نشریه تصمیم گیری و تحقیق در عملیات
سال ششم شماره 1 (بهار 1400)

By increasing the use of container transportation, one of the existing problems is the constant capacity of container terminals due to problems, such as lengthy construction process, lack of budget and space to build new locations, as well as lack of manpower. Also, this constant capacity leads to problems such as reduced trade relations, increased maintenance and warehousing costs, increased transportation costs, and increased loading and unloading times, as well as container allocation problems. To solve this problem without increasing the area of the terminal, this paper present a mathematical model to allocate containers that can be used not only to solve this problem, but also in other cases such as transportation and shipping used. Due to the size of problems used in this research, a heuristic algorithm, namely LOGIC algorithm is used. According to studies, carried out in the literature, this algorithm has not been used in relevant problems so far. Also, due to the development of the LOGIC algorithm in this paper, it can be used for other large-scale optimization problems. The development of the proposed algorithm can be improved to solve the layout problem of maritime containers with other real constraints.

Considering maintenance strategy in models which schedule and allocate jobs to machines, will make the proposed models compatible with production environments. Furthermore, this will cause higher model efficiency in optimizing the production systems. To this end, a mathematical model for scheduling unrelated parallel machines is developed to minimize total weighted completion times. Also in this approach, availability constraints have been considered, and preemption is allowed. Due to executing preventive maintenance and emergency maintenance programs, machine inaccessible times have been added to job completion times. Since the proposed model has high complexity, in order to solve the problem, two meta-heuristic methods including simulated annealing and genetic algorithm are used. In addition, their performances are compared to each other. The results indicate the superiority of simulated annealing over genetic algorithm for this particular problem.

Survey academic performance by educational data mining is one of the most important issues in the field of educational management and researchers focus on it. The purpose of this study is to present an experimental method for appropriate algorithm selection in predicting students' academic status in two and three classes. Two-class database predicts the admission or rejection of students in the course, while the database of the three classes, in addition to admission or rejection, identifies students who are prone and elite. Using the previous articles in the field of educational data mining and experts' opinions, factors that effect on academic performance of students were identified and database was compiled based on them. After optimization of parameters and implementation of different algorithms, the performance scores of the algorithms were calculated using paired t-test based on three indexes include of accuracy, f-measure, and ROC, algorithms were compared by TOPSIS and VIKOR methods. In the two-class mode, Support Vector Machine algorithm in TOPSIS with value of 0.999115 and VIKOR with value of zero has shown the best performance. In the multi-class mode, the Logistic Regression algorithm in TOPSIS and VIKOR in turns with values 0.9986044 and 0.0009798 performances better than other algorithms. The proposed method can be used as a tool for selecting algorithm that has the best pergormance in educational data mining. Because choosing the algorithm to achieve accurate and exact results is very effective and can be taken into account in the process of counseling and preventing students' academic failure

The models of Data Envelopment Analysis (DEA) based on Goal Programming (GDEA) seeks to address some drawbacks of classical DEA by increasing the degree of resolution and providing real weights to Decision-Making Units (DMUs). Experimental results indicate that the GDEA models do not completely cope with these in some cases which are tested. Also, in calculating the optimal solution with different methods of evaluating the efficiency of units, we are faced with a group of Pareto optimal solutions that make a decision maker facing a serious challenge in choosing the most appropriate solution. To solve this, in the first step, this paper uses the concepts of fuzzy logic and then proposes the F-GDEA approach based on fuzzy logic in solving the GDEA models, which increases the resolution of the methods to rank the units. In the second step, by using the F-GDEA approach, we propose a new hybridized fuzzy approach called HF-GDEA for short, taking into account the various ranking results from the different programming models. With this new proposed approach, we combine the rankings obtained from different methods and present a new ranking for the DMUs. In other words, the HF-GDEA approach makes it possible to compare and thus select an optimal solution from Pareto's optimal solutions set. Finally, the proposed approach is applied to two practical examples and their numerical results are presented.

In this paper, we deal with a linear programming problem with non-symmetric trapezoidal intuitionistic fuzzy numbers. In recent years, many authors have studied the symmetric trapezoidal intuitionistic fuzzy numbers. After defining a ranking function and arithmetic operations on these numbers, they solved the intuitionistic fuzzy linear programming problem.But the main problem with their method was that only available for symmetric trapezoidal intuitionistic fuzzy numbers. Now in order to overcome this limitation, in this paper, we present a new arithmetic and a new ordering for non-symmetric trapezoidal intuitionistic fuzzy numbers. Then, we present the general model of an intuitionistic fuzzy linear programming problems and prove a number of important theorems for solving it. Then we present the intuitionistic fuzzy simplex algorithm and finally, by presenting two examples, we will show the application of this new approach and show its superiority over the fuzzy mode.

In the last decade, multi-criteria decision making methods have been used extensively to evaluate multiple units with similar task descriptions. One of the most widely used methods, which is based on mathematical principles, is the TOPSIS method. ranking mechanism in TOPSIS method based on performance distance measurement is from positive ideal and negative ideal and the existence of Outlier-data can have a negative impact on the calculations. in this study the modification of TOPSIS method so that Be able to control Outlier-data, is on the agenda. For this purpose modified algorithm TOPSIS method is introduced. With the aim of validating the proposed algorithm, the performance of 1951 branches of agri-Bank in the case study section has been evaluated and the results have been compared with the standard TOPSIS method. Calculation of the modified TOPSIS method by considering different coefficients of data scatter control and examination of the correlation coefficients show that the modified TOPSIS method has been able to control Outlier data well.

The green construction industry has been emerged with the urbanization development, the global populatation growth, and growing demand for more sustainable and eco-friendly structures and it has been expanded quickly regarding some benefits such as energy and resources saving, less greenhouse gas emissions, and optimizing the health of residents. The urban development and planning based on green buildings is more complexed compared with the one based on regular buildings, and as a result, it seems necessary to design the accurate and comprehensive planning for identifying risk factors for the success of green buildings projects. This study is presented with the objective of the identification and evaluation of the risk for green buildings based on a real case study in the city of Shiraz. To this end, based on the opinions of experts team including consulting engineers, designers, executors, contractors, and the literature review, 17 criteria are identified as the most important factors and they are classified in 5 groups including policies and standards, economic factors, environmental factors, management factors, and technical and quality factors. The relationship between risk criteria and sub-criteria is studied with DEMATEL procedure and the ranking of risk criteria is done by applying the analytic network process (ANP). The results show that government policies and complicated approval procedures, the project delay, the lack of specific insurance for green buildings and the lack of accurate estimation of investment returning are the most important risk factors of green buildings that urban designers must focus on them to increase the successes of the new emerging green construction industry.

The timetable is the problem of placing particular resources due to constraints in a limited number of times lots and space, in order to satisfy a set of goals that is used to a variety of problems. Among these problems, one can point out the University Examination Timetabling Problem (UETP), which is the particular importance in educational problems. The university examination timetabling problem defined as the assignment of a certain set of exams to a fixed number of time slots and rooms, so that it meets all the hard constraints, also soft constraints are optimized as much as possible. This research presents a modified approach to optimize the incapacitated UETP. In this approach, a proposed Genetic Algorithm (GA) is modified by local search operators. These operators will make alterations to the timetable. This involves shifting or changing scheduled exams and thus greatly improve the ability of the algorithm to search. The efficiency of the proposed approach is compared with other techniques from literature using the Carter’s benchmark. The computational results show that this approach is quite effective and competitive in improving the solutions and is able to produce better solutions in most of the datasets compared with other algorithms.