Iranian Journal of Operations Research

Neutrosophic Fuzzy Regression: A Linear Programming Approach

Zahra Behdani

Regression is a statistical technique used in finance, investment, and several other domains to assess the magnitude and precision of the association between a dependent variable (often represented as Y) and a set of other factors (referred to as independent variables). This work introduces a linear programming approach for constructing regression models for Neutrosophic data. To achieve this objective, we use the least absolute deviation approach to transform the regression issue into a linear programming problem. Ultimately, the efficacy of the suggested approach in resolving such problems has been shown via the presentation of a concrete illustration.

Causal Relations of Collaboration in Supply Chain 4.0

Alireza Aliahmadi

The basic purpose of this study is to investigate and display causal relationships among collaboration components in supply chain 4.0 using a fuzzy framework. The power of collaboration increases with the effect of Industry 4.0 technologies for the improvement of supply chain performance, so supply chain 4.0 is the context of this study. To achieve the research purpose, after reviewing articles and extracting indicators, a collaboration model with trust, initiators, barriers, dimensions, and outcomes was designed. Then using the fuzzy DEMATEL method, the effect of each variable and its position were determined. To collect data targeted sampling and snowball methods were used. 20 questionnaires were distributed to supply chain and digital technologies experts. Findings show that Trust and Information and Communication Technology infrastructure are closely related and are considered the most fundamental factors of the collaboration conceptual model, and can lead to more serious and effective changes in SC 4.0 such as improved Economic and Social performance. SC 4.0 managers can facilitate the development of collaborative trust across the SC By investing in communication and technology infrastructure.

A Decision-Making Model for Supplier Selection Based on Data Envelopment Analysis

Hoda Moradi

Efficiency plays a pivotal role in impacting costs and optimizing resource utilization for
businesses. This study aims to evaluate the technical and scale efficiency of 15 suppliers within
a production unit over a three-year period (2020-2022) using data envelopment analysis
(DEA). The analysis will involve assessing efficiency under two assumptions - constant returns
to scale and variable returns to scale. Variables were selected based on indicator availability,
representation principles, and expert input, with inputs including investment, nonoperating
expense costs, and operational expenses (comprising raw material costs, wages, and
overheads), while outputs encompass net sales and return on investment. Results from the study
indicated that supplier one, scoring 0.5716 assuming constant returns to scale and 0.6790
under variable returns to scale, emerged as the least efficient supplier. Interestingly, only two
suppliers (8 and 15) demonstrated higher efficiency levels. However, the net technical efficiency
of the supply chain showed an increasing concentration, which indicates the overall reduction
of the gap between suppliers and the improvement of the net technical efficiency in the supply
chain of the production unit. This study provides valuable insights into the differences between
suppliers from a macro perspective and offers guidance for manufacturing units looking to
expand their supply chain.

A Chance-Constrained Inverse DEA Approach under Managerial and Natural Disposability

Sohrab Kordrostami

The traditional inverse data envelopment analysis (IDEA) models assess specific performance metrics in relation to changes in others, without taking into consideration the existence of random and undesirable outputs. This study presents a novel inverse DEA model with random and undesirable outputs, enabling the estimation of some random performance measures for changes of other random measures. The proposed chance-constrained inverse DEA model integrates both managerial and natural disposability constraints. By using the introduced approach, the estimation of natural disposable random inputs is presented for changes in random desirable outputs. Also, undesirable outputs are assessed for the perturbation of managerial disposable random inputs while the stochastic efficiency is maintained. The models are solved as linear problems, with a numerical example provided to illustrate their application. The findings indicate that this approach is effective for evaluating efficiency and performance metrics in scenarios involving random and undesirable outputs.

Optimizing Hub-And-Spoke Networks Under Demand Uncertainty: A Stochastic Capacitated Single Allocation P-Hub Covering Model with Lagrangian Relaxation

Yaser Khosravian

Traditional maximal p-hub covering problems focus on scenarios where network flow is constrained by resource limitations. However, many existing models rely on static parameters, overlooking the inherent randomness present in real-world logistics. This oversight can result in suboptimal network designs that are vulnerable to congestion and rising costs as demand varies. To address this issue, we propose a novel mathematical model for the capacitated single allocation maximal p-hub covering problem that takes into account stochastic variations in origin-destination flows. Although solving this model poses computational challenges, we utilize a Lagrangian relaxation algorithm to enhance efficiency. Computational experiments using the CAB dataset highlight the effectiveness of our approach in achieving optimal solutions while reducing computation time. This framework offers valuable insights for designing robust hub-and-spoke networks in the face of demand uncertainty.

Impact of Foreign Investment Risk Factors on Attracting Foreign Investment in Upstream Industries

Mohammadreza Shahriari

The main objective of this study is to identify and rank foreign investment risk factors and determine their impact on attracting foreign investment in the upstream oil industries. In terms of nature and method, it is descriptive and, in terms of relationships, it is inferential and correlational. The statistical population of the research includes managers and experts in the upstream oil industries, and the sample size was estimated to be 103 people using random sampling. The collected data was analyzed using SPSS, Expert Choice, and Smart PLS software. The results showed that according to the experts in the statistical population, economic risk is the most important factor in foreign investment. Also, in the structural equation modeling method, the correlation between foreign investment risk and attraction factors was significant, with political risk having the greatest impact on foreign investment risk, followed by economic and financial risks and 87.4% of the changes in foreign investment attraction factors could be predicted by foreign investment risk, and the overall fit of the proposed model showed a GOF value of 0.447, indicating a high fit of the research model.

Investigating management styles and determining their impact on increasing the organization's productivity

Shervin Eshaghi Nia

Background & Aim: Management style in medical care is very important in the guidance and performance of the treatment team. This is effective in improving the quality and reducing the treatment time and increasing the productivity of organizations. Using different management styles in organizations will lead to different results in employee performance and organizational productivity. The present study was conducted to determine the relationship between management styles and organizational productivity in the Children's Medical Center of Tehran University of Medical Sciences.
Methods & Materials: This study is predictive correlational in nature. It was conducted on 112 nurses and managers working in the treatment department who were selected based on the random sampling method from. The data were analyzed through conducting ANOVA, t-test, Pearson correlation, and linear regression analysis in SPSS version 16.
Results: The mean score of applying authoritarian style was 4.54, consultative style 5.54, collaborative style 5.55, relational style 5.64 and transformational style 5.59. There was a statistically significant and direct relationship between management style and organizational productivity (p<0.05), (r=0.760). The determination coefficient of linear regression modeling was used to predict the changes in organizational productivity based on the management style indicated that 87.2% of the changes in organizational productivity was explained under the influence of independent variables.
Conclusion: Due to the direct relationship between the use of management styles and organizational productivity, it is not possible to use one style in organizations and the use of different styles in different departments will improve the productivity of the organization.
Background & Aim: Management style in medical care is very important in the guidance and performance of the treatment team. This is effective in improving the quality and reducing the treatment time and increasing the productivity of organizations. Using different management styles in organizations will lead to different results in employee performance and organizational productivity. The present study was conducted to determine the relationship between management styles and organizational productivity in the Children's Medical Center of Tehran University of Medical Sciences.
Methods & Materials: This study is predictive correlational in nature. It was conducted on 112 nurses and managers working in the treatment department who were selected based on the random sampling method from. The data were analyzed through conducting ANOVA, t-test, Pearson correlation, and linear regression analysis in SPSS version 16.
Results: The mean score of applying authoritarian style was 4.54, consultative style 5.54, collaborative style 5.55, relational style 5.64 and transformational style 5.59. There was a statistically significant and direct relationship between management style and organizational productivity (p<0.05), (r=0.760). The determination coefficient of linear regression modeling was used to predict the changes in organizational productivity based on the management style indicated that 87.2% of the changes in organizational productivity was explained under the influence of independent variables.
Conclusion: Due to the direct relationship between the use of management styles and organizational productivity, it is not possible to use one style in organizations and the use of different styles in different departments will improve the productivity of the organization.

A Comprehensive decision framework for Optimizing Cooperative Performance Through a Development Strategies

mobin mokhtari

As the chain stores grow, crop cooperatives are in danger of being destroyed In this study, strategies for increasing the efficiency of crop cooperatives in one of the northern provinces of Iran are studied. A knapsack problem mathematical model was presented, and then The fuzzy analytic hierarchy process (FAHP) method calculated the desirability (weights) of the identified criteria. Field survey, structured interview,status analysis questionnaire,intra-system strengths and weaknesses, opportunities and threats (SWOT) were evaluated. After analyzing the results of the assessments in the 10 General Productivity Indicator Criteria, 3 Specific Indicator Criteria and 7 Criteria Indicates that there is a deep weakness in the co-operative system and reveals the need for a fundamental change.Therefore, according to the results obtained in this study Criterion (Member Investment -Training-5S-Productivity and Problem Solving-Customer Orientation) made the most investment. The most important point in this issue is limited financial resources .For this reason, considering the utility of the criteria with the expert opinion and the current status of each cooperative in each criterion are two crucial factors. Finally, the strategic goals of the research, using the SWOT matrix analysis, are based on the results of the interviews all cooperatives form of success packages and executive policies.

Virtual Space and Social Laziness in Organizations: A Structural Modeling Approach

Asadollah Alirezaei

This study investigates the impact of excessive virtual space usage on social laziness within the executive bodies of Sirjan. Utilizing structural modeling to analyze data collected through standardized questionnaires, the study reveals that heavy use of virtual space can lead to an increase in social laziness, which, over time, negatively affects employees' efficiency and productivity. Structural analyses further indicate that this phenomenon can gradually reduce effective participation and interaction among organization members. The paper also offers practical recommendations for managers, such as holding awareness workshops, enhancing time management, and promoting in-person interactions, all of which can help reduce the adverse effects of excessive virtual space usage. This study provides valuable insights into the challenges posed by digital engagement and its consequences for organizations, paving the way for future research in this field.