DECISIONS AND OPERATIONS RESEARCH
Year:2023 | Volume:8 | Issue:1|Papers Count:10

Purpose: Nowadays insurance companies, same as other companies, are facing massive competition. This issue indicates the value of customer loyalty also a predictive model. Customers play a crucial role in the sustainability of organizations by constant repurchasing. Companies with loyal customers have more market share, and more money may return on investment. This article's main aim is to identify the factors affecting customer loyalty in insurance companies. Methodology: This research was quantitative, analytical-descriptive. In gathering information, Data was collected through the survey, and the findings are practical. In this way, two methods, Confirmatory Factor Analysis (CFA) and Artificial Neural Networks (ANN) were used. For localizing the factors extracted from other similar prior literature, first, the elements were examined by CFA with SMART PLS application due to some conflicts in the literature to evaluate whether each factor affects customer loyalty or not. Then, the elements were introduced to the ANN for training by this program. Findings: In this article, by using the MORGAN table, the sample size detected 384 people in 0. 05 error. Questionnaires were distributed randomly between four Iranian insurance companies, ASIA insurance company, ALBORZ insurance company, and PARSIAN insurance company. Based on Confirmatory Factor Analysis, elements of commitment, perceived quality, trust, perceived value, empathy, brand image, the attraction of other alternatives, and customer satisfaction impact the customer loyalty of insurers in these companies. The cost of change, nevertheless, did not have a significant effect on customer loyalty. Then, the factors used as inputs for the multi-layer perceptron training also customer loyalty are indicated as output. The model was designed with eight inputs, 110 nodes in the hidden layer, and one output the error was E= 0. 00992 and the regression = 0. 98684. Originality/Value: the finding of this research is, expanding a model for predicting customer loyalty in Iranian insurance companies.

Purpose: Banks as a service and financial economic enterprise, while accompanying the economic programs of countries, seek to benefit their stakeholders. In order to achieve this goal, they must be able to equip and allocate their resources optimally. One of the important issues is to identify the factors affecting the absorption of resources that the purpose of this study is to provide a suitable model to identify the factors affecting the supply of resources. Methodology: To achieve the purpose of the research, by reviewing the research background, mission of the bank and the opinions of banking experts, 62 factors were presented in the form of a questionnaire. After approval by banking experts, the questionnaire was distributed to a sample of 30 employees of Tejarat Bank in Zanjan province for pre-testing. Then its reliability was tested and confirmed by Cronbach's alpha. After field collection of research data, the effective components were divided into two main groups of external and internal organizational factors. Then the factors within the organization into four subgroups, Financial, physical, service and communication and human factors were separated. Finally, the main research model was extracted using the model of unattended neural networks (self-organized maps) and the research data were analyzed. Findings: Research findings show that, From the set of factors affecting the provision of banking resources, communication and human factors had the most impact and external factors had the least impact. Also, due to the lack of similarity between the models of research input vectors, the correlation between each of the factors affecting resource equipping was not confirmed. Originality/Value: In this study, using a new approach of neural network model (self-organized mapping) to identify and weigh the factors affecting the equipping of bank resources, the findings of which help to develop the literature in the field of resource equipping.

Purpose: The hotel industry has become a competitive industry at the international level in recent decades, and countries have tended to use developed models and new techniques, and provide innovations to maximize income from it. As a result, it is critical to pay attention to how we can manage hotel income while noticing travel and passenger transportation costs and use modeling compatible with this field to optimize goal achievement. Methodology: The problems of optimizing hotel revenue management, passenger cost management, and analyzing how to expand the transportation used by them have been studied in this research. One of the key issues studied is to predict how to transport a passenger and choose its type based on different modes of travel such as air, rail, water, and road based on the amount of the passenger’s budget. Findings: Many effective factors and criteria have been considered in the modeling done, and the amount of hotel reception capacity in the selected cities of travelers and the provision of various types of rooms with different pricing, and the examination of elements related to the services provided to travelers by the hotel and different accesses of the hotel, which is based on the hotel’s revenue model, affect on. It is useful to estimate the state of competitive factors of hotels. Noteworthy, the transfer and mode of transportation have been determined to predict the level of demand for hotel reservations for all types of travelers during different periods in different tourism seasons. This subject is based on the traveler’s budget allocated for paying expenses during the travel pattern and the related results extracted from the estimated income model, as well as the influencing factors in choosing the hotel and transportation. Originality/Value: In the current study, the design of NP-Hard problems led to the use of exact methods in small-sized problems and two multi-objective meta-heuristic algorithms, namely NSGA-II and MOPSO, in medium-and large-sized problems. The computation results show that the proposed algorithms are efficient and suitable methods for problem-solving.

Purpose: The purpose of this paper to evaluate the level of antifragility in the supply chain of a Daroopakhsh company. To improve the company's competitive position and confrontation to disruptions and breakdowns, the supply chain must move towards antifragility. Accordingly, the supply chain, in addition to being prepared to deal with and respond to disruptions, has the ability to recover pre-disruption conditions and create even better conditions. To move in this direction, it is necessary for decision makers to properly recognize the current position of their supply chain and make the right decisions to improve its dominance. Methodology: To achieve this goal, the present study intends to determine the declining performance of this supply chain system in optimal, current and minimum conditions using Demetel technique, graph theory method and matrix approach. Finally, using the importance-performance analysis method, the components of supply chain are analyzed and prioritize the improvement of each factor. Findings: Based on the results, respectively, supply chain structure, improvement and recovery, learning, flexibility and innovation are in the first to fifth priority to improve the dominance structure of the company's supply chain. Originality/Value: This research supports organizations in assessing the level of sufficiency of their supply chain and facilitates decision making. The following approach can simplify the dynamic nature of the environment for managing supply chain disruptions and even allow managers to compare different supply chains. Continuous assessment and monitoring of the level of chain volatility enables the creation of a competitive advantage to achieve greater market share even during a disruption or ongoing disruptions.

Purpose: In the most investigations of sustainability, including environmental, social and economic issues, in addition to the desirable outputs, undesirable outputs are also presented, which is an obstacle to sustainable development. In this regard, the purpose of this paper is providing an approach based on Data Envelopment Analysis (DEA) with different forms of weak disposability of undesirable outputs to move towards sustainability. Methodology: Presenting a DEA-based model, the sustainability and performance of each dimension of sustainability are calculated simultaneously, while undesirable outputs are present with different forms of weak disposability. The sustainability performance of provincial gas companies is examined using the proposed technique. Findings: The results show that the proposed method in the performance analysis of sustainability and its dimensions is efficient when undesirable outputs are presented. Originality/Value: DEA provides a variety of disposability to minimize undesirable outputs and moves to optimize. In this study, an integrated approach with different forms of weak disposability is presented to analyze sustainability.

Purpose: Despite the growing importance of the role of knowledge in promoting innovation performance and maintaining competitive advantage in a highly competitive global environment, organizations face many difficulties in utilizing and developing the external knowledge flow infrastructure. Promoting organizational learning based on absorptive capacity theory and creating exploration and exploitation structures based on organizational ambidexterity theory can be an explanation to help organizations to solve these problems. This study aims to explain the effect of knowledge absorptive capacity in achieving competitive advantage and the mediating role of organizational ambidexterity in a study in export companies. Methodology: The initial model was extracted from the literature and in the qualitative stage through in-depth interviews with experts, the final conceptual model was drawn. The research questionnaire, after confirming its reliability and validity, was distributed among managers and experts of Iranian export companies by random sampling method. The statistical population of the research were 570 top Iranian export companies. In the qualitative section, sampling was performed by theoretical sampling method and 11 managers of companies with more than 5 and 10 years of experience were selected. The field of activity of selected companies included: gas and petrochemical, steel, auto parts, pipes and fittings and food. Findings: In the quantitative section, a sample of 78 companies was selected with the help of G*Power software. Qualitative data analysis was performed by ATLAS. ti and quantitative data with Structural Equation Modeling (SEM) based on Partial Least Squares (PLS). The results show that the absorptive capacity does not have a significant effect on the competitive advantage. Nevertheless, the effect of this variable on organizational ambidexterity and the effect of organizational ambidexterity on competitive advantage is significant. Therefore, it can be said that organizational ambidexterity has been a perfect mediator in the relationship between absorptive capacity and competitive advantage. Originality/Value: The findings of this study provide a path for export companies in order to gain a competitive advantage. Companies can facilitate the flow of external knowledge into the organization by strengthening the ambidexterious organizational structures, creating learning environments and strengthening the capacity to absorb knowledge.

Purpose: Coronavirus (COVID-19) is a pandemic that has affected all countries of the world. Forecasting the spread of corona disease will lead to the necessary measures to be taken by the authorities to control this disease. These include increasing vaccinations, quarantining cities and banning entry and exit, increasing the capacity of hospital beds, setting up round-the-clock vaccination centers, requiring the use of masks in public places, and observing social distances. Therefore, predicting such cases will reduce the number of corona cases and therefore reduce the mortality rate. Methodology: In this paper, using the Singular Spectrum Analysis (SSA) algorithm, the sixth peak of coronavirus in Iran is predicted by considering the current situation. To improve the grouping process of the SSA algorithm, eigenvalues have been selected in the optimization process, so that the predicted time series of which has been significantly improved according to the error-index. Findings: Comparing the proposed method with other forecasting methods include Autoregressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), TBATS, and Neural Network Autoregression (NNAR), it is observed that the forecasting error is acceptable and the SSA method can be used for forecasting. Originality/Value: This article predicts a new case of COVID-19 using efficient method SSA and the presented results confirm the effectiveness of the proposed method.

Purpose: This article seeks to provide a flexible structure for the learning organization tailored to the conditions of Iranian schools. Using this structure, schools, as an educational organization, facilitate innovation and effectiveness in the face of an ever-changing environment. Also, teaching human values and principles of education help students become people who live in a healthy and civilized way in a world rich in future technologies. Methodology: This paper uses multi-criteria decision-making methods and fuzzy techniques such as fuzzy Delphi and DEMATEL and ANP techniques to provide an executive and operational framework in school learning organizations. Findings: The results show the structures of the learning organization, the skills of the learning organization, and the technologies of the learning organization as the main criteria of the learning organization in Iranian schools, respectively. Also, reinforcing leadership sub-criteria, knowledge management technology, personal abilities, and subjective models with the nature of cause play a crucial role in forming learning organizations in schools. Originality/Value: Researchers have identified the way for Iran to achieve the economic goals envisaged in the Iran Vision 2025 document, the transformation of Iranian organizations into learning organizations. However, the study of databases such as Irandak, scientific information of Jihad Daneshgahi, and the citation database of sciences of the Islamic world shows that limited efforts have been made in this direction, especially in schools in Iran. Without focusing on why and what the learning organization is, this article, using the dimensions and criteria introduced for the learning organization, points out how to provide a flexible structure for the learning organization appropriate to the conditions of Iranian schools.

Purpose: One of the most important issues in the field of production scheduling, which has recently received much attention from researchers, is Dual Resource Constrained Flexible Job Shop Scheduling Problem (DRCFJSP). To deal with unexpected disruptions such as machine breakdowns, the job schedule must be robust so that in the event of a malfunction, the job schedule works properly and deviate less from the optimal solution. The purpose of this paper is to study the DRCFJSP problem with possible scenarios of machine failure or workshop disruption. Methodology: In solving the under-studied problem, the assignment of jobs and the sequence of operations on each machine should be done in such a way that under any possible scenario, the maximum completion time is minimized so that the weight combination of system performance in average mode, system performance in worst mode, the penalty for violating the time window constraints of the due dates and the variance of the objective function value is optimal according to different scenarios. For this purpose, a Robust Scenario-based Stochastic Programming (RSSP) model based on a mixed integer linear programming model has been presented for this problem and has been solved by Gams software for validation in small and medium-sized problems. Also, due to the Np-hard nature of this problem, a meta-heuristic method based on Genetic Algorithm (GA) is proposed for solving the large-sized problems. Also, the results of a case study in Alborz Yadak company related to the problem of the research are reported in the article. Findings: The results of the proposed RSSP model indicate that GAMS software is able to solve these problems up to medium sizes in an acceptable time and achieve a controlled and robust solution. Numerical results also show the proper performance of the proposed GA as an alternative to solve the RSSP model in the large-sized problems. Originality/Value: In this paper, DRCFJSP problem is studied with possible scenarios of machine failure or disruption in the workshop. Also, a RSSP model according to the mixed integer linear programming formulation and a meta-heuristic Algorithm have been presented for mentioned problem in this article.

Purpose: Due to the increasing complexity of uncertainty and its impact on the supply chain network, many researchers have resorted to coping approaches with data uncertainty. In addition, the occurrence of any disruption in the supply chain networks can cause irreparable damage. Therefore, adopting appropriate strategies to increase the level of the supply chain network resilience toward any disruptive events seem to be necessary. Methodology: In this paper, a multi-objective, multi-period, and scenario-based mathematical model is presented in which objective functions of delivery time and total network cost are minimized, and to increase network resilience, non-resilience measures are also minimized. Furthermore, a Two-Stage Stochastic Programming (TSSP) approach has been utilized to overcome the uncertain nature of the input parameters. Goal programming has also been used to transform the model into a single-objective one. Findings: In order to prove the model's applicability, the real-world data of a case study of Mashhad has been implemented. Eventually, according to the validation and sensitivity analysis results, the proposed uncertain model has clear superiority over the deterministic model. Originality/Value: This paper presents a multi-objective linear mathematical model for designing the Pharmaceutical Supply Chain (PSC) network under the COVID-19 situation. Two indicators of time and resilience as optimization tools have been considered simultaneously.