that contextual factors are the most influential in shaping the organization’s financial dynamics, followed by intervening conditions and causal factors. Additionally, a system dynamics approach was employed to simulate and analyze three policy scenarios: Gradual increase in insurance premiums for government-supported workshops by 1% annually from 2023to 2033, projected to yield a positive return of 245,000 billion rials per year. Reduction of healthcare costs through direct treatment models, ISO-standard equipment procurement, and recruitment of skilled personnel, expected to generate 32 trillion rials in savings by 2023. And Workforce retention support through shared unemployment benefit payments (50% by the fund, 50% by employers), with partial government reimbursement, projected to improve the unemployment fund’s balance by 13 trillion rials by 2033.