نشریه تصمیم گیری و تحقیق در عملیات
سال هشتم شماره 3 (پاییز 1402)

In many real-world optimization problems, we are facing uncertainties in parameters describing the problem. In general, as a simplifying assumption, uncertainty is ignored. In the school bus routing problem, there are uncertain parameters that are assumed to have deterministic values. As a result of this simplifying assumption, the obtained solutions may be mismatched with the real world. This issue arose by violating some hard constraints.

In this research, a mixed linear integer programming for school bus routing with mixed loading by using a heterogeneous fleet is presented. The uncertainty of travel times is modeled as interval numbers. We propose a heuristic algorithm to generate extreme scenarios. Each scenario is generated in order to make the last found optimal solution into an infeasible one as much as possible.

Experimental results show that deploying this novel algorithm for generating extreme scenarios, efficiently produces diverse scenarios. After the scenario generation algorithm is converged, the intersection of the feasible optimal solutions under diverse scenarios is extracted as robust sub-tours or robust trips.

Originality/Value: 

It is the first time to apply the notions of robust optimization using the extreme scenarios generation scheme. At each iteration of the extreme scenario’s generation, the most conflicting scenario against a given optimum solution is generated. The main advantage of this method over other present robust optimization methods is its emphasis on maintaining the feasibility of the optimal solution when dealing with the most diverse set of uncertainty scenarios while keeping the computational effort needed as low as desired.

The main challenge in devastating events such as the Kermanshah earthquake is the optimal location of humanitarian distribution centers, which plays an effective role in allocating relief shipments to demand centers. Therefore, balancing the complexity of the issue and the uncertainty with the constraints on aid scheduling and resource management is critical. In this regard, the location-allocation model has been developed by considering the reliability of the distribution hub set, which provides the possibility of dealing with impending disruptions after the crisis. The proposed model divides the affected area into several layers and simultaneously considers the capacity of the relief fleet. Also, a combined approach of fuzzy programming with chance constraints and robust programming has been developed to deal with parametric uncertainty.

With the thorough assessment of the disaster areas of Iran, a comprehensive model of the relief network was designed including strategic and temporary distribution hubs along with a wide range of factors and effective parameters. Subsequently, mathematical modeling was distributed by considering the reliability of the earthquake crisis distribution hub and relief according to the topography of the study area. Next, the Epsilon constraint method was applied to cover the multi-objective optimization problem and to determine non-dominant Pareto optimal solutions, and the mathematical combination of possibilistic-robust programming was used to deal with uncertainty.

The results show that the management of relief distribution and the development of strategic and operational levels of distribution based on the geographical classification of the affected area in critical conditions are effective in reducing network costs. The reliability policy used in the distribution hub set has improved the confidence capability of the humanitarian distribution network. Finally, the output results of the case study show the application and effectiveness of the extended relief network model.

Originality/Value: 

The present study, as a decision support system, facilitates relief in the regions of the country in the event of a crisis. Predicting a reliable distribution hub set with a combined transportation approach appropriate to the topography of the region ensures the optimal implementation of relief operations. Also, the developed model is operational in the areas at risk of the country.

The problem of allocating space to academic needs is one of the complex optimization issues that distributes a limited set of educational and research needs to a set of resources with a set of constraints. Due to the complexity of this problem, several techniques based on innovative methods have been proposed. In this paper, a mathematical model of integer programming is presented to formulate this problem.

To solve the model, the gradient descent method is used and its parameters are adjusted. To evaluate the proposed model and solution, the data and facilities of one of the fledgling faculties at Allameh Tabatabai University in Tehran are tested. There are 11 requirements and 18 allocable spaces in this faculty and therefore there are 198 binary decision variables, in the model. In experiments, several scenarios are created and the results of each scenario are compared.

The proposed model and solution is a general method and can be used for other faculties and universities that face space constraints.

Originality/Value:

 In this article, a mathematical model was presented to formulate the problem of allocating space, which is one of the important decision-making issues for organizations and research educational institutions.

Choosing stocks has always been one of the investors' concerns. The primary purpose of this study is to identify the factors affecting the decision-making and ranking of stocks in the stock exchange's three metal, chemical and pharmaceutical industries according to the importance of these industries.

The statistical sample of this research includes the shares of 84 companies in these three industries, which have been examined based on the data of 2021. First, stock rating factors were extracted by reviewing the research background. Experts used these factors in this field, and the final factors were selected after screening. Weighting and prioritization of these factors were done using the fuzzy SWARA method. According to the weight of the factors obtained from the fuzzy ride method and companies' financial information, the COCOSO method was used to rank the target stocks.

The results showed that price-income ratio, operating profit margin, and percentage of return on capital are the essential criteria for experts. Also, Fasabezvar, Fasmin, and Vetoka from the metal group, Vepakhsh, Desobha, and Depars from the pharmaceutical and shoyande, Shepdis and Shefan from the chemical group won the first and third places.

Originality/Value: 

In the last four years, various works have been done in this field, but less work has paid attention to the uncertainty in experts' opinions. Regarding other innovations of this article, it can be pointed out that the three industries of metal, chemical, and pharmaceutical, due to the importance of these industries, have not been specifically studied. Regarding the method of prioritizing criteria and stocks, less attention has been paid to new decision-making methods and uncertainty in decisions. Therefore, using new techniques, the importance of criteria can be determined with higher accuracy, and a better return on investment can be obtained.

This article studies an issue in the fish farming industry in which the goal is to find the best multi-period planning for handling various chains, including ordering, breeding, and selling of trout over a time horizon.

In this study, a new formulation is presented as a mixed integer linear programming model that could find the optimum solution quickly. In the new proposed formulation, some intermediate stages of the breeding chain that do not affect decisions are ignored, and therefore, the size and complexity of the proposed model reduce without compromising the optimality of the answers.

After implementing the proposed model, using different data samples, it can be seen that this model achieves the optimal solution in a short time, including volume and time of spawning in each breeding chain and different periods, harvesting time, and accepting or rejecting the main demands.

Originality/Value:

 In this paper, the issue of scheduling of fish farming chains and sales management, which there are a few studies in this field, has been studied and a new mixed integer linear programming model is presented. Compared to the previous model, this model has more realistic assumptions and less complexity and execution time.

Most tax policies are based on how taxpayers make decisions based on classical economic models. However, studies show that conventional decision-making models, which are designed without socio-psychological foundations and based only on economic components, can not explain the developments and the exact way decision-makers work. Considering that the issue of preventing tax evasion by taxpayers is very important and necessary, in this study, using mathematical modeling and scenario making tools to calculate tax crimes, the theory of behavioral economics and the theory of expected desirability have been compared.

In this research, using mathematical modeling tools and a questionnaire to calculate tax crimes, the theory of behavioral economics and the theory of expected desirability have been compared.

The results indicate that the amount of crimes calculated in the theory of behavioral economics is closer to crimes in the real world. Therefore, the obtained results are a good justification for choosing the theory of perspective instead of the theory of expected utility, and by adding the parameter of tax ethics, the amount of tax penalty in both theories is reduced.

Originality/Value: 

Since a healthy economy is an economy that is mostly based on taxes and has tried to cover government expenditures through taxes. To achieve this, the country's tax system must be reformed.

Delisted companies, despite their importance in the economic and social issues of society, is less considered in the financial literature. This issue is important because for each country, one of the criteria for economic measurement is the size of the capital market. Therefore, the delisted companies not only destroys the company's reputation, its stock price and the market for the sale of its shares, but also affects the growth of the market and the economy of each country. The present study seeks to review the financial statements and audit reports of active companies and compare it with delisted companies to design a model for forecasting delisted companies in the Tehran Stock Exchange with the help of artificial intelligence modeling techniques.

In this study, which was conducted on companies of the Tehran Stock Exchange, data related to three years before the delisting of 73 companies removed from the stock exchange from 2003 to 2019 in the first group and data of 148 active companies that are continuously. They were present in the stock market in the second group and were selected by systematic elimination method. Then, with data mining techniques, which are among the most efficient and up-to-date models of artificial intelligence, and with the help of multi-layered perceptron neural network classifiers, decision tree, and Bayesian theory classifiers, stock delisted companies have been predicted.

The findings show that the Bayesian classifier had the best performance and the multilayer perceptron neural network was in the second place and the decision tree classifier was in the third place.

Originality/Value:

 Little research has been done in the field of predicting delisted companies from the Iran capital market. This study by filling this gap, suggests to researchers to use other classifiers, combine several classifiers together to better cover the errors of each, combine classifiers with each other and weigh in a way that is more accurate, add other variables influential in the dismissal of companies, including the ownership structure and shareholder composition can have other results.

This research proposes a multi-objective and robust model considering both cost and risks related to the environment (water consumption and environmental pollution), social responsibility (working conditions and employee health), operations (change in demand and return rates), and disruption (accs and diseases such as COVID-19) in the supply chain, using horizontal collaboration to deal with it.

In this research, mixed-integer linear programming and robust optimization technique have been used for closed-loop supply chain network design and a multi-objective method has been developed to solve the problem and create Pareto spaces.

The results of the calculations show the effect of failure probability on the capacity of the facility, the total cost of the network and the degree of collaboration between members of the supply chain to deal with the risk. Also, the amount of cost required for allocation to reliable and unreliable facilities and also creating a suitable Pareto space for deciding on the optimal choice of facilities, capacity and flow between them and iron and steel production technology, according to sustainability and social responsibility indicators, are other research findings.

Originality/Value:

 In this study, for the first time, the design of a robust, sustainable, and resilient network of iron and steel under different risks has been studied. Horizontal collaboration has been used as a new approach to deal with risk and solution method for multi-objective problems has been developed. Using the results of this study, the decision-maker can make informed decisions about the supply chain under risk conditions by considering suitability for each of the objectives.

Futurology of the aluminum industry, as a strategic industry and one of the sustainable development pillars, is contemplated by the government strategists in order to know future against unpredictable elements. The purpose is to identify and analyze the upcoming scenarios of the aluminum industry and, finally, its market analysis, to draw a correct vision of the future and choose the appropriate strategy in this industry.

Firstly, the key variables affecting the development of the aluminum industry are identified through interviews with experts and specialists in the industry, and the probability of variables is determined by completing a questionnaire; and then based on the extracted variables, specific scenarios are developed. The interactions of the variables will be determined using the fuzzy DEMATEL method and the matrix obtained from this method will be part of the supermatrix of the analysis network process. Game theory will be used in two forms, considering competition and or cooperation among players to analyze the aluminum industry market, such that one can find balancing points among existing situations of players’ strategic options and scenarios of this industry.

The results indicate that the possibility of "increasing inflation" is the most likely and "increasing investment in the country" is the least likely variable, and "lifting sanctions" is the most influential variable and "production and export" is the most impressive variable. Five scenarios as "Iran's difficult scenario in the ordinary world", "Iran's catastrophic scenario in the ordinary world space", "disaster scenario for Iran in the difficult global space", "Iran's developing scenario in the ordinary world space", and "Iran's favorable space scenario in the difficult world space "were ranked. Three main players, namely policymakers, smalters and downstream, were identified and 13 strategic options in the form of investment, pricing, exports, and energy rates in different directions and shapes were extracted. Players’ preferences in different scenarios are explained in the form of 12 modes and, selected modes are extracted for each scenario based on different equilibrium points.

Originality/Value:

 Futurology has been the main subject of this paper, which, by analyzing the aluminum industry and the activities of the main players active in this industry, paints a picture of the future of this industry, including the entire value chain in a five-year horizon.

The main objective of this study is to rank methods of improving debt-asset management at branches of Bank Sepah in Tehran.

Questionnaires were the tool to collect data, and statistical sample is 146 managers and experts of Bank Sepah in Tehran that have been selected by the simple random sampling method. In this study, by using Multiple Criteria Decision-Making (MCDM) techniques of fuzzy TOPSIS, we have ranked the goals of debt asset management in Bank Sepah.

Based on the results, among the main criteria for Asset and Liability Management (ALM) goals, “the risk management of interest rate” with a weight of 3. 83 is at the first priority, then the “maintenance of adequate capital” with a weight of 3. 67 is in the second place and then “liquidity risk management” with a weight of 3. 41 is in the third priority. Also, according to Friedman test results؛ there are differences between the achievements for each of the major debt-asset management in Bank Sepah in Tehran.

Originality/Value: 

This study is a mixed method (Delphi (qualitative) and survey (quantitative)) in terms of performance and in terms of data collection. By using MCDM techniques, we have ranked the major objectives of asset-debt management in Bank Sepah. In addition, the results could be used in the planning process of banks and financial institutions.

The purpose of this research is to provide a hybrid and improved version of one of the Multi-Attribute Decision-Making (MADM) methods, which is a more useful tool for decision support due to the constructive interaction with the decision-maker.

For this purpose, a linear programming problem with weight restriction and discrimination intensity functions was used to present the improved WASPAS method, and thus, when the explicit weight for the indicators was not taken from the decision maker, it was possible to rank the options.

The results of comparing the use of the proposed method with the WASPAS method showed that this method has a good capability for use in multi-criteria decision making problems and in this particular issue, the results of using this method showed that in the context of the Covid-19 epidemic, the supportive leadership style had the highest rank among the leadership styles, followed by the transactional leadership, participatory, transformational and was placed authoritative.

Originality/Value: 

In this study, the improved WASPAS method was presented to determine the priority and weight of criteria in solving MADM problems and to determine the organizational leadership style in the Covid-19 pandemic.

Portfolio optimization is a selection of assets with the lowest risk and highest return. Asset performance evaluation is a useful way to choose assets and construct a profitable portfolio. For this purpose, the non-parametric Data Envelopment Analysis (DEA) method is used, which is a suitable tool for measuring performance. By the fact that stock returns are not normally distributed and usually exhibit skewness, kurtosis and heavy-tails, which definitely affects the assets performance, we have to consider the characteristics of the returns distribution. In the proposed model, we apply the Variance Gamma (VG) process, which covers the skewness and kurtosis of returns. As a result, we construct a portfolio by selecting assets which their performance is more realistic.

In the introduced model, the only input of the model is Conditional Value at Risk (CVaR), and the mean return and Sharpe index are the model’s outputs. Since the outputs can be negative, the model is inspired by VRM in the output-oriented DEA model, which deals with negative values. As the returns on stock are VG distributed, its parameters are simulated by the method of moments estimation, and then the process factors are simulated by the Monte Carlo technique. Finally, the scenarios of returns are obtained, and the assets performance is evaluated.

The correctness of the model is investigated by evaluating the relative efficiency of 7 companies from different industries in Iran Stock market. The results show that by considering the returns distribution characteristics, the input and outputs values of the model are estimated more realistically and more reliable results can be obtained; thus a profitable portfolio can be constructed.

Originality/Value: 

Evaluation of the assets performance by taking into account the returns distribution characteristics leads to realistic results.

The Analytic Hierarchy Process (AHP) is a multiple criteria decision-making method extensively used in various fields. Prioritization of decision criteria or alternatives from pairwise comparison matrices in AHP has been studied extensively. This article proposed the “Double-Frontier DEA” approach for prioritization in AHP. This new approach uses two optimistic and pessimistic DEA models to obtain the best local priorities from a pairwise comparison matrix, regardless of whether it is fully consistent or not.

One of these methods is Data Envelopment Analysis (DEA). The combination of DEA and AHP (DEAHP) is used to obtain and aggregate weights in AHP. Studies show that DEAHP fails in obtaining and aggregating weights in AHP and sometimes produces priority vectors contrary to evidence for inconsistent pairwise comparison matrices that limits its application.

This new approach uses two optimistic and pessimistic DEA models to obtain the best local priorities from a pairwise comparison matrix, regardless of whether it is fully consistent or not. Some numerical examples, including a real application of AHP for selecting an innovation team for a university, are provided to specify the advantages of the proposed approach and its potential applications.

Originality/Value:

 The double-frontier DEA approach generates true weights for fully consistent pairwise comparison matrices and best local priorities for inconsistent pairwise comparison matrices, that are logical and fit subjective judgments of decision-makers.

Data Envelopment Analysis (DEA) is a technique used to assess performance and measure the relative efficiency of Decision Making Units (DMUs) through linear programming. In most cases, DEA models evaluate inefficient units on the boundary of the production possibility set using reference points that are not Pareto efficient. Consequently, these models often yield zero weights for multipliers, failing to justify all sources of inefficiency. This paper aims to introduce a model that generates non-zero weights.

Weight restriction methods have primarily addressed the issue of non-realistic weights. We impose constraints on the weights in the proposed model to achieve our objectives.

This paper presents a one-stage method based on the BCC model, incorporating weight restrictions, to evaluate the relative efficiency of decision-making units. The proposed model ensures non-zero weights and prevents dissimilarity between weights while maintaining feasibility. Notably, the proposed model does not require any prior information on weights or the classification of units, reducing the complexity of the problem.

Originality/Value: 

To highlight the strength of the proposed method, the model is implemented on two case studies and compared with the results obtained from standard BCC models and those of Ramon and colleagues. The results indicate the superior performance of the proposed model.

The current paper tries to determine the type of returns to scale in a decision-making unit under the condition that integer-valued inputs or outputs are present.

This paper introduces radial models for determining the value and type of Returns to Scale (RTS) in 4 scenarios, including single integer-valued input – single real output (scenario one), mixed inputs – exclusively real outputs (scenario two), exclusively integer-valued inputs  exclusively real outputs (scenario three), and exclusively integer-valued inputs – exclusively integer-valued outputs (scenario four); in each scenario, the values of the left RTS and right RTS are determined, and the RTS type is then determined on that basis. Finally, by presenting three examples based on two scenarios, namely single integer-valued input – single real output and single integer-valued input – single integer-valued output, the new method is compared with previous methods using GAMS software, and the conclusions are provided.

The type of returns to scale differs when integer-valued inputs or outputs are present as compared with the case where the inputs and outputs are assumed to have real values.

Originality/Value: 

This study focuses on the value and type of returns to scale for integer-valued data. For this purpose, returns to scale was modeled in 4 scenarios using input-oriented radial models, and in the fourth scenario (exclusively integer-valued inputs – exclusively integer-valued outputs), the modeling was carried out for output orientation as well. The existence of a difference between the results produced by our proposed model and those of the classical model was demonstrated through two examples, one using hypothetical data and the other real-world data.