Mazyar Ghadiri Nejad

Flexible robotic cells are utilized to produce standardized products at a high speed production rate, and to set up the production floors based on rapid operating environment changes. In FRC, there are a number of computer numerical control machines, an input buffer, an output buffer, and a robot. The input and output buffers contain unprocessed and finished products, respectively, whereas the robot performs the loading and unloading activities and transports the items among buffers and machines. The system repeats a cyclic schedule in its run and the cycle time depends on the order of robot activities. In order to maximize the efficiency of the system, the order of the robot activities yielding the minimum cycle time should be determined. The aim of this research is to find novel exact models to solve this cycling scheduling problem. These types of problems have tight relations with traveling salesman problem. In this study, four main modeling approaches of the TSP, such as Dantzig-Fulkerson-Johnson approach, Miller-Tucker-Zemlin approach, Vajda's n-step approach, and the network flow approach are studied. Only the DFJ approach cannot be adapted to the scheduling problem. The three other approaches are adapted successfully. Similarities and differences are discussed by using several numerical examples.

In recent years, unquestionable warnings like the negative effects of CO₂ emissions, the necessity of utilizing sustainable energy sources, and the rising demand for municipal electrification have been issued. Therefore, users are encouraged to provide off-grid and sustainable energy systems for their own homes and businesses, especially if they are located rurally and far from grids. Hence, this study aims to design an off-grid hybrid energy system, in order to minimize both the baseline cost of energy and the net current expenditure in the desired system. To construct such a system, wind generators (WG), photovoltaic arrays (PV), battery banks, and bi-directional converters are considered in the real case of a supermarket with a 20-year lifespan in Malmö, Sweden. Some significant assumptions, such as the usage of renewable energy resources only, electricity production close to the business location, and a maximum allowance of 0.1% unmet are incorporated. To optimize the considered problem, a particle swarm optimization (PSO) approach as developed to provide the load requirements and establish the number of WGs, PVs, and other equipment. Moreover, to verify the obtained results, the developed system was simulated using HOMER Pro software, and the results are compared and discussed. The results indicated that the designed hybrid energy system is able to perform completely off-grid, while satisfying 99.9% of the yearly electricity demand. The best results obtained by the proposed PSO offered 160, 5, and 350 PVs, WGs, and batteries, respectively, while the best solution found by the simulation method was the use of 384 PVs, 5 WGs, and 189 batteries for the considered off-grid system. This study contributes to decentralized local electrification by utilizing renewable energy sources that have the potential to revolutionize green energy solutions.

Multi-objective task graph scheduling is a well-known NP-hard problem that plays a significant role in heterogeneous distributed systems. The solution to the problem is expected to optimize all scheduling objectives. Pretty large state-of-the-art algorithms exist in the literature that mostly apply different metaheuristics for solving the problem. This study proposes a new hybrid algorithm comprising an improved multi-objective differential evolution algorithm (DE) and Pareto-front neighborhood search to solve the problem. The novelty of the proposed hybrid method is achieved by improving DE and hybridizing it with the neighborhood search method. The proposed method improves the performance of differential evolution by applying appropriate solution representation as well as effective selection, crossover, and mutation operators. Likewise, the neighborhood search algorithm is applied to improve the extracted Pareto-front and speed up the evolution process. The effectiveness and performance of the developed method are assessed over well-known test problems collected from the related literature. Meanwhile, the values of spacing and hyper-volume metrics are calculated. Moreover, the Wilcoxon signed method is applied to carry out pairwise statistical tests over the obtained results. The obtained results for the makespan, reliability, and flow-time of 50, 18, and 41, respectively, by the proposed hybrid algorithm in the study confirmed that the developed algorithm outperforms all proposed methods considering the performance and quality of objective values.

In recent years, unquestionable warnings like the negative impacts of CO2 emissions, the necessity of utilizing sustainable energy sources, and the rising demand for municipal electrification have been issued. In this study, by incorporating two significant assumptions, such as electricity production in close proximity to the business location and only renewable energy resource usage, a modest off-grid hybrid energy system is designed. To construct the system, a number of elements such as wind generators (WG), photovoltaic arrays (PV), battery banks, and bi-directional converters are taken into account. Moreover, a real case in Malmö, Sweden, is considered. To optimize the system, a bi-objective problem is developed, and it is solved by proposing a particle swarm optimization (PSO) approach to provide the load requirements (with a maximum allowance of 0.1% unmet) for a nearby supermarket (approximately 1000 m2). Moreover, to verify the obtained results, the developed system is simulated using HOMER Pro software, and the results are compared and discussed. The contribution of this study is to provide off-grid or local clients around the world with a dependable and affordable option by minimizing both the baseline cost of energy and the net current expenditure in the desired system. The best-obtained results by the proposed PSO offered 160 PVs, 5 WGs, and 350 batteries, respectively, while the best solution found by the simulation method was using 384 PVs, 5 WGs, and 189 batteries for the considered off-grid system.

Recently, the relationship between green human resource management (GHRM) and environmental performance has received a lot of attention from scholars. Teaching, training, and research and development carried out in higher education institutions, which are crucial sources for the promotion of sustainability, encourage GHRM. Using signaling theory, this study aimed to deal with the different roles of green training in the Ministry of Education’s corporate environmental performance. The mediation analysis of organizational citizenship behavior towards the environment was considered and the moderation role of perceived organizational support was evaluated. A survey was prepared to analyze the opinions of managers and staff of the Ministry of Education in Iran. After collecting the surveys, 211 complete responses were analyzed and the most important results from these surveys concluded that: (1) the important tools in adopted strategies for green training improve organizational citizenship behavior towards the environment (OCB) and the Ministry of Educations’ environmental performance; (2) the role of OCB in mediating the effects of training on corporate environmental performance (CEP) is essential; (3) perceived organization support (POS) has a moderation role between green training and CEP.

Nowadays, sustainability is one of the key elements which should be considered in energy systems. Such systems are essential in any manufacturing system to supply the energy requirements of those systems. To optimize the energy consumption of any manufacturing system, various applications have been developed in the literature, with a number of pros and cons. In addition, in the majority of such applications, multi-objective optimization (MOO) plays an outstanding role. In related studies, the MOO strategy has been mainly used to maximize the performance and minimize the total cost of a trigeneration system with an HCCI (homogeneous charge compression ignition) engine as a prime mover based on the NSGA-II (non-dominated sorting genetic algorithm-II) algorithm. The current study introduces a novel multi-heuristic system (MHS) that serves as a metaheuristics cooperation platform for selecting the best design parameters. The MHS operates on a proposed strategy and prefers short runs of various metaheuristics to a single long run of a metaheuristic. The proposed MHS consists of four multi-objective metaheuristics collaborating to work on a common population of solutions. The optimization aims to maximize the exergy efficiency and minimize the total system cost. By utilizing four local archives and one global archive, the system optimizes these two objective functions. The idea behind the proposed MHS is that metaheuristics will be able to compensate for each other’s shortcomings in terms of extracting the most promising regions of the search space. Comparing the findings of the developed MHS shows that implementing the suggested strategy decreases the total unit costs of the system products to 25.85 USD/GJ, where the total unit cost of the base system was 28.89 USD/GJ, and the exergy efficiency of the system is increased up to 39.37%, while this efficiency was 22.81% in the base system. The finding illustrates significant improvements in system results and proves the high performance of the proposed method.

One of the fundamental areas of the tourism industry that has a significant impact on the economy of the host country is medical tourism. Many nations strive to establish a reputation both domestically and abroad. There is intense competition among nations to entice medical tourists, yet the motivating factors and purchasing patterns may be very different for different cultures. Healthcare decision-makers must employ efficient marketing strategies to secure a sustainable market share given the international rise of medical tourism. By interviewing the managers and patients of international hospitals, the current study aims to determine the potential growth of medical services with the idea of medical tourism in Iran. In addition, the basic indicators and significant categories are further distinguished by examining the key stakeholders and the perspectives of international patients toward medical tourism activities in Iran. To the best of our knowledge, the main emphasis in earlier studies was only on time, cost, and quality, although other indicators are needed for stakeholders to be investigated in this area. Despite Iran’s rich natural, historical, and cultural heritage, a long history of political unrest and conflicts between reformists and fundamentalists regarding the benefits of tourism has hurt the industry’s growth. The findings of this study, including the eight categories provided, make medical tourists aware of the cutting-edge technologies being used in the Iranian healthcare sector. It also includes tourist attractions that could be used in future research by employing multicriteria decision-making techniques to identify the most crucial ones for each nation.

Emphasizing the need to provide a coordinated flood management strategy in the country and avoid acting in an isolated way when it occurs, improving the attitude of flood control during floods, and controlling floods with comprehensive analysis are among the main purposes of the current study. In this study, the environment’s physical, technological, social, economic, and political characteristics are considered to assess the urban flood risk. This study entails a discussion of flood hazard control in Benin City, the capital city of Edo State of Nigeria. The research methodology involves employing both interviews and questionnaire distribution. First, three key persons involved in flood control are interviewed at the State Emergency Management Agency (SEMA), which coordinates disaster risk reduction in the state, and then, the responses are classified into themes that are used to prepare the questionnaire to be distributed in four main regions. The questionnaires are distributed to the employees in institutions concerned with flood control and to the general population living in the region. Based on the obtained results from the interviews, some methods that could be applicable in controlling flooding in the region are listed. The results showed that the most important ones are flood warning systems, flood prevention through using the building resilient infrastructure and community programs, protection and mitigation through using natural processes, and strategic implementation of a flood emergency plan through sustainability.

To characterize the process of lead-time reduction in EPC project-based organizations, including the engineering, procurement, and construction activities, various elements such as organizational issues, firm resources, used methods and techniques, and performance measuring tools are important. In this study, the four mentioned elements are evaluated through a survey in different EPC project-based organizations. The survey is sent to only experts and managers who are involved in EPC projects, and the obtained data is used to test the relationship between the mentioned variables and the lead time of developing new products. This evaluation is followed by a set of generalized lessons learned and courses of action to improve the development process. Additionally, the attributes of performance measurement in such projects are scrutinized. Therefore, to analyze the impacts of the mentioned variables and their variations on the performance measurement, a mathematical model based on the fuzzy approach is developed. The results show that all of the four considered variables are correlated to the lead time and affect the quality perceived by the main client and the cost of the project. Moreover, data obtained from the proposed mathematical formulations illustrated that the fuzzy modeling approach is an effective method to predict the performance measurement level when the levels of the input variables are given.