Iranian Journal Of Operations Research
Volume:12 Issue: 2, Summer and Autumn 2021

Developing realistic models for the evaluation of sustainable supply chains has turned into a major challenge facing managers. The decision-making approaches proposed here consist of two stages. At the first stage, a dynamic-network data envelopment analysis (DNDEA) model is established for the first time, wherein the current efficiency of a business can be influenced by its prior social and environmental activities, as two main dimensions of sustainability. The second stage correspondingly presents, for the first time, a model in which total efficiency is calculated based on the value of historical data. Sensitivity analysis is exploited to determine the more effective factors of sustainability in efficiency evaluations. To validate the model, it is used to assess the sustainability of the suppliers of an auto spare parts manufacturer. The study results reveal that the model is well-able to evaluate the performance of dynamic network structures, with a very high discriminating power. Following the implementation of this model, only the supplier(KARAN) is found to reach the efficiency limit, and  SIRIN S.N. is recognized as the most inefficient supplier with an efficiency score of 0.6409. The sensitivity analysis outcomes demonstrate that the least amount of efficiency change is related to the economic pillar; however, the rising trend in wage costs, compared with other economic factors, brings a better effect on augmenting the efficiency of some inefficient suppliers. The highest efficiency changes during sensitivity analysis are further observed in both social and environmental dimensions. Therefore, it is claimed that investing in these two pillars can have a significant impact on the efficiency of suppliers.

Data envelopment analysis (DEA), as a well-established nonparametric method, is used to meet efficiency evaluation purposes in many businesses, organizations, and decision units. This paper aims to present a novel integrated approach to fuzzy interpretive structural modeling (FISM) and dynamic network data envelopment analysis (DNDEA) for the selection and ranking of sustainable suppliers. First, suppliers' efficiency values in a supply chain are determined, using the dynamic network data envelopment analysis (DNDEA) model developed for this purpose. Then, a heuristic method is presented based on the fuzzy interpretive structural modeling (FISM) to find a common set of weights (CSWs) for the variables involved. Depending on the data conditions, two approaches, viz. centralized and decentralized, are proposed for efficiency measurement. To illustrate the model's capability, the proposed methodology is further applied to the real data of a company, named Nirou Moharekeh Industries (NMI). The results of a study on 12 suppliers within the DNDEA model accordingly reveal that one unit (i.e. KARAN) obtains an efficient value, but an inefficient score is observed in 11 units, whose technical efficiency value is in the range of 0.6409 to 0.9983. After utilizing the weights gained from the heuristic method, the efficiency value of the most inefficient supplier (that is, SIRINS.N.) dwindles from 0.6409 to 0.6319.

This paper develops slacks-based measure (SBM) and additive SBM (ASBM) to evaluate efficiency of decision making units (DMUs) in a two-stage structure with undesirable outputs and feedback variables from the internal perspective. The SBM model is linearized  for a specific weight and the ASBM model is reformulated as a second order cone program. The target values for all inputs, outputs (both desirable and undesirable) and intermediate products are  provided. This study shows that unlike the SBM model, ASBM can be adapted to the preference of the decision maker by selecting the weights to aggregate stages in the network.

Nowadays, the capability of cloud management suppliers is one of the important advantages for suppliers that can improve the performance and flexibility and reduce costs in companies through easy access to resources. Also, the environmental impacts of suppliers are a significant issue in today’s industrialization and globalization world. This paper analyzes these subjects by fuzzy multi-objective scenario-based stochastic model. Its objective functions are minimizing the total cost, environmental impacts of suppliers, and maximizing the capability of cloud management of suppliers. Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) and Multi-objective Simulated Annealing meta-heuristic (MOSA) are developed to settle this problem. Five computational experiments analyze the performance of the solution algorithms. The results illustrate that the NSGA-II algorithm provides better solutions than the MOSA algorithm for the presented model.

‎In this paper‎, a nonmonotone line search strategy is presented for minimization of the locally Lipschitz continuous function‎. ‎First‎, the Armijo condition is generalized along a descent direction at ‎the ‎current point. Then, a step length is selected along a descent direction satisfying the generalized Armijo condition. We show that there exists at least one step length satisfying the generalized Armijo condition. Next, the nonmonotone line search algorithm is proposed and its global convergence is proved. ‎Finally‎, ‎the proposed algorithm is implemented in ‎the‎ MATLAB environment and compared with some methods in‎ the subject literature. ‎It can be seen that the proposed method not only computes the global optimum also reduces the number of function evaluations than the monotone line search ‎method.‎

Establishing safety at work is one of the essential and necessary conditions for starting, performing, ending and exploiting work. Due to the importance of this issue, in the present study, the evaluation and management of safety risks in the construction industry in the direction of human health using multi-criteria decision-making techniques have been done. In the framework of the proposed method, safety risks in construction projects were first extracted according to the study of previous researches and opinion polls of experts and experts, who are divided into four general categories including machinery, fire, work at height and unexpected accidents. Then the questionnaire is designed based on these risks and is distributed among the statistical sample. After reviewing the validity and reliability of the questionnaire, the mentioned factors are ranked based on the costs of providing workforce for safety and health of workforce using the fuzzy hierarchical analysis method. The results of fuzzy hierarchical analysis method show that the factor of people getting stuck between machines is the most important that should be considered in all stages. The next most important factor is the accident with the machines. The third factor is the throwing of materials from machines. Thus, due to financial constraints in this regard, in order to manage safety in construction projects, it is necessary frst to consider the factors that have priority. In the end, based on the obtained rankings, suggestions are provided in order to ensure the health of human resources.

The purpose of the present study was to determine the effect of education using mathematical games on learning and retention of third grade elementary students. This research in terms of purpose was conducted as an applied research. Also in terms of implementation and data collection method, the quasi-experimental method and pre-test-post-test design with a control group was used. The statistical population of the present study included all 6,500 female third grade elementary school students in District 1 of Mashhad- Iran. The sampling method in this study was in convenience form that included 60 students and were selected through convenience sampling method, thus two classes with 30 female students for each classroom were selected among the elementary girls' schools in District 1 of Mashhad- Iran. In order to collect data, two researcher-made tests of learning and retention were used, the validity of which was confirmed by experts and its reliability was calculated based on Cronbach's alpha equal to 0.81 and 0.83, respectively. Multivariate analysis of covariance (MANCOVA) was used in order to test the hypotheses inferential analysis. The results of data analysis showed that math games are effective on students math both learning and retention (P <0.01). Therefore, it can be concluded that education using math games is effective and has increased students' learning and retention.

This study developed a mathematical programming model in order to consider an SCND problem. In this model, the operational and financial decisions are simultaneously considered to design a supply chain network. It also paves the way for opening or closing facilities in order to adapt to fluctuations at market. Furthermore, an accounts payable policy is provided for the company managers because bank loans, liability repayment and new capital from shareholders are considered as decision variables in this model. The economic value added (EVA) index was also used besides the common operational objectives and constraints in order that the financial performance of supply chain and lower and / or upper limit value for financial rations to be measured. To demonstrate the efficiency of the proposed model, a test problem from the recent literature is used. And also, sensitivity analyses to evaluate the results are provided to obtain better insight and access to different aspects of the problem. The results illustrate that with appropriate financial decisions, creating more value for the company and its shareholders is achievable since the total created value by the proposed model with a new financial approach is able to improve the total created shareholder value as much as 21.05% and convince the decision-makers to apply it as an effective decision tool.

In this paper, we present a new primal-dual predictor-corrector interior-point algorithm for linear optimization problems. In each iteration of this algorithm, we use the new wide neighborhood proposed by Darvay and Takács. Our algorithm computes the predictor direction, then the predictor direction is used to obtain the corrector direction. We show that the duality gap reduces in both predictor and corrector steps. Moreover, we conclude that the complexity bound of this algorithm coincides with the best-known complexity bound obtained for small neighborhood algorithms. Eventually, numerical results show the capability and efficiency of the proposed algorithm.

Due to the changes of performance measures, a vital aspect for decision makers is finding optimal scale sizes of entities. Moreover, there are undesirable measures in many investigations. In the existing data envelopment analysis (DEA) approaches, optimal scale sizes (OSSs), average-cost efficiency (ACE) and average-revenue efficiency (ARE) of decision making units (DMUs) with desirable measures under strong disposability have been estimated while undesirable factors are presented in many real world examinations. Accordingly, in this research, OSSs and ARE of DMUs with undesirable outputs are addressed under managerial disposability. ARE is defined as the composite of scale and output allocative efficiencies under managerial disposability. To illustrate in detail, a two-stage DEA-based approach is rendered to estimate ARE and OSSs in the presence of undesirable outputs. A numerical example and an illustrative case are given to explain the proposed approach in this study.

The goal of this study is to use the grounded theory technique to uncover the characteristics that influence personal branding in the food business. The current study is quantitative in terms of approach and applied in terms of goal. The systematic technique of Strauss and Corbin was applied in this study, which involves three basic procedures: open coding, axial coding, and selective coding. The snowball sampling approach was utilised in this study for sampling. Snowball sampling is generally the first step, and it continues until saturation is reached. Based on this, 19 specialists and managers in Iran's food business who were familiar with the term personal branding were interviewed in-depth and semi-structured interviews. MAXQDA 11.1.4 software was utilised to analyse the data in this investigation. The results showed the components identified in personal branding in casual conditions of this study including personal characteristics, business characteristics, strategies including identity tools defined in behavior, appropriate tools for illustration, social networking, social responsibility, use different and distinctive methods, focus on goal, market research, contextual conditions including cultural values, political, economic and social issues, ideas and beliefs, dynamic world and intervening conditions including criticisms and suggestions, difference between cyberspace and real world, audience expectation level, social norms and outcomes include advancing the company's goals, saving time and money, gaining internal satisfaction, attracting and retaining audiences, gaining reputation, and improving the quality of communication.

Nowadays, with the expansion of globalization, increasing competition, the entry of various domestic and foreign companies, various products and advances in technology, maintaining customer satisfaction and loyalty has become difficult. One of the hallmarks of successful companies today is their competitiveness. The main purpose of this study is structural-interpretive modeling of industrial brand competitiveness in the petrochemical industry. This research is qualitative-quantitative mixed exploratory research. The statistical population in the qualitative part of the research includes faculty members and experts in the field of industrial management, marketing and industrial brand, professors familiar with the subject of research and managers and deputies with experience in petrochemical companies in the country using 16 snowball sampling method were chosen.  In a small part, the statistical community includes personnel (managers, deputies and experts) of the marketing and sales department of petrochemical companies in the country. For sampling, due to the small size of the statistical population and the irreversibility of the questionnaires has been used the whole number and the whole population has been considered as a sample in a small part (N = 255). The research tool in the qualitative part of the interview is semi-structured and in the quantitative part the researcher has made a questionnaire. For data analysis in the qualitative part, fuzzy Delphi theme and technique analysis has been used and in the quantitative part, ISM technique has been used for data analysis. In the qualitative part of the research, a total of 14 variables were identified as factors affecting the competitiveness of the industrial brand. These 14 factors are: Technological opportunism, Brand strength, brand differentiation, Commercialization of innovation, Strategic entrepreneurship, Exploratory marketing, Innovative marketing, Brand charm, Strategic knowledge management, customer relation management, Brand management system, Strategic intelligence and strategic pricing