|
|
|
 |
Search published articles |
 |
|
Showing 187 results for Type of Study: Original
Somaye Mohammadpor, Maryam Rahmaty, Fereydon Rahnamay Roodposhti, Reza Ehtesham Rasi,
Volume 14, Issue 2 (12-2023)
Abstract
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%.
Amir-Mohammad Golmohammadi, Hamidreza Abedsoltan,
Volume 14, Issue 2 (12-2023)
Abstract
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.
Dr Zahra Behdani, Dr Majid Darehmiraki,
Volume 15, Issue 1 (7-2024)
Abstract
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.
Aminmasoud Bakhshi Movahed, Alireza Aliahmadi, 3. mohammadreza Parsanejad, Hamed Nozari,
Volume 15, Issue 1 (7-2024)
Abstract
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.
Hoda Moradi, Mehdi Abbaszadeh,
Volume 15, Issue 1 (7-2024)
Abstract
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.
Yasaman Zibaei Vishghaei, Sohrab Kordrostami, Alireza Amirteimoori, Soheil Shokri,
Volume 15, Issue 1 (7-2024)
Abstract
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.
Mr Yaser Khosravian, Prof Ali Shahandeh Nookabadi, Prof Ghasem Moslehi,
Volume 15, Issue 1 (7-2024)
Abstract
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.
|
|
