Journal of Operational Research in its Applications
Year:2022 | Volume:19 | Issue:2|Papers Count:8

Due to the growth of the aging phenomenon, the use of intelligent systems technology to monitor daily activities, which leads to a reduction in the costs for health care of the elderly, has received much attention. Considering that each person's daily activities are related to his/her moods, thus, the relationship can be modeled using intelligent decision-making algorithms such as machine learning. In this study, to model Mood according to the daily activities of the elderly, intelligent decision-making algorithms such as descriptive Bayesian network-based probabilistic model, support vector machine, K nearest neighbor, linear discriminant analysis, decision tree, and ensemble learning methods, such as Bagging and Boosting was used. For the mentioned purpose, an intelligent system, including smart home, wristband, and smartphone, was designed and prepared to collect the dataset, needed to implement, train, and evaluate the proposed intelligent models. With the intelligent system, an elderly woman's daily activities are recorded for five months and her mood status is labeled by a psychologist team. The obtained results prove that the decision-making methods of the support vector machine and the ensemble Bagging tree can accurately detect the person's moods according to her daily activities. However, in a practical aspect, the designed Bayesian network model is more effective to assist psychologists to analyze the causes of detected mood states, by providing a descriptive relationship between variables related to the daily activities and mood states.

One of the most important challenges in applying data envelopment analysis (DEA) approach in real-world problems is how to deal with data uncertainty. Because the results of data envelopment analysis method are significantly sensitive to data changes. In other words, a little bias or deviation in the data may lead to a significant change in the performance results and ranking of decision-making units (DMUs). Therefore, it is necessary to develop a robust formulation for DEA approach in order to deal with uncertain data. Especially when data for different scenarios have different values. Accordingly, the purpose of the current study is to present a robust data envelopment analysis (RDEA) approach that can be implemented under scenario-based data. The proposed research approach is also used to performance evaluation and ranking of research and development (R&D) projects in Iran.

Returns to Scale studies output variations with respect to input changes, which is presented both qualitatively or returns to scale (RTS), and quantitatively or scale elasticity (SE). This paper describes SE and then SE in cost efficiency (CE) models and uses data envelopment analysis (DEA) approach to calculate them. Since the sugar industry plays a significant role in the Iranian economy, the efficiency and CE of sugar mills are of crucial importance to prevent the waste of resources and to save on production costs, respectively. For this purpose, the SE in cost efficiency model is considered for fifteen sugar mills, which process raw sugar beets. We obtain the SE of these mills before and after the image of inefficient units on the technical and cost-efficient frontier. It is found from their scale elasticity that their RTS are constant. In this paper, the methods for integrating and normalizing many parameters with different measurement units to a smaller set are described. “Eighteen inputs-four outputs” are consolidated into “four inputs-one output” categories. Also, the methods for calculating the price per unit based on the total price are explained.

Human resource planning helps the organization to ensure its human resource condition, surplus manpower, lack of human resources or being in balance in different sections. Typically, the division of teaching staffs in the universities takes place according to the bargaining power without taking into account the actual needs. In many cases, universities have problems such as lack of accurate estimation in required staff, disproportion between the number of staffs and the amount of activities and the inability to plan employee’s overtime due to lack of proper planning. To solve this problem, this research has provided a mathematical model for optimizing the human resource planning of universities’ teaching staffs using integer programming. In order to design the model more appropriately and validate it, Ferdowsi University of Mashhad was used as a case study. At first, initial modeling was performed to conduct the research. Then, to solve the proposed model, its parameters were collected and finally, it was solved with the help of GAMS software. After validating the results, optimum plan for using staff is presented for a one-year planning horizon to the organization under study. Examination of the model outputs, while confirming the validity of the model, showed its proper quality.

The max+sum spanning tree problem is to find a spanning tree T* which makes the combined weight “max w(e)+Sum c(e) time” as small as possible, in which a weight w(e) and a cost c(e) are prescribed for each e ͼT. The problem can be solved in O(mlog n) time. In an inverse max+sum spanning tree problem, T0 is a given spanning tree of G, which is not an optimal max+sum spanning tree. Then we modify the weight vector w to w̅ so that T0 becomes a max+sum spanning tree. The goal is to minimize the modification cost ||w̅-w||under Hamming distance. This paper presents a new solution algorithm for the inverse max+sum spanning tree problem under weighted sum-type Hamming distance by modifying the max-weight vector with time complexity of O(mlog n). First, we formulate the problem and then present a new solution algorithm for the problem under investigation. Finally, we present a numerical example to illustrate the efficiency of the algorithm.

Obtaining the priority vector from a pairwise comparison matrix is an important subject in Analytic Hierarchy Process (AHP) and has been extensively addressed in studies on AHP. Furthermore, research has been conducted on employing DEA models for generating local weights of alternatives in AHP. This research proposes an approach based on double-frontier analysis for determining priorities in AHP. In this approach, two specific DEA models are used to obtain the best local priorities from one or a group of pairwise comparison matrices whether they are entirely consistent or not. The proposed method gives true weights for consistent mutual comparison matrices and yields general, but logical, weights for inconsistent ones. A numerical example is examined to demonstrate the advantages of the presented technique. Finally, a real-world example is presented to illustrate the proposed method.

Selecting the right alternatives to efficient use of available resources has always been considered by managers. Supplier selection is especially important because it has a direct impact on the performance and profitability of an organization. Green supply chain management as an environmental innovation with the integration of environmental thinking and supply chain management is a necessity for today’s humans. The purpose of this study is to determine the criteria of green supplier selection in an automotive group and supplier selection based on the extracted criteria. Decision making criteria were identified through literature review, interaction with experts and using the Delphi method. These criteria include green design, green purchasing, green production, green transportation, environmental accountability, environmental management system, and pollution control. Then a new group voting analytical hierarchy process was designed and used to select a green supplier in Saipa. The research experts were consisted of thirty persons of supervisors, production managers and senior managers in Saipa Company. The results of the implementation of the new group voting analytical hierarchy process showed that by obtaining less information from experts and in a shorter time than other decision-making methods, acceptable results can be achieved.

Nowadays, flexibility is essential in all components of the supply chain due to globalization and increasing disruptions and vulnerabilities. Since, supply chain resilience assessment under insufficient and ambiguous information conditions can be one of the main problems of multi criteria decision making problems, therefore, in this study, we introduce the gray theory, which is a suitable way to evaluate resilience in the presence of insufficient and vague information. Since the credibility of a fuzzy event is greater than the possibility of its occurrence, so the use of credibility in a fuzzy TOPSIS event is much more effective, But since in Fuzzy TOPSIS event, based on the credibility theory the weights of possibility and necessity are considered equally, For this reason, in this study, we introduce and develop Fuzzy TOPSIS method based on measure theory, in which the assumption of weights equality is eliminated, to evaluate the supply chain resilience in the banking industry. In the proposed model, the weights of the indicators are calculated using the BWM method, and finally the alternatives are ranked by the proposed method of Measure TOPSIS.