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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.
 

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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.
 

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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. 

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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.
 

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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.
 

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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.

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In this work, we consider a ‎multi-objective‎ ‎minimal ‎cost ‎flow (MMCF) ‎problem where there are several commodities to transport from‎ sources ‎to ‎destinations and there is more than one conveyance for those transporting. We also assume that in each conveyance, there are distinct capacities for each commodity. The obtained model is not necessary balanced, and we introduced a method to solve this model without converting it to a balanced model. Some advantages of the proposed method is discussed.
 

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One of the ways to evaluate the performance of decision-making units (DMUs) such as banks and commercial companies is to use the concepts of economic efficiency in data envelopment analysis (DEA). In the process of evaluating the performance of the DMUs, it is important to apply the superior information of the decision maker (DM). In this paper, we obtain cost and revenue efficiency measurement models to evaluate DMUs based on the DM's opinion. In this regard, we use the method of production trade-offs in DEA. Using the production trade-off method, we apply the importance of inputs and outputs to the efficiency measurement process based on the opinion of the DM. We assumed that the cost (price) of each input (output) is different for different DMUs. We present the efficiency scores ​​and efficient targets corresponding to the DMUs. We present an application of the presented models in the banking sector and present the results of the paper.
 

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Cai et al.(2013) and  Cai and Han (2014) presented the polynomial time algorithms for two-pair and three-pair networks with common bottleneck links, respectively. Also, Chen and HaiBin(2012) proposed a non-polynomial time algorithms for $n$-pair networks with common bottleneck links, where $n$ is an arbitrary integer. This paper presents a new sufficient and necessary condition to determine the solvability of single rate $n$-pair networks with common bottleneck links, which concludes a polynomial time algorithm for $n$-pair networks with common bottleneck links, where $n$ is an arbitrary integer. Our algorithm runs in $O(|V||E|^{2})$ time, where $|V|$ and $|E|$ are the number of nodes and links, respectively.

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During the COVID-19 pandemic and the resulting constraints, businesses have encountered changes in their customers’ behaviors and business environments. Scholars have emphasized on the role of digital transformation as a response to these challenges. This study investigates the level of digital maturity in seven dimensions of digital transformation presented by Kane (2017) in small service businesses (SSBs). A mixed method combining the logic of statistical accuracy of questionnaire analysis with the realistic aspects of discourse analysis was applied. The results revealed that digital transformation does not fully occur among the SSBs. Instead, some extent of digitalization happened in various areas such as communications with customers and the digitalization of some aspects of services. Besides, the study revealed that the most frequent pathways taken by SSBs toward digitalization are the less capital-intensive and technology-based ones  . Furthermore, customers are involved in three types of relational activities categorized as “transactional”, “intercommunication”, and “information sharing”.

 

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The expansion of gas transmission lines in Iran involves numerous risks, requiring regular assessments to ensure safe and efficient transport. This study examines six kilometers of Iran’s oldest gas pipeline, located in Tonekabon, a densely populated and touristic city. The pipeline was divided into six zones, considering pipeline class, population density, and intersections. In each zone, three events—leakage, rupture, and explosion—were assessed using four methods: simple matrix, weighted matrix, fuzzy weighted matrix, and a 3D uncertainty-based matrix. Four experts evaluated the probability and severity of consequences, categorized as technical, safety, environmental, and cost impacts. The consequences enabled risk calculation across all categories. Standard deviation was used to compute a three-dimensional uncertainty-based risk, incorporating uncertainty in both probability and consequence estimation. Risk management levels were then adjusted accordingly. Chang’s fuzzy AHP method and Mamdani’s fuzzy logic in MATLAB were applied to handle inherent uncertainties. Results showed discrepancies between simple and fuzzy matrices due to the exclusion of cost impacts, given the state-owned nature of the company. The 3D matrix further indicated that most risk cells require only preliminary review, attributed to the company’s regular inspections and access to reliable data.
 

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Supplier selection is one of the main discussions in the Supply Chain. The issue of assigning purchase orders to suppliers that act differently in terms of quality, cast, services, etc. criteria is one of the significant concerns of purchase managers in the supply chain. To adopt an optimal decision in this regard is related to a multi-objective problem that the objectives are contradicting each other and have different importance and priority depending on the location. In practice, the existence of kind of ambiguity in explaining the information related to the problem constraints and complicated. In this regard, the emergence of Fuzzy set theory as a tool to describe such conditions besides presenting question model realistically can help to solve such problems well. Despite the importance of the model with the mentioned structure, unfortunately, few original works have been done in this field. As a result, in this paper, in addition to presenting a new multi-objective Fuzzy model being modelled based on assigning purchase order to suppliers in a supply chain a solution method is introduced based on using Fuzzy linear programming. To clarify solution process modelling and description, a case study is included related to selecting flour supplier for providing industrial bread of Khoshkar factory. The proposed model includes four objective functions:

1. Aggregate costs of minimizing type,
2. Services of maximizing type (such as packing, being faithful to promise, factory heath, discount, correct transportation, good relationships, honestly, etc.),
3. Flour useful survival of maximizing type (regarding monthly flour buying by the factory),
4. The purchased flour quality of maximizing type (concerning product type).

 Especially in the solution process, a method is determined based on setting weight for each of the objectives concerning the major factory stockholders.
 

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The backup 2-median location problem on a tree T is to deploy two servers at the vertices such that the expected sum of distances from all vertices to the set of functioning servers is minimum. In this paper, we investigate the backup 2-median location problem on tree networks with trapezoidal interval type-2 fuzzy weights. We first, present  a new  method for comparing generalized trapezoidal fuzzy numbers and then develop it for trapezoidal interval type-2 fuzzy numbers. Then numerical examples are given to compare the proposed methods with other existing  methods. Finally, we apply our ranking method to  solve the the backup 2-median location problem on a tree network with trapezoidal interval type-2 fuzzy weights.

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In this paper, a modified hybrid three-term conjugate gradient (CG) method is proposed for solving unconstrained optimization problems. The search direction is a three-term hybrid form of the Hestenes-Stiefel (HS) and Liu–Storey (LS) CG parameters. It is established that the method ensures the sufficient descent property independent of line search techniques. The convergence analysis of the proposed method is carried out under standard assumptions for general functions. Numerical experiments on CUTEr problems and image denoising tasks demonstrate that our method outperforms existing approaches in terms of efficiency, accuracy, and robustness, particularly under high levels of salt-and-pepper noise.

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A sum-network is a directed acyclic network with multiple sources and multiple sinks where each sink  demands the sum of the independent information generated at the sources. The coding capacity of sum networks with independent sources has been investigated in Tripathy and Ramamoorthy(2015) and it was proven that the upper bound of the coding capacity of such networks is 1. In this paper,  it is shown that the upper bound of the coding capacity of a sum network with dependent sources is greater than 1 which is different from the obtained results in Tripathy and Ramamoorthy(2015).
 It is also shown that a non-solvable sum-network with independent sources can be converted to a solvable one when the sources have arbitrary dependencies

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This study explores the implementation of virtual platforms in supply chain management, emphasizing online production, procurement, and distribution without traditional factory infrastructures. Using a qualitative descriptive-survey approach with inductive reasoning, the research aims to enhance supply chain performance through advanced digital technologies. Rapid advancements in Information and Communication Technologies such as Internet of Things and Artificial Intelligence challenge conventional models by enabling real-time data exchange, improving forecasting accuracy, and reducing delays. Digital integration facilitates seamless communication among suppliers, manufacturers, distributors, and customers, enhancing coordination and cost efficiency. Semi-structured interviews with industry experts were analyzed through thematic analysis, yielding 139 initial codes refined into 25 categories and 5 key themes. These highlight critical dimensions: Digital Integration, Stakeholders Coordination, Edge Computing, Data Analytics and Agility Management. Advanced analytics, leveraging mathematical models and Intelligence algorithms, provide actionable insights for demand forecasting and inventory optimization, strengthening decision-making. The findings underscore the importance of flexibility and agility in addressing market disruptions, with edge computing and real-time data processing identified as vital for operational resilience. Practical recommendations include deploying simulation tools, developing logistics optimization algorithms, and implementing robust cybersecurity protocols. Overall, virtual platforms offer a transformative approach to supply chain management, improving efficiency, reducing costs, and enhancing competitiveness in dynamic markets.

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Prostate cancer is the most common cancer in men and the second leading cause of cancer-related death worldwide. Over the years, researchers from various fields, beyond medicine, have sought to expand their understanding of the disease to develop more effective treatments. Treatment planning for high-dose-rate (HDR) brachytherapy involves designing the trajectory of the radiation source to deliver sufficient doses to the target area while minimizing exposure to surrounding organs at risk (OAR) within clinically safe limits. Since the exact tumor volume is not known, the model uses gray numbers instead of tumor volume, which provides more accurate results.
In this study, four powerful multi-objective evolutionary algorithms (MOEAs) NSGA¹-II, PESA²-II, SPEA³-II, and MOPSO⁴ are employed. Instead of yielding a single best solution, these algorithms produce a set of Pareto-optimal solutions, each representing a trade-off where no one solution is definitively better than the rest. However, they demonstrate improved performance compared to other optimization methods. The results show that the MOPSO algorithm performs better than the other three powerful algorithms in terms of solution quality and maintaining diversity among solutions.
 

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Emotion recognition in Persian texts using data mining is a significant area within text analysis. Emotions are typically defined as individuals’ emotional reactions to situations, events, and information. Emotion recognition in text involves identifying and analyzing emotional content across various types of textual data. This paper presents a model for detecting different emotions in Persian texts using an enhanced transfer model. The proposed model comprises an encoder and a decoder, each equipped with a self-attention mechanism and RNN modules. Initially, a dataset of sentences annotated with emotional states—anger, happiness, sadness, and fear—is created by multiple users. These sentences are then converted into image representations and fed into the improved transfer model for emotion recognition. Experimental results demonstrate that the model effectively identifies the emotions of sadness, anger, happiness, and surprise with precision, accuracy, recall, and F1-score values of 90.25%, 91.4%, 91.6%, and 90.80%, respectively.
 

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Facility location and routing problems have attracted significant research attention since the 1960s due to their practical relevance and complexity. Efficiently establishing production facilities, optimizing vehicle routes, and implementing effective inventory systems are essential for improving organizational performance. In this study, we propose an integrated location-routing model for the pharmaceutical supply chain, designed to satisfy all retailer demands through an appropriate inventory policy, ensuring no demand is unmet. The proposed mixed-integer mathematical model considers a four-tier supply chain, including manufacturers, distributors, wholesalers, and retailers, with the objective of establishing cost-effective warehouses while fulfilling all demand requirements. Demand uncertainty is addressed using a scenario-based probabilistic approach. The model is solved using GAMS for a small-scale case study. For larger-scale instances, where exact solutions are computationally challenging, a meta-heuristic approach—specifically, a genetic algorithm—is employed to efficiently obtain near-optimal solutions.