Journal of Operational Research in its Applications
Year:2021 | Volume:18 | Issue:3 (70)|Papers Count:8

The haplotype assembly problem aims at finding originate haplotypes of some fragments that obtained from sequencing methods. In diploid case in which organisms have pair chromosomes, like humans, the aim is to reconstruct two haplotypes such that each of reads is originated from one of the two reconstructed haplotypes. In diploid case, the problem is well studied and since it is NP-hard due to unavoidable errors of sequencing machines, exact approaches are of exponential order. So, many fast, but approximate approaches have been proposed. In triploid case, the aim is finding three haplotypes such that each read originates from one of the three haplotypes. The triploid case is much harder than the diploid case and faces with more computational difficulties. For this reason, a few researchers studied the triploid case of haplotype assembly problem. In this paper, two binary linear programming models are proposed for two cases of availability and non-availability of genotype data for triploid haplotype assembly and the computational efficiency of the models is tested on simulated datasets using AIMMS. The proposed models could be generalized to higher ploidy.

Preparing a timetable for university courses is a very time-consuming decision-making process that is done in each semester at higher education centers with many restrictions in mind. In this research, a multi-objective linear mathematical model for scheduling university courses is presented. The objective function of this research includes three functions as minimizing the total of the difference between the beginning and end of classes per day for different groups of students, maximizing the usefulness of presenting courses and minimizing the number of courses that the distance of their sessions is less than one day. The restrictions include restrictions on the non-interference of professors, classes, and students' curricula, as well as restrictions on how different courses are presented. In the proposed model, the method of presenting three-unit courses with a two-week period is considered. Finally, the performance of the proposed model using GAMS software is shown for a real example.

In this research, the interval linear fractional programming model is considered. Since this model is an interval model, hence we are looking for methods where an optimal solution set is obtained. In this paper, we suggest two methods for the determination optimal solution set of the ILFP model so that these methods are formed from two sub-models. The obtained solutions solving these two sub-models form a region that we consider it as optimal solution set of the ILFP. If the obtained solution satisfies in largest region of interval constraints of the ILFP model, the solution is called feasible. In the first method, we gain an optimal solution set that some of its points may not satisfy some constraints of the largest region, hence we use an alternative method to improve the optimal solution set such that we will able to remove the infeasible part of the optimal solution set of the first method by an alternative method and obtain a feasible optimal solution set. In the second method, to ensure that the optimal solution set is completely feasible, we add a supplementary constraint to the second sub-model and we obtain a feasible optimal solution set.

Efficiency evaluation is an important and key issue in competitive conditions. Organizations and companies face various uncertainties which make their efficiency evaluation extremely difficult and complicated. In this research, open-network data envelopment analysis models have been developed for three uncertain states, including uncertain outputs, uncertain inputs, and uncertain concurrent inputs and outputs. The proposed models are used to evaluate the efficiency of 10 two-stage processes for sellers and buyers in a supply chain, and the effect of data uncertainty is investigated. The results obtained from the developed models have been compared with the results of traditional DEA network models. The validity and accuracy of the developed models have also been examined. The results show that the proposed models are more reliable than the traditional DEA network models. Also, by examining the efficiency of decision-making units in different conditions of data uncertainty and deviations, it was determined that with increasing uncertainty and deviation, the efficiency score of different units decreases, which is consistent with reality.

Performance evaluation is one of the most important ways to deal with the performance of organizations compared to their previous situation or other competitors, which makes it possible to take actions for performance improvement. In this study, using a hybrid data envelopment analysis and artificial neural network approach, the performance of the Iran Daroo pharmaceutical company has been evaluated. For this purpose, first by reviewing the literature, the company’ s evaluation criteria were studied based on the balanced scorecard perspectives and then more important criteria were identified according to the experts' judgments. Accordingly, due to the necessity of studying and evaluating the performance of the Iran Daroo pharmaceutical company, its performance was evaluated over the 4-year period (2014-2017) using DEA, under the assumptions of constant return to scale and output-oriented approach. Next, using an artificial neural network algorithm, the performance of the company was predicted. Finally, the results of different artificial neural networks under different layers were examined and the results of the network with the most appropriate number of layers were compared with the other machine learning algorithms based on the accuracy, correctness, invocation and error indexes. The results illustrate better performance of the proposed model compared to the decision tree, naive Bayes, support vector machine and K-nearest neighbor algorithms.

Asset and Liability Management (ALM) is one of the key issues in strategic planning in banks. Whereas asset and Liability management has provided a ground in which various financial activities can be centrally assessed, most financial analysts use this approach for long-term strategic planning of banks. In this study, considering a fractional planning model through nonlinear constraints, it has analyzed the assets and liabilities management of the Melli Bank of Iran with 10-year data (2009-2018), so that in previous research, most of the planning problems were solved through other linear programming models. To achieve the above objective, 14 items existing in the assets and liabilities of the balance sheet are extracted for computing of 9 variables used in the model and Finally the results obtained from solving the fractional programming model in Lingo software indicate that asset management in Melli Bank of Iran has been in a better position than the debt ratio in 2016-2018 by comparing real and optimal ratios. The results also show optimizing of the present situation a higher capital adequacy ratio of up to 16% compared to the ratio approved by the Basel Committee 3.

Evaluating the performance of countries in international markets and measuring how they use allocated resources provides a clear picture of the state of trade management in countries. If there is information about the performance of export markets, you can make clear business rules, allocate quotas and loans by international organizations such as the World Bank and its subsidiaries, financial and non-financial support, allocate credit and establish financial and non-financial transactions with a clear vision. The main purpose of this paper is to better understand and accurately assess the current state of research in the field of evaluating the performance of countries' export markets and identifying the indicators and variables used in this regard. In this research, the systematic literature review (SLR) method has been used to collect and review previous studies. By integrating automatic and manual search strategies, all related research articles published in this field in the period from 2009 to 2019 in Persian and 2008 up to 2018 in English are covered. The results show that countries' performance evaluations are mainly based on economic indicators and focus on gross domestic product standards that focus more on the economic dimensions of performance. Because these criteria are not able to explain the non-economic dimensions of functional performance of countries, researchers have evaluated non-economic criteria such as level of education, human capital, environmental indicators, and so on. Finally, it is suggested that economic and non-economic criteria be evaluated simultaneously to evaluate the performance of countries’ export markets.

Traditional models for the Data Envelopment Analysis (DEA) like the modified Russell model consider the decision-making units as a black box and use only the initial and final inputs for measuring the performance of decision-making units congruent with network structures and do not consider their internal operations. Therefore, the origin of inefficiency in the structures will not be specified precisely. The network DEA models have been developed to resolve this problem. The overall efficiency and the stage efficiency score have not been considered in the modified Russell model due to the intermediate data. In this paper, a model based on the modified Russell model has been presented for evaluating the performance of general two-stage network structures, which in it some variables have been added to the model for determining the target (optimal) intermediate data. Besides the capability for evaluating the overall efficiency and the stage efficiency scores, the proposed model is capable of presenting some solutions for improving the efficiency of the any stage by changing the optimal intermediate data. The validation of the proposed model has been investigated by three mathematical theorems proving. A real case study in an education system is modeled by the proposed model.