Journal of Industrial Engineering and Management Studies
Volume:8 Issue: 2, Summer-Autumn 2021

Most organizations and companies have hierarchical structures, and appropriate models are required to measure the efficiency of this kind of network systems. Data envelopment analysis (DEA) is a well-known method introduced for measuring the relative efficiency of a set of decision-making units (DMUs) that use multiple inputs to produce multiple outputs. Conventional DEA models usually generate misleading results while evaluating the performance of network systems. The present study aims at developing suitable models for measuring the efficiency of hierarchical structures using the centralized and non-cooperative leader-follower game models. In the proposed method, the divisional efficiencies (within an organization) and the overall efficiency of the organization are calculated. The proposed models are applied to assess the performance of 20 schools in Iran. The results of the two proposed models show that none of the schools are efficient, suggesting that these schools do not optimally utilize their resources. The application of the results of the proposed models enables managers to identify inefficient sub-units and develop strategies to improve their performance.

This paper aims to confront the uncertainties in the flexible job shop scheduling (FJSS) problem by considering the tax regulations of energy consumption and timely delivery. Uncertainties include all unexpected disruptions such as machine breakdowns, modifications or cancellation of the orders, and receiving new orders that lead to failure in initial scheduling. Two strategies with the energy-saving approach have been proposed based on scheduling repair. Two considered objective functions are to minimize the tax cost on surplus energy consumption and to minimize total cost of jobs tardiness. The problem is described with the parameters and decision variables clearly in the form of MIP model. Moreover, the proposed model is investigated using data of a real case study in a company based on casting processes. Since the problem is well known strongly NP-hard, a new approach is introduced based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) to find proper solutions for decision-makers. The computational results show that the proposed model and solution approach repairs properly the original scheduling and could improve the Pareto front comparing with the original scheduling. Due to the result, two proposed strategies could reduce total cost of jobs tardiness more than 47.56% compared with the original scheduling in eight different cases. It could also improve the second objective more than 56.91%. This approach will help the manufacturing industry managers, especially in make-to-order (MTO) systems with high-powered machines to respond rapidly to unexpected disruptions with the lowest energy consumption and tardiness penalty.

In recent decades, researchers are turning to the potential of ABMs to study complex phenomena. Due to intrinsic interconnections, structural interactions and inter-dependencies, individual variations, and communications of various components, supply chain network should be accordingly treated as a complex adaptive system. ABM is dominant tool exploring the emergent behavior of supply chain network with numerous interactive agents. This paper aims to conduct a systematic literature review on the agent-based modeling in the concepts of supply chain and various fields of research. Using reputable databases, combining intended keywords and applying filters based on restrictions and indicators, a total of 123 relevant articles are selected from the valid journals and conferences in year 2010-2019, and 17 subjects in association with supply chain management and 23 subjects related to other fields are presented. Moreover, a brief history and the definition of the three basic areas including complex systems, complex adaptive system and agent-based modeling are provided. The main objective is to provide a perspective based on agent-based modeling and complex adaptive systems for researchers in different sciences and especially supply chain researchers, who are not sufficiently familiar with the philosophy and applications of these approaches.

Quick response to the relief needs right after disasters through efficient emergency logistics distribution is vital to the alleviation of disaster impact in the affected areas. In this paper, by focusing on the distribution of relief commodities after disaster, the best possible allocation for the affected areas is specified and shortest path to vehicle transporting is determined. The objective of the proposed model is the minimization of the maximum distance traveled by each vehicle in order to achieve fairness in response to the wounded. In our proposed model, the location of demand is uncertain and determined by the simulation approach. The proposed approach solves the proposed model and determines appropriate allocation and best route for vehicles according to the allocation, simultaneously. Consequently, using genetic algorithm with two-part chromosome structure in routing and allocation problems. Computational results show the efficiency and effectiveness of the proposed model and algorithm for solving real decision-making problems.

In this study, the problem of simultaneous determination of order acceptance, scheduling and batch delivery considering sequence-dependent setup and capacity constraint has been presented. This problem is a combination of the three problems of order acceptance, scheduling and batch delivery. The most important innovation of this research is the simultaneous optimization of profits and the total weighted earliness and tardiness as two conflicting objectives in the problem of combining order, scheduling and batch delivery. Another innovation of this research is the use of multi-objective Grey Wolf Optimization (GWO) algorithm, which has not been used in studies of this field so far. It has also been shown that the multi-objective Grey Wolf Optimization algorithm is comparable to the exact solution methods. The second part of the numerical results compares the results of the ε-constraint method, NSGA-II and the multi-objective Grey Wolf Optimization algorithm. The results of this section show that by increasing the scale of the problem, the efficiency of the multi- objective Grey Wolf Optimization algorithm is better displayed, and in general, this method has a significant advantage relative to NSGA-II and ε-constraint in terms of DM, SNS and NPS indicators. Also, the solving time of this method is very shorter than that of the ε-constraint. Therefore, from a managerial point of view, a tool called the multi-objective Grey Wolf Optimization algorithm can be used as an efficient tool for supply and production managers, which is able to provide several optimal solutions with different profits, earliness and tardiness.

Considering the role and importance of innovation in the performance of organizations in general and banking institutions in particular, the current work aims at identifying effective factors in the success of innovation management system in Iranian Banks, about which exists a scarcity of research in comprehensively identifying these organizational factors. Having examined several potentially suitable research methodologies, the Grounded Theory is chosen as a suitable approach to determine a comprehensive understanding of the main drivers of innovation management success in Iranian Banks. Theoretical and snowball sampling are used to recruit fifteen participants from across the country. The result of this study is a theory that explains the main drivers of innovation management success in Iranian banks. Innovation supportive leadership, market and customer orientation, information technology management, intellectual opportunities, as well as innovation opportunities and process management are the main factors for innovation management success in Iran’s banking industry. These factors contribute to the common factors mentioned by other studies, including communication, cost, and HR management, and offer a more specific approach to innovation management. Findings can help banks in the evaluation of effective factors in innovation management and provide the necessary ground for designing practices for improvement.

The design of control chart has economic consequences that pure statistical viewpoint does not consider them. The economic-statistical design of control chart, attends not only statistical properties such as average time to signal (ATS) but also economic consequences like hourly expected total cost. The x-bar control chart dominates others if the quality is measured by continuous scale. This paper has considered the economic-statistical design of variable sample size and sampling interval (VSSI) x-bar control chart with multiple assignable causes. Using three sample sizes and three sampling intervals to construct the VSSI x-bar control chart and considering possible combination of design parameters as a decision-making unit, are part of novelty of this research. The problem is formulated as multiple objective decision making (MODM). Also, one capable hybrid meta-heuristic based on genetic algorithm is developed in this research and it was compared with some approaches extracted from the literature and it is found that it can be competitive based on economic and statistics factors.

During the last decade, many researchers have been attracted to study the role of uncertainties in their supply chain designs. Two important uncertainties of a supply chain are demand uncertainty and supply disruption. The basic concept of the proposed model of this paper is based on the newsvendor problem. The model consists of many retailers and many suppliers as two types of autonomous agents that interact with each other considering demand and supply uncertainties. To cope with the uncertainties, retailers have three choices: a forward contract, an option contract, and purchasing from the spot market. Retailers maybe risk sensitive or risk neutral. A new simulation optimization approach is developed to find the best behavior of a risk sensitive retailer in contrast with the other risk neutral retailers during the multiple contract periods. In this model two objectives are defined to find the best behavior of the risk sensitive retailer: the maximization of the profit and the service level. In order to optimize the agent based simulation, an NSGA-II approach is used. The proposed simulation based NSGA-II is further developed in two directions: the one is different realization numbers of the uncertain parameters, and the other is preference points. Under the different preference points and different number of realizations, Pareto optimal solutions are discovered by the collaboration of the agents. Results of the numerical studies showed that adopting more risk averse policies during the contract periods will result in a larger service level and smaller profit rather than adopting more risk taking policies.

Since human societies have endured massive financial disruptions and life losses after the outbreak of the COVID-19 pandemic, it is critical to eliminate this disease as soon as possible. Today, the invention of the COVID-19 vaccine made this objective more reachable. But unfortunately, the suppliant of the vaccines is limited. Hence, to prevent further lethal harms, it seems rational to use a scientific method for vaccine allocation. This study proposes a method for prioritizing the patients based on their level of life-threatening danger according to the proven risk factors (e.g., age, sex, pregnancy, and underlying diseases) of the COVID-19. That is a new data-driven decision-making method for patients’ classification based on their health condition information using several machine learning algorithms. In this method, vaccine applicants are classified into four classes. The scheduling of vaccine distribution would be conducted based on the results of this classification. Furthermore, a real-life case study is also investigated through the proposed method for better illumination in this paper. The vaccine distribution schedule of the real-case study has been performed with 94% accuracy. It should be mentioned that the main achievement of this research is to design a new efficient method for a vaccine distribution schedule.

In the age of a knowledge-based economy, identifying, measuring, and managing the intellectual capital (IC) of organizations has become very significant. These depend on identifying the main components of intellectual capital and their relationships. So far, however, no study has been conducted to clarify the interactions among those components or to develop a model for laying out a hierarchy of IC components. There is, indeed, an urgent need to analyze the behavior of IC components so that the corresponding policies may be successfully implemented. This paper aims to prioritize the IC components based on the identified relationships among the IC components with a focus on the banking industry. A literature review was used to identify the 16 most important IC components. At the first stage, the Interpretive Structural Modeling technique was practiced to determine the interrelationships among these components, based on the data gathered from the Export Development Bank of Iran. The interconnections between the components were clarified. At the second stage, the application of Analytic Network Process for the prioritizing of IC components has been demonstrated. MICMAC analysis and classifying them into four categories including the autonomous, driver, dependent, and linkage components regarding their driving and dependence power is a new effort in the field of IC. A hierarchical structure was proposed through leveling of the components. And finally, the importance and priorities of the components are calculated with the help of the fuzzy analytic network process. The adoption of such an ISM-ANP model of IC components in the banking industry would provide insights for managers, decision-makers and policymakers for a better understanding of these components and to focus on the major components while managing their IC in their organizations.

Since one of the main dimensions of any organization in terms of survival and development is strategy, so the main purpose of this study is to provide a model of alignment of organizational strategies and human resource strategies for this purpose, both qualitative and quantitative methods have been used. The quality section has two sections. In the first part, the quality method of articles from 2014 to 2020 related to the subject were reviewed and evaluated, and the main criteria of strategy, organizational strategy, human resources strategy, strategic alignment and organizational structure were identified. The second part of the qualitative method was based on interviews with experts until the theoretical saturation in the field of the present study was done and coding and analysis was done using MAXQDA software. The main criteria identified based on interviews with experts include organizational communication, employee empowerment, employee attitude evaluation, organizational strategies, human resource strategies, organizational development, social capital, intra-organizational factors, external organizational factors, organizational responsibility and goal setting. It is based on environmental factors that the highest percentage of frequency is related to organizational development and is equal to 20.94% and the lowest value is related to improving the ability of employees equal to 2.6%. In quantitative evaluation, F.DEMATEL, F.AHP and DEA methods have been used. Using F.DEMATEL method, effective and efficient dimensions were identified that the effective factors include organizational communication, employee attitude evaluation, organizational strategies, organizational development, social capital, external environmental factors and organizational responsibility and effective factors include improving employee ability, resource strategies Human, internal factors and goal setting are based on environmental conditions. Then, using F.AHP, the identified variables were prioritized. The first rank among the sub-criteria belongs to the factor of exposure to critical factors with a normal weight of 0.785, and the first rank among the main criteria includes social capital with a normal weight of 0.134. The results obtained from DEA show that exposure to critical factors has the highest efficiency score with a maximum score equal to 1, and finally, based on the results, practical suggestions are presented.

Studies in the field of the tourism industry are often carried out using classical statistics. While the use of spatial statistics in tourism helps a lot in identifying existing models and trends in the industry and discovering them. Due to the interaction between economic, political, environmental and social elements in tourism activities, analysis methods of spatial statistics can be used by identifying information between samples and using large volumes of information by not indicating the independence of the data, can obtain suitable tourism clusters and help identify the appropriate tourism model in Iran. This study aims at to design a model of the tourism industry in Iran with the approach of the spatial correlation structure. The research method was qualitative and quantitative. To identify the variables affecting the tourism industry, the qualitative meta-analysis method, and to collect the required data in spatial statistics, the data of the Cultural Heritage and Tourism Organization in the summer of 2008-2018 have been used. To determine the model of tourism clusters Moran statistics and to study tourism clusters in all provinces of the country, the best interpolation method of tourism has been determined. ArcGIS software was used to analyze the research data. The results of data analysis showed that tourism data has a spatial autocorrelation and a cluster and regular model in the statistical period of summer 2008 to 2018. The most cluster model of tourism using the Moran spatial autocorrelation index is related to the summer of 2008 with 0.991 and the lowest cluster model of tourism is related to the summer of 2014 with the amount of 0.976. Also, the results of the study of the distribution of tourism direction in the provinces of the country in this statistical period showed that the predominant direction of tourism is with a slight change from northwest to southeast.