Forecasting Models/ Time Series
Adel Gardoon; Nader Khedri; Ali Mahmoodi; Mehdi Basert
Abstract
Purpose: Financial statements are the main decision-making bases of capital market actors; which is affected by internal and external factors. Uncertainty in other markets is one of the most important factors affecting the financial statements of listed companies. As a result, the aim of the current ...
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Purpose: Financial statements are the main decision-making bases of capital market actors; which is affected by internal and external factors. Uncertainty in other markets is one of the most important factors affecting the financial statements of listed companies. As a result, the aim of the current research is to model the spillover of uncertainties of parallel markets on the types of profit management.Methodology: The present research is practical. The research period is a 10-year period with seasonal data between 2011 and 2021. VAR-MGARCH model has been used to investigate the spillover of uncertainties of parallel markets on the types of profit management.Findings: Based on the results of VECH, CCC, BEKK and DCC models to extract the uncertainty of the studied variables; VECH models had higher accuracy. Based on the results of multivariate GARCH models, the spillover effect between different markets was observed. As a result, the uncertainty of one market strengthens the uncertainty between other markets. Based on the results of vector autoregression model; Uncertainties of variables have a stronger effect on accrual profit management than actual profit management. The results of variance analysis show the fact that oil price uncertainty has the highest contribution in the interpretation of real profit management change and exchange rate uncertainty in the interpretation of accrual profit management change.Originality/Value: Uncertainties of parallel markets reinforce each other and increase the level of profit management in the investigated companies.
Strategic Planing
Zahra Joorbonyan; Ali Sorourkhah; Seyyed Ahmad Edalatpanah
Abstract
In a competitive environment , various ways to maintain, survive, or grow the organization are conceivable. Among these, marketing experts believe that customer loyalty is one of the most effective tools in facing this challenge. To achieve customer loyalty, various and diverse strategies have been mentioned ...
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In a competitive environment , various ways to maintain, survive, or grow the organization are conceivable. Among these, marketing experts believe that customer loyalty is one of the most effective tools in facing this challenge. To achieve customer loyalty, various and diverse strategies have been mentioned in the literature by researchers and experts, which organizations can use, depending on the conditions, one or a combination of them. In such circumstances, managers usually have several strategies at their disposal and must choose the most appropriate one(s) from among them. The present study aims to provide a combined approach for prioritizing customer loyalty strategies.
This research uses a matrix-based approach to robustness analysis, which can deal with both complexity and uncertainty. The proposed algorithm combines it with strategic planning tools (strategies derived from strategic objectives and SWOT analysis) for prioritizing and selecting strategies. The proposed approach was implemented in a case study on prioritizing customer loyalty strategies for a women's clothing boutique in Ramsar City. Available strategies, influential environmental variables, definitions of future scenarios, and the performance of strategies in different environmental conditions were determined based on the judgments of the problem owner.
The results showed that considering influential environmental variables (national currency value, market access and raw materials, lifestyle changes, investment security, government-private sector relations, and the speed of technological change), supplier selection, contractor selection, and attracting a sponsor have the highest priority strategies. Afterward, environmental advertising, collaborative production, and customer relationship management were placed in subsequent rankings. The outputs of the proposed approach indicate that considering the country's foreseeable future conditions, higher-priority strategies minimize environmental risks and their impact on the business.
The literature suggests that classical strategic planning approaches (QSPM) or multi-criteria decision-making approaches (MCDM) are used in most cases of such decision-making. Despite their capabilities and features, these approaches face challenges in dealing with variable and evolving conditions (future uncertainty). An alternative approach, robustness analysis, can consider alternative futures but cannot define available strategies. Based on this, combining the matrix approach to robustness analysis with classical strategic planning approaches will be a response to the above problem.
Robust optimization
Mohamad Ali Movafaghpour
Abstract
Purpose: 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 ...
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Purpose: 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.Methodology: 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.Findings: 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.
Scheduling Modeling
Niloofar Khalili; Parisa Shahnazari-Shahrezaei; Amir Gholam Abri
Abstract
This paper deals with modeling a nurse scheduling problem considering service level in uncertain conditions. In accord with the urgent need of hospitals to provide better services to patients, it is necessary to consider nurses’ preferences in the work shift scheduling. Hence, a multi-objective ...
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This paper deals with modeling a nurse scheduling problem considering service level in uncertain conditions. In accord with the urgent need of hospitals to provide better services to patients, it is necessary to consider nurses’ preferences in the work shift scheduling. Hence, a multi-objective model is presented by regarding the rules and regulations related to the assignment of nurses to work shifts, in which the service level to patients is also considered. Due to the uncertainty of the number of patients that refer to the hospital, this parameter is taken uncertain into account. In order to evaluate the output results, two numerical examples in small and large sizes with real data of Labbafinejad Hospital with 18-person and 90-person wards are designed and Epsilon constraint method is used to solve the small-sized problem. Computational results reveal that increasing the service level to patients is directly related to increasing the number of nurses per each shift. Moreover, because of NP-Hard nature of nurse scheduling problem, the large-sized problem (90-person ward) is solved by a Gray Wolf algorithm and on the basis of designing a new chromosome. The results obtained by this method include 35 different efficient solutions for nurse scheduling in this hospital.
Multi-Attribute Decision Making
Seyyed Ahmad Edalatpanah
Abstract
Purpose: Designing an appropriate model/approach for decision-making (incredibly strategic decisions) in a complex and uncertain environment has always been one of the researchers' goals. This study proposes a method that can consider the above dimensions and provide a suitable answer to the challenge ...
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Purpose: Designing an appropriate model/approach for decision-making (incredibly strategic decisions) in a complex and uncertain environment has always been one of the researchers' goals. This study proposes a method that can consider the above dimensions and provide a suitable answer to the challenge of choosing the best option in the Matrix Approach to Robustness Analysis.Methodology: In this research, the superior option is identified by converting the matrix elements of the robustness analysis into hesitant fuzzy elements and using the score function.Findings: Implementation of the proposed approach in four different problems that in previous studies faced with the challenge of choosing the best option showed that a more appropriate answer could be achieved using hesitant fuzzy elements.Originality/Value: Developing the matrix approach to robustness analysis to solve the problem of choosing a strategy regarding equal stability of options.
Financial modeling
Masoumeh Labbafi; Roya Darabi; fatemeh sarraf
Abstract
Asset- Liability Management (ALM) is an important activity in strategic planning of banks and it can be seen as an optimization issue where banks want to achieve their specific goals. The main purpose of this study is to provide a mathematical model for optimizing the assets and liabilities of Bank Melli ...
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Asset- Liability Management (ALM) is an important activity in strategic planning of banks and it can be seen as an optimization issue where banks want to achieve their specific goals. The main purpose of this study is to provide a mathematical model for optimizing the assets and liabilities of Bank Melli Iran under conditions of uncertainty with fuzzy fractional programming model over a period of 10 years (2009-2018). In order to achieve the above goal, 14 items in the assets and liabilities of the bank's balance sheet have been extracted to calculate the 9 variables used in the model and finally, the results obtained from solving the fuzzy fractional programming model in Lingo software environment indicate that the proposed model of the article is able to provide the optimal values of each of balance sheet items for future years according to the conditions of previous years and the value of the objective function for Bank Melli can achieve the desired capital adequacy ratio by considering optimization in asset and liability management decisions.
meta-heuristic algorithms
Ehsan Aghdaee; Ali Husseinzadeh Kashan
Abstract
In managing a project, reliable prediction is an essential element for success. Project managers are always looking for controlling their projects to make sure the the project is within acceptable limits. For a long time, the earned value management (EVM) for pursuing time performance and the cost of ...
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In managing a project, reliable prediction is an essential element for success. Project managers are always looking for controlling their projects to make sure the the project is within acceptable limits. For a long time, the earned value management (EVM) for pursuing time performance and the cost of the project has been used. However, using this method to valuate project time performance by utilizing the time performance index (SPI) by researchers and practitioners has been faced with serious criticism. Therefore, the present study proposes a framework for assessment and prediction of the temporal performance of each of the thread activities in project management. In this framework, using the multi objective league championship algorithm (MOLCA), the initial plan of the projects is optimized and then via using the Kalman Filter prediction method, project execution planning is done such that the projects in conditions of uncertainty could be forecasted and ahead horizon being demonstrated accurately with the least error for project managers. In this paper, in order to ensure the quality of the solutions, the output of the algorithm is compared with genetic algorithms (NSGII) and particle swarm optimization (MOPSO), where results demonstrate the superiority of the proposed algorithm.
Linear Optimization
Mehdi Allahdadi; Hasan Mishmast Nehi
Abstract
In this paper, solution space of interval linear programming (ILP) models that is a NP-hard problem, has been considered. In all of the solving methods of the ILP, feasibility condition has been only considered. Best-worst case (BWC) is one of the methods for solving the ILP models. Some of the solutions ...
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In this paper, solution space of interval linear programming (ILP) models that is a NP-hard problem, has been considered. In all of the solving methods of the ILP, feasibility condition has been only considered. Best-worst case (BWC) is one of the methods for solving the ILP models. Some of the solutions obtained by the BWC may result in an infeasible space. To guarantee that solution is completely feasible, improved two-step method (ITSM) is proposed. By using a new approach, we introduce a space for solving ILP models in which by two tests, feasibility and optimality of the obtained space has been guaranteed.