Industrial Engineering in the Big Data Era

Delivering the projects on time and in accordance with the customer requirements is a crucial process for almost all software companies due to the budget and schedule constraints. Effective time planning provides optimum usage of all resources (i.e., people, time, budget, etc.). This study presents a new integrated decision support methodology for planning software projects. For this purpose, we identify the most important factors by expert judgments and literature review, find priorities of factors by Hesitant Fuzzy Linguistic Term Pairwise Comparison, and estimate time effort (duration) for the projects, respectively. Subsequently, we develop a hybrid metaheuristic by using the priorities of factors and estimated time efforts of the projects. As an experimental study, we apply this methodology to determine time planning of software projects in a Turkish company. We analyze that the proposed methodology gives very efficient plans with less delayed projects and higher award in comparison with the initial solutions.

Energy is a vital necessity that ensures the continuity of life. It is also important to ensure the existence and continuity of the energy. Solar is the most important source of energy among renewable energy sources that are being developed as an alternative to fossil fuels that are consuming. This study develops models for the evaluation of solar energy systems and allows calculation of radiation values in the sloped surface for isotropic and anisotropic sky conditions. In literature, the effects of extraterrestrial, atmospheric, and terrestrial uncertainties are usually ignored. In the proposed fuzzy models, these uncertainties inherit in the solar energy production capacity are considered. These newly developed isotropic and anisotropic fuzzy models help to determine the most appropriate solar energy system by providing more realistic calculations.

Inventory routing problems (IRPs) have been one of the most important problems in the last thirty years and include inventory management, vehicle routes and distribution sub problems. Several IRPs have been implemented in various sectors. Maritime inventory routing problem (MIRP) has also been tackled widely. The problem includes the distribution of products and holding the inventory levels between upper and lower limits. In this study, MIRP for the distribution of containers considering available inventory levels aiming minimum total cost has been proposed. Distribution amount and the routes under the constraints of routing and inventory levels have been decided. The model is proposed for deciding both optimal routes of the ships and optimum inventory levels. An integer programming approach for the problem has been proposed and solved using GAMS software.

Container transportation problems include logistics activities conducted in both terminals and hinterland of the terminal. Transferring containers considering cost, time and environmental issues is a difficult problem. Hinterland container transportation (HCT) is defined as the movement of the containers between terminals and customers by different transportation modes. Processes of the hinterland transportation are the most important cost factors for door-to-door services. These problems aim to decide the distribution of the containers considering the cost and time minimization. In this study, a container distribution, routing problem has been proposed in the hinterland. The problem aims to perform the distribution of the containers at minimum total traveling distance. Allocation of containers and the routes under the constraints of routing, capacity is investigated in this study. An integer programming approach has been proposed and solved.

Lean six sigma methodology offers a broad range of assessments and implementation services to meet the demands and challenges organizations face in today’s global marketplace, where improved processes and unobstructed flow are essential elements for reducing costs and maintaining a competitive advantage. So that organizations can maximize profits and increase business value by providing knowledge and guidance on implementing continuous improvement, culture change, methodologies, and tools. From this standpoint, the purpose of this study is to analyze and enhance improvements for the non-value adding processes in airline ground handling operations via lean six sigma methodology and utilization of the appropriate tools.

The education system is one of the most important factors in achieving the level of developed countries. Higher education institutions have a very important place among educational institutions since they lead to the future. For this reason, the way to improve the development level of countries is to increase the quality of education in higher education institutions. For this purpose, in this study, the Quality Function Deployment (QFD) approach is applied to the educational curriculum in Gaziantep Industrial Engineering Department and the results are analyzed.

Job shop scheduling for labor-intensive and project type manufacturing is a too hard task because the operation times are not known before production and change according to the orders’ technical specifications. In this paper, a case study is presented for scheduling a labor-intensive and project type workshop. The aim is to minimize the makespan of the orders. For this purpose, the artificial bee colony algorithm (ABC) is used to determine the entry sequence of the waiting orders to the workshop and dispatching to the stations. 18 different orders and 6 welding stations are used for the scheduling in this case. The input data of the algorithm are the technical specifications (such as weight and width of the demanded orders) and processing times of the orders which vary according to the design criteria demanded by the customers. According to the experimental results, it is observed that the ABC algorithm has reduced the makespan.

Public transportation decisions are an important aspect of management of cities. Especially, for the small and midsized cities, bus transportation is more important due to lack of rail transportation. The goal of this paper is to present determination of public transportation vehicle from the municipal corporation managers’ perspective by using analytic hierarchy process (AHP). In our analysis, we use 4 main criteria and 9 sub-criteria that are indicated by the experts for the selection of the most suitable bus considering sustainability conditions. A survey is designed about vehicles with internal combustion engines and electric motors that enables each expert to compare the relative priority of each criterion with other criteria. A real world application of a municipal corporation is conducted to illustrate the utilization of the model. The results presented in this study highlight the necessity to integrate analytic and comprehensive decision-making process into public transportation decisions.

Effective material distribution is a vital issue to maintain the assembly lines’ operations. So, coordination of the material supply to the assembly lines requires a system design that minimizes total material handling, inventory holding costs and prevents parts shortage. This is called the multi-commodity multi-vehicle material supply system design problem. To solve this, first, a Single-Vehicle Milk-run Mathematical Model is proposed. Then, a Matheuristic that iteratively employs the proposed model is developed to design a multi-vehicle in-plant milk-run system. The proposed methodology is validated by designing the milk-run system of a real washing machine assembly plant.

In this study, a nonlinear optimization model is proposed to determine the constraint propagation (CP) of qualitative constraint sets to minimize search backtracking points. The model gives answers to the questions of what the optimal sequence is in the case that there is a set of variables with known values and, alternatively, what variable sequence is optimal to be able to have an optimal value propagation (what variable values should be known to have optimum variable sequence). In order to improve the solution performance, a constraint activation analysis is initiated for the constraints that are defined for the variables with known values by sign algebraic Karush-Kuhn-Tucker conditions. The optimization model and the qualitative activity analysis carried out can be applied to any constraint propagation problem where the variables have a limited set of values.

Copula is a distribution function on the unit hypercube with uniform margins. The margin is directly related to the stochastic behaviour of one variable, while joint distribution function covers the holistic character of more. In multivariate (and particularly bivariate) analysis, using copulas is an elegant way to solve the missing information problem between joint distribution function and the total of the margins. Hereby, the intention of this paper is twofold. Firstly, the paper intends to emphasize the advantages of copulas in practice. In order to encourage potential researchers to diversify their subject of work with these functions, authors give the essential introductory details for a clear understanding of copulas associated with their basic mathematical and statistical preliminaries. Secondly, the study exemplifies the practical usage of copulas in statistical process control area. In this context, process parameters are estimated in order to calculate the control limits of a typical Shewhart type control chart. Parameter estimation is performed by Maximum Likelihood Estimation (MLE) for the bivariate Clayton copula in univariate AR (1) time series with several different levels of high dependence. Since monitoring autocorrelated data in control charts is known as being one of the main causes of producing tighter control limits than required, false alarm rate may be increased and accordingly, the performance of control charts may be dramatically decreased. This study shows that copulas may alternatively be used for getting the same or little wider acceptable region between upper and lower limits. This recognition of the properness of copulas may help to decrease some of the negative effects of dependent data being monitored on charts for further studies.

In order to survive in a competitive environment, companies are required to increase the productivity by identifying the factors that affect the failure rates. In this study, fault rates of a pipe manufacturing company are investigated. Therefore, based on data attributes such as demographic characteristics of employees, employee training, physical working conditions, social facilities etc. were gathered. Briefly, data were analyzed by association rules, correlation, and various classification algorithms. The association rules, measurements, and correlations of attributes are examined to understand the current situation. Then, based on this information, various classification algorithms have been used for estimation. The higher accuracy of the prediction, the greater results will occur. Therefore, the accuracy of the classification algorithms is compared and the algorithm with the highest performance is achieved.

In recent years, with the increase of world trade size, the importance of the Air Cargo operations has increased even more. The rapid progression of air cargo transportation has caused development of intralogistics systems, the establishment of new facilities, the installation of new material handling equipment and facilities layout design issues. Although it is possible to reduce the system costs and increase the total cargo handling capacity with the facility layout planning (FLP) algorithms; it has been observed that the FLP algorithms have not been used in the airway cargo facilities designs. In this study, the air cargo facility design issue has been tackled as layout problem. Firstly, the existing layout algorithms in the literature have been addressed and then, FLPs have been taken into the consideration with the BlocPlan layout construction that is integrated with the Ant Colony Optimization (ACO) algorithm. In the application part of the study, the data of a major air cargo operator in Istanbul Airport data have been used and the transportation costs have been decreased with the proposed integrated FLP algorithm.

Quality control in multivariate manufacturing processes should be applied with multi variate control charts. Although this method is sufficient, it doesn’t include the causes of uncontrolled situations. It only shows samples that are out of control. A variety of methods are required to determine the root cause(s) of the uncontrolled situations. In this study, a classification model, based on the ensemble approach of machine learning classification algorithms, is proposed for determining the root cause(s). Algorithms are compared according to predictive accuracy, kappa value and root square mean error rates as performance criteria. Results show that Neural Network ensemble techniques are more efficient and successful than individual Neural Network learning algorithms.

In this study, inventory and production control of a group of products from a major manufacturer of domestic and industrial gas meters is examined. ABC and XYZ analyses are carried out for the inventory items to determine the production strategy of each item class and the Economic Order Quantity (EOQ). After this examination, one of the end products of the company is chosen to develop the Materials Requirement Plan (MRP) for. The Bill of Materials (BOM) for the chosen product is created and MRP is developed according to the BOM levels. The monthly demand data for the final product is obtained based on the annual demand and the required quantities for all sub materials of the final product are calculated with MRP. Finally, after the ABC/XYZ analysis, BOM structuring, and MRP calculations, a user interface is developed in Excel using Visual Basic for Applications (VBA) to access, edit, and add the desired information easily.

Quality greatly affects both customer satisfaction and the performance of a product or service. Therefore, due to competitive market conditions, the importance of quality measurement has increased. In reality, measuring quality is not an easy task, especially in the service sector, due to the heterogeneous, inseparable and incomprehensible characteristics of service products. Most service sector products are intangible. In the field of aviation, the quality of care directly affects aviation safety. This increases the importance of measuring and improving service quality in aviation. In this study, fuzzy analytical hierarchy process approach was used for measurements. In the hierarchical structure, 3 main criteria, 6 first level sub-criteria and 17 s level sub-criteria were used for quality measurement in airline maintenance service. The surveys were answered by experts working for maintenance companies. The final maintenance quality results were converted to a letter scale and used for service quality improvement.

The concept of industry 4.0 is a critical topic that has been addressed by many studies recently as well as the business community. However, there are not many studies on the risk assessment of industry 4.0 transition process. In this paper, it is aimed to identify the risks that companies may face in the industry 4.0 transition process and to suggest a methodology for prioritization of these risks. We applied to expert opinions to address all numerical and verbal factors and used a fuzzy multicriteria decision-making (MCDM) methodology in order to determine the most and the least critical risks. For this aim, hesitant fuzzy sets (HFSs) and interval type-2 fuzzy sets (IT2FSs) have been utilized together to obtain the best results that are closer to the reality. Finally, risks have been prioritized for companies in the transition process to Industry 4.0.

Recent technological advances enabled companies to collect, store and process a large amount of data. Automated collection of human behavior is one of the recent developments in data collection field. Companies can analyze the behaviors of their customers and get insight into their needs by using automated collection technology. In this study, we analyze location-based services data collected from a major shopping mall in İstanbul. The data is composed of 293 locations and 12070 unique visitors. The results show the most frequent routes that users follow during different periods.

We propose a sampling-based method to estimate the expected cost of a given strategy that evaluates a given Boolean function. In general, computing the exact expected cost of a strategy that evaluates a Boolean function obtained by some algorithm may take exponential time. Consequently, it may not be possible to assess the quality of the solutions obtained by different algorithms in an efficient manner. We demonstrate the effectiveness of the estimation method in random instances for algorithms developed for certain functions where the expected cost can be computed in polynomial time. We show that the absolute percentage errors are very small even for samples of moderate size. We propose that in order to compare strategies obtained by different algorithms, it is practically sufficient to compare the estimates when the exact computation of the expected cost is not possible.

The purpose of this study is to investigate the effect of the dimensions of organizational justice over the perception of general justice, specifically concerning the architects and the civil engineers working in the construction sector. The study firstly defines organizational justice and the dimensions of this concept. Afterwards, the study model used that is used in the research is constructed. The data was received from a data collection form that was prepared based on similar studies in the literature. A total of 313 subjects participated in the study: 157 civil engineers and 156 architects. The analysis has shown that the dimensions of organizational justice (distributive justice, procedural justice, and interactional justice) have a positive effect on the perception of general justice. The results of the study provide important data for the construction firms who aim to create a sense of organizational justice among their employees.

With the increased volume of environmental studies, hybrid vehicle routing and recharging stations location problem for electric vehicles have become more important. The aim of this paper is to review the literature on hybrid vehicle routing problem from 2000 to latest researches in order to identify the current research and to provide direction for future research in this field. Researches are classified considering the research publication year and research fields. Research gaps are identified for future research areas.

Electricity load forecasting and planning have vital importance for suppliers as well as other stakeholders in the industry. Forecasting and planning are relevant issues that they provide feedback to each other to increase the efficiency of management. Accurate predictions lead to more efficient planning. Many methods are used for electricity load forecasting depending on characteristics of the system such as stationariness, non-linearity, and heteroscedasticity of data. On the other hand, in electricity load forecasting, forecasting horizons are important issues for modeling time series. In general, forecasting horizons are classified into 3 categories; long-term, mid-term and short-term load forecasting. In this paper, we dealt with short-term electricity load forecasting for Istanbul, Turkey. We utilized one of the efficient nonlinear dynamic system identification tools to make one-step ahead prediction of hourly electricity loads in Istanbul. In the final, the obtained results were discussed.

Aggregate production planning (APP) can be considered as a great picture of the planning process. Rather than focusing on individual products or services, APP focuses on total or collective capacity. Therefore, it has a very important place in production and operation management functions. In the literature, different kind of methods has been proposed for the solution of APP problems. In some situations where the cost and demand parameters cannot be defined as crisp values due to the environment of the problems, fuzzy logic is used to handle the imprecise data. This paper provides a fuzzy optimization approach for aggregate production planning problems. After given information about fuzzy linear programming and solution approaches, a case study in a beverage industry is carried out. The results are analyzed using different a-cut values.

Talent management has become an increasingly popular area in the human resource management. Over the past decade, talent management has gained importance and become an effective tool for organizations that want to have a competitive advantage and achieve maximum organizational performance. For a successful talent management process, organizations need to identify talent attributes or in other words characteristics that a talented employee should have. The main purpose of this study is to define talent attributes in the automotive industry in Turkey through a qualitative research. Within the scope of the study, in-depth interviews were carried out with the participation of 20 employees who works in different companies that operates in the automotive industry in Turkey. 29 talent attributes were identified as a result of face-to-face in-depth interviews.

Effective energy management is the key to achieving national objectives and sustainability goals for governments depending on international policies. A reasonable energy management should be cost-effective, environmentally sensitive and aim to optimize the use of available resources. Fossil-based resources are not enough to meet the rapidly growing energy need. Some urgent measures should be taken against the rapid depletion of fossil fuels. In this context, it is necessary to investigate alternative energy sources. Renewable energy sources are seen as one of the most important alternative energy sources. The geographical location of Turkey provides a great advantage in renewable energy sources. However, there are some obstacles to the use of renewable energies such as market structure, political and legal regulations, the intermittent nature of renewable resources and the financial burden on technological investments. A proper energy policy should be developed considering all these factors. Research and development activities continue for new alternative energy sources. Given the diversity in the energy portfolio, new trends for alternative sources such as nuclear power plant, shale gas, wave and tidal energy are expanding. This study proposes an appropriate policy for effective energy management using some statistical tools and evaluates the current situation to reveal the future energy situation.

Enterprise resource planning (ERP) is an integrated management system that aims to bring together all the data and processes of an organization. There are many factors influencing the use of ERP systems. The purpose of this study is to analyze a variety of factors that affect end-users’ behavioral intention to use ERP implementation based on the technology acceptance model (TAM). Besides the basic constructs of TAM, we determined other constructs such as consultant support and user guidance. The data was collected from end-users who used or have been using an ERP system in the companies. A total of 136 responses were obtained. SmartPLS software was used for the data analysis and testing of the validity of the hypotheses. The results show that perceived usefulness affect behavioral intention to use an ERP system, while perceived ease of use is not a significant determinant of ERP system usage. Moreover, both perceived ease of use and user guidance affect perceived usefulness and consultant support affect perceived ease of use.

This study aims to analyze the impact of university service quality on academic motivation and school engagement as well as the impact of school engagement on academic motivation. In order to analyze the structural model, not only hypotheses about the causal relationships but also indicators operationalizing the concepts have been proposed. Data was collected by means of an online questionnaire, applied to students in private and state universities, and analyzed using structural equation modelling based on partial least squares. According to the findings, academic aspects and physical characteristics among the service quality dimensions are the most important ones explaining the variation in school engagement perception. The results also show that school engagement has a strong significant impact on academic motivation.

In the era of social media, big data and Industry 4.0, technology has to make more contributions to help nimble decision making in response to severe disasters, both natural (including climate-related extreme events) and manmade, by providing the right solutions. Different reports and experiences originating from recent disasters and their crisis management processes have highlighted the need for a resilient and innovative disaster decision support system, even at the modern, developed and well-equipped communities working upon real-time big data. The aim of this paper is to propose a tool to foster preparedness, response, recovery, and mitigation as the fundamental steps of catastrophe management via innovation for disaster-resilient societies. The proposed tool consists of a novel conceptual hybridization of virtual experiments, machine learning, block chain, and database management technologies to overcome limitations of currently used technologies. This tool will utilize innovation in information registration and distribution, data exploration and discovery for generating reliable solutions. The most impressive implications of the proposed technology are its ability to measure community sentiments, generate smartly-designed hazard scenarios and propose the best emergency evacuation plot on 3D notifications as an innovative distinct feature.

Since the advent, blockchain has found its application in numerous industries, thus disrupting the current way of design and development of the new applications for supply chain management and logistics. Nowadays for business providers, the efficiency of the service they provide is crucial for the long-term improvement of their operations. This efficiency depends on consumer satisfaction with service delivery and reliable information related to goods, correct delivery and timeliness. These providers are strongly dependent on the application and models they use for planning and managing their daily activities. In this context, information sharing is crucial to ensure a reliable and efficient way of collaborating. This papers aims at surveying the current range of academic literature and applications from the business perspective related to the application of blockchain technology in supply chain management and logistics.

The topic of data mining is a popular subject, especially nowadays. Data mining is a process which accesses the information among large-scale data and mine the knowledge. The most widespread use in the literature is to process large amounts of data automatically or semi-automatically to find meaningful patterns. Depending on the pace of the spread of Internet usage, digital media takes the place of traditional media, so the number of textual forms in digital media is increasing day by day. For this reason, text mining techniques should be used for text review. Such as text mining, data mining, machine learning technologies are related to the big data concept, and these technologies are used for increasing productivity in too many areas. According to the analysis in the United Kingdom and the United States, companies adopting decision-making based on data have been observed to be 5–10% higher in output and productivity than firms using only information technology components such as software products. So, even if small and medium enterprises can adopt these technologies in their life cycle can gain more productivity and economic profit in their areas.

Airline industry has many challenges: decreasing costs, responding changeable demand, achieving high quality requirements as well trying to maintain superior services and satisfy customer needs. Turkish aviation industry presents a remarkable growth during the recent years. Besides the strategic role of the geographical position of Turkey, when the new airport started to service in İstanbul, Turkey’s importance in the world aviation sector will increase further. Customer satisfaction is crucial to increasing the profitability of airline companies as the aviation industry grows. Turkey centered airplane companies have flight more than 120 countries and 300 cities around the world. Previous studies have investigated customers’ perception of service quality and the effect of customer satisfaction levels on their future behavior, and various strategies for achieving customer satisfaction and customer loyalty. According to J.D. Power 2017 report, customer satisfaction with airlines has been rising for the past five years. So companies should understand customer satisfaction elements well to sustain their competitive advantage. This study looks at how online, cabin, flight services, and personnel characteristics could affect customer satisfaction of an airplane company centered in Turkey. Data were collected from the more than 1400 passengers, domestic and mainly international flights. Relations in the model is tested with Structural Equation Modelling using IBM SPSS Amos package.

The mobile application market is expanding with the diversity in mobile devices, and competition among the mobile application developers becomes fierce. Usability of the mobile applications is crucial to gain a competitive advantage under these circumstances. This study aims to reveal the difference in terms of usability of four of the commonly used mobile applications (WhatsApp, Facebook, YouTube, and Mail). Furthermore, this study investigates the difference in terms of usability between iOS and Android operating systems. To measure the usability of the mobile applications, a System Usability Scale (SUS) with an adjective rating scale is applied to the young 222 participants, using the applications on their mobile phones. The result of the study shows that usability of all applications is satisfactory and above the standards. The comparison of mobile applications with each other shows that, WhatsApp has the highest usability score, whereas Facebook has the lowest one. In addition, according to the results, there is no significant difference between operating systems in terms of the usability of mobile applications.

The purpose of this study is to examine the factors affecting the intention to use big data tools, using an extended technology acceptance model. The model includes job relevance, big data dimensions, compatibility, self-efficacy, complexity, and anxiety. The study was conducted on a Turkish airline company, and data were gathered from its employees through an online survey. A total of 252 questionnaires were collected. The results show that behavioral intention to use big data technology is explained by perceived usefulness and perceived ease of use. Of these, perceived usefulness has a higher direct influence on behavioral intention to use big data tools. Another result of this study is that perceived usefulness is explained by perceived ease of use, job relevance, compatibility, and big data dimensions, where big data dimensions have a higher direct influence on perceived usefulness. The final result is that perceived ease of use is explained by self-efficacy and anxiety. Of these two factors, self-efficacy has a higher direct impact on the perceived ease of use.

National level innovation has been studied prominently. Global indices are utilized while evaluating country level innovation. Global Innovation Index (GII) is one of the most commonly used indices in this context. Moreover, cultural dynamics also affect innovation level. Hofstede’s Cultural Dimensions (HCD) is one of the outstanding guidelines on cultural dynamics of countries. In this study, infrastructure, institutions, and human capital and research are designated input indicators of GII while knowledge and technology output and creativity output are chosen output indicators of GII. Moreover, indulgence is considered as a moderator variable from HCD. Our main aim is to examine the relationships among global innovation index factors and investigate the moderating effect of indulgence on these relationships. For this purpose, we proposed a conceptual model to explore these relationships. Structural Equation Modeling (SEM) was employed in order to conduct path analysis with data from official web sites. The results show that all hypotheses related to GII factors are supported, and a moderating effect of indulgence is observed on some of the relationships. These findings indicate that countries with sufficient innovation input make the transformation to innovation outputs. Furthermore, innovation leaders should be aware of societies have more indulgent score moderate several relationships.

The objective of this research is to assess the attitudes and preferences of female patients living in rural areas regarding various functionalities a mobile app can play. I classified mobile app functionalities into two major categories of static and dynamic. Static functionalities are those with whom a patient develops one-way, top-down interactions, such as receiving drug information from hospital staffs. Interactive functionalities develop mutual and engaging interactions among patients, physicians, and health staffs. This is a descriptive, cross-sectional study collected data from 460 female patients visited rural “Health Houses” in provinces of Ardabil and Isfahan. The respondents were selected randomly. The data collection tool is a questionnaire designed by the author. Validity (content and form) and reliability (Spearman–Brown with r = 0.83) includes two categories of questions: background and questions regarding the role of mobile information-communication apps. Data were analyzed by SPSS software. This research shows that the patients prefer a mobile health app that develops a static interaction between themselves and their physician or other health staffs. In general, patients have medium trust to mobile apps, and they prefer to use mobile apps developed and run by a clinic or a hospital so that they can receive health information, medical bills and the results of their medical tests. They showed little tendency to interact with other patients, physicians, and health staffs through a mobile app. They prefer face to face interactions.

Humanitarian supply chain management (HSCM) has gained popularity in recent years in research fields. The aim of this paper is to review the literature on humanitarian operations and crisis/disaster management from 2010 to the latest researches, in order to identify the current research and to provide direction for future research in this growing field. Studies are classified considering the research publication year and research fields. Articles from humanitarian supply chain management were reviewed, and keywords were identified within a disaster management lifecycle framework. Research gaps are identified for future research areas.

Green hospitals and Lean Healthcare Systems are both dealing with increasing efficiency and effectiveness by decreasing waste/non-value added activities and cost in healthcare institutions. When both of the issues are used and applied, it can be seen that green hospitals will encourage more effective and efficient usage of energy, water and material currently used, ensure the prevention of any kind of waste, perform environmentally sensible and eco-friendly building design and be environmentally friendly in the process of service provision. In addition, lean heathcare will decrease waste, costs, non-value added activities, increase patient, doctor, nurse, and staff satisfaction, decrease waiting times, increase performance and finally increase revenues. The aim of this study is to give information about the concept of the green hospital with a lean healthcare system when applied jointly. It demonstrates the applicability of the concept of green hospitals in the healthcare sector together with lean management. The study examines the contribution of these two concepts to healthcare institutions as well as to the environment. For this purpose, in this study, the concept of green, green healthcare, lean management, and lean healthcare is defined and the joint application of green and lean healthcare is presented as a suggestion. The implementation of environmentally friendly green strategies, together with lean strategies to healthcare within the framework of social responsibility and in this respect extended employees, patient and community awareness can be suggested to both public, university and private hospital managers for developing and improving the sustainable lean healthcare systems.

In healthcare, a number of applications have been applied from high-risk industries to minimise the risk of harm. However, there is little formal evidence to demonstrate the relationships between those applications and their contributions to patient safety. In this study, a correlation analysis was conducted to explore the link between risk management and patient safety incidents in hospitals in NHS England. Findings revealed that hospitals with the highest risk management level report more incidents and demonstrate a statistically significant relationship between risk management and patient safety incidents data. In contrast, hospitals with lower risk management levels do not demonstrate any statistically significant relationships. This study concludes that reporting a higher number of incidents is likely to be as a result of having a better risk management in hospitals, which indicates that a higher number of incidents reported refers to having a better incident reporting culture.

In healthcare, a number of patients experience incidents, where accident models have been used to understand such incidents. However, it has been often traditional accident models used to understand how incidents might occur and how future incidents can be prevented. While other industries also use traditional accident models and built incident investigation techniques based on the traditional models, such models and techniques have been criticised to be insufficient to understand and investigate incidents in complex systems. This paper provides insight into the understanding of patient safety incidents by highlighting the problems with traditional accident models to investigate patient safety incidents, and gives a number of recommendations. We hope that this paper would trigger further discussions on the fundamental concept of the incident investigations in healthcare.

Around 13% of the world’s adult population was obese in 2016 and the prevalence of obesity increased at a significant rate in the last decade (Deitel in Obes Surg 13:329–330, 2003). One of the health consequences of obesity is an increased cancer risk. In this study, we model obesity levels based on BMI, cancer, and death using a Markov decision process model in order to observe the effect of obesity on cancer and mortality risks. The objective of the model is total discounted quality adjusted life years and we simulate an individual’s lifetime from 20 to 70 years by sex. Actions available to the decision makers are no intervention and bariatric surgery. Bariatric surgery is one of the effective clinical prevention methods of obesity and it is particularly recommended for morbidly obese patients. However, it is also associated with increased mortality risk. Our model aims to observe this complex dynamic between obesity, cancer and mortality risks and bariatric surgery. We parametrize the model using randomized clinical trials and published literature and obtain the optimal policy by sex. Our results suggest that obese patients for all obesity levels should undergo bariatric surgery to improve their health outcomes and to decrease cancer risk. This study has the potential to provide guidance to the obese individuals when considering bariatric surgery and it could be further enhanced by the addition of other health outcomes of obesity to the model.

The complexity of the problem grows as multiple individuals involved in the decision making process. Since each individual may have a different experience, attitudes, and knowledge, their approaches might be different from each other on the same problem. Therefore, more comprehensive techniques are needed in group decision making methods in order to determine how much a decision maker’s contribution is considered in the final solution (i.e., the weight of each decision maker). The purpose of this study is to determine the combined weights of decision makers based on both the objective weights, using the geometric cardinal consensus index, and the subjective weights provided by a supervisor. In order to represent the implementation of the method, the study includes a case study in a medical decision making. There are several anesthesia method alternatives to apply; specifically, general anesthesia, local anesthesia, and sedation, which are considered by surgeons. In the case study, the combined relative weights of the medical doctors are derived regarding this issue.