Journal of Operational Research in its Applications
Year:2021 | Volume:18 | Issue:1 (68)|Papers Count:8

This paper seeks to present a new multi-objective defensive location problem (MDLP) with the manufacturing cost and considering different capacities for facilities in a network. In the novel MDLP, to prevent the attacker from reaching strategic sites (cores), the defender locates various facilities in the vertices of the network. In this regard, a mixed integer programming is formulated to find the Stackelberg solutions that defender and attacker are the upper and lower-level decision makers, respectively. In this study, the goal of defensive strategy is to maximize the distance between the cores and the attacker, which aims at reaching the closest possible vertices to the cores. On the other hand, since the problem is an NP-hard problem, to find a satisfactory solution, an interactive fuzzy method based on the cuckoo search algorithm has been introduced. The application of the method is illustrated solving two random instances of MDLP.

In the traditional shortest path problems, there is one attribute associated with each arc. However, in many real applications several cost and profit attributes should be considered for the arcs. In the presence of multi attributes for the arcs, one can aggregate different attributes to determine a score for each arc. To this end, we propose two methods in this paper. In the first method we use the cross-efficiency evaluation for evaluating the efficiency score of each arc. We convert multi-objective shortest path problem to a single-objective optimization by inserting the cross-efficiency scores of the arcs in the objective function. The objective function of the proposed model is so designed to provide the shortest path with maximum efficiency between the source and the sink node in the network, called the efficient shortest path. Due to the discriminating power of the cross-efficiency evaluation approach, our method improves the discriminating power in determining the best path between two specific nodes in a given network. Furthermore, our estimated efficiency scores for the arcs have a global nature; that is, the efficiency score of each arc is measured against all the arcs in the network. In the second method, in order to finding the Pareto-optimal shortest path for a given network, we use a common set of weights, which it maximizes the efficiency of all arcs, for all arcs to convert multi-objective shortest path problem to a single-objective optimization.

The previous studies in the field of performance evaluation of academic centers and educational institutions are more concentrated on educational and research indicators, and the rankings made for universities are based on these indicators; whereas, today, the lack of financial resources of universities and especially the Islamic Azad University (IAU), is one of the issues that should be given special attention. Accordingly, the purpose of this research is to evaluate the financial performance of universities by using the combination of ANFIS and PSO meta-heuristic algorithms. This research has been applied in terms of the purpose, applied-developmental research and descriptive research in terms of method. The statistical population of this research has been all units of IAU across the country. The statistical sample size was selected based on Morgan's table of 214 units of the IAU. The sampling method was also purposeful and in order to obtain the required data for input and output variables. To collect data from the questionnaire, the Delphi method and the existing university documents were used. In order to analyze the research data, Delphi methods and comparative neural-fuzzy system combined with a particle congestion optimization algorithm have been used. The research results show that the designed system with the least error is able to evaluate and predict the financial performance of the university.

Hospitals are the most important resource consuming units in the health sector, so paying attention to these parts is very important. The purpose of this study was to investigate and evaluate using resources in Department of Imam Reza Hospital of Mashhad using the concept of efficiency to how using resources to improve their activities. This study is an applied research that was conducted in Imam Reza hospital of Mashhad in a cross-sectional study during 1395. Data was collected through the Hospital Information System (HIS). At first, we identified hospital input and output indicators. In the next step, we measured the efficiency and analyzed resource allocation. The models used were the linear programming type and Lingo version 14 was used to solve them. The number of physicians, the number of nurses and the number of active beds, as input factors and bed occupancy, the number of hospitalized patients were considered as the output indicators. Assignment analysis results showed that there is a possibility of reallocating resources in order to increase efficiency. Reallocation of the resources on average can increase the efficiency of 36 percent for different sectors. Therefore, the optimal allocation of resources is one of the key factors for improving the performance of hospital departments. Considering the direct impact on the quality of service should be seriously considered by health managers.

Multiobjective linear fractional programming (MOLFP) problems are the important problems with special structure in multiobjective optimization. In the MOLFP problems the objective functions are linear fractional functions and the constraints are linear, i. e., the feasible set is a polyhedron. In a MOLFP problem, let the coefficients of the constraints and the vector of resources be fuzzy, which construct a fuzzy multiobjective linear fractional programming problem. In this paper, we suggest a method to identify the efficient solutions of a fuzzy MOLFP problem by introducing some linear programming problems.

Returns to scale is an important topic in economics; however, one can study the rate of variation only in the neighborhood of inputs, because it is a local notion. Zarepisheh and Soleimani-damaneh proposed a method to determine returns to scale in disjoint intervals in their study. For this means, they used the simplex method and parametric analysis in linear programming. They disregard the probability of existing multiple optimal bases; this is a defect of their approach. In this paper, in the same point of view and studying on defect of that approach, we present another method based on supporting hyperplanes of the production possibility set.

Choosing a stock portfolio is always one of the most important issues for investors. Theoretically, selecting a stock portfolio can be solved by minimizing risk assumptions with the help of mathematical relationships, but with the variety of choices in the capital market, mathematical relationships alone are not an effective solution. The variety of investment tools and the differences in the functionality of investors’ complexity have complicated the selection process. Now the expansion of financial and capital markets, the use of rule-based systems for quick decisions, with minimal risk and away from human error, design, development, or improvement of these systems can be a competitive advantage. In the present study, neural network algorithms and genetic programming algorithms have been used to identify effective features and the decision tree to improve id3 has been proposed as a method for predicting price and trend of stock price change to select the optimal basket. The research results show that in addition to reducing computational and memory overhead, the proposed method is able to accurately predict severe fluctuations with nonlinear patterns and compared to modern methods such as nearest neighbor search, linear regression, autoregressive integrated moving average, and time series prophet algorithm will do better.

The present research aimed to identify the affective factors of the green supply chain in the Guilan steel industry with regards to a quantitative approach. The type of this study was applicable, and in terms of purpose, it is descriptive research. The present study used a mixed-method for data gathering by utilizing questionnaires and interview tools. The statistical population of the study consisted of steel industry experts and university professors in Guilan province. In the first stage, 25 individuals were selected through judgment, to identify the components and indicators of the green chain. In the second phase, 8 experts from the fuzzy DEMETEL questionnaire were provided. In the process of research, after identifying the indices using theoretical baseline study, 5 main factors and 25 sub-factors were identified using the fuzzy Delphi technique. Then, using the fuzzy DEMATEL technique, the degree of influence and effectiveness of each of the factors was found to be the most influential among the "environmental factor" with 0. 792 and the "financial factor" with 0. 996.