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In this article, the modeling and solution of a cryptocurrency capital portfolio optimization problem has been discussed. The presented model, which is based on Markowitz's mean-variance method, aims to maximize the non-deterministic internal return and minimize the cryptocurrency investment risk. A combined PSO and SCA algorithm was used to optimize this two-objective model. The results of the investigation of 40 investment portfolios in a probable state showed that with the increase in the internal rate of return, the investment risk increases. So in the optimistic state, there is the highest internal rate of return and in the pessimistic state, there is the lowest investment risk. Investigations of the investment portfolio in the probable state also showed that more than 80% of the investment was made to optimize the objective functions in 5 cryptocurrencies BTC, ETH, USTD, ADA, and XRP. So in the secondary analysis, it was observed that in the case of investing in the top 5 cryptocurrencies, the average internal rate of return increased by 9.92%, and the average investment risk decreased by 0.1%.
 

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Enhancing the efficacy and productivity of transportation system has been on the most common issues in recent decades, noteworthy to the industrial managers and expert so that the products are delivered to the clients at right time and the least costs. Therefore, there are two important issues; one is to create hub as the as intermediaries for streaming from multiple origins to multiple destinations and also responding to the tours of every hub at the proper time. The other is a route where the vehicles should pay at time window of each destination node. On the other hand, these problems may cause cost differences between hub and interruption of their balance. Accordingly, this paper presents a model dealing with cost balancing among the vehicles as well as reducing the total cost of the system. Given the multi-objective and NP-Hard nature of the issue, a multi-objective imperialist competitive algorithm (MOICA) is suggested to provide Pareto solutions. The provided solutions are at small, average and large scales are compared with the solutions provided by Non-Dominated Sorting Genetic Algorithm (NSGA-II) algorithm. Then, its performance is determined using the index for evaluating the algorithm performance efficacy to solve the problem at large dimensions.
 

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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.