فهرست مطالب seyed mohammad seyed- hosseini

In this research, the concepts, theories and models on supply chain disruption and its recovery have been investigated. The purpose of this paper is to collect theoretical and applied research for researchers who will tend to research in the future in the field of supply chain disruption and its recovery. After reviewing theoretical studies, the applied research has been studied and the methods and techniques used in these papers have also been examined and its conclusions presented in a separate table comparatively in case.

Economical, environmental, and social issues are significant challenges for industries and governments nowadays. The spare parts impose high inventory costs on the companies and require human resources, energy, and budget for the repair operations. These issues justify integrating repair, and inventory management decisions to reduce costs. Since the system is interacting with the environment, incorporating the sustainability dimensions with network design and planning decisions help managers to make more reliable decisions. We investigated the social and environmental dimensions to cover the sustainability dimensions of the spare part supply chain. These attributes contribute to industry-oriented properties in real-world problems. This paper investigates a multi-objective model to minimize costs while maximizing sustainability in a repairable spare part supply chain. Life cycle assessment (LCA) is utilized to assess social and environmental dimensions. Finally, the model is solved using NSGA-II with a priority-based encoding and decoding procedure. The findings shed light on contributing to formulating the spare part supply chain sustainability which integrates the network design and planning decisions resulting in more reliable outcomes.

In today’s turbulent world, unpredictable events with various effects on human health have highlighted the importance of an agile and integrated network to provide health services. Providing health services to applicants is realized when procurement of the required health items is effectively and efficiently managed. In the present research, a practical approach is taken to design an integrated network to provide laboratory services and manage the procurement of related items under uncertainty. For this purpose, a multi-product and multi-period optimization model is presented to minimize the expected costs. Also, a scenario-based robust optimization approach is adopted for uncertainty programming. Moreover, a real-world case study in Iran is employed to ensure the effectiveness and efficiency of the presented model. Integrating the network of providing laboratory services and managing the procurement of related consumable items, employing geological software such as Arc GIS to locate potential facilities in laboratory service network, and simultaneously dealing with disruptions and operational risks in healthcare networks are the distinctive research contributions. The obtained results indicate the advantages of designing an integrated network to provide laboratory services and manage the procurement of relevant items to save costs and improve the quality of providing service to applicants. In general, it could be observed that a lack of planning to deal with disruptive incidents could cause severe damage to the performance of the studied integrated network.

In this paper, we investigate an integrated procurement and capacitated vehicle routing problem for the distribution of multiple relief goods after the disaster, to determine the best tour for vehicles as well as the best selection of multiple relief goods and their quantity to be loaded on vehicles. Due to the uncertain nature of the parameters, the demand distribution and cost parameters are considered as fuzzy parameters. Furthermore, this paper examines the impact of information and communication technology in the affected areas so that instant information, communicate between the affected areas and the disaster coordination center due to new events caused by the disaster. We have examined the impact of information and communication technology on reducing demand uncertainty such that with consideration of the cost of equipping GPS in affected areas, as well as its impact on reducing demand uncertainty and the cost of dissatisfaction as a result; the best affected areas are selected to be equipped with GPS. To have robust solutions, a robust possibilistic programming model is proposed. The results of the model are shown in a real case study in district 7 of Tehran which acclaim that the proposed model achieves a better result than the traditional models without considering ICT.

The oil industry is one of the most significant strategic industries in countries with oil reserves. For this reason, politicians desire to know how to choose a condition with maximum exploitation but minimum cost. In recent years, most of the experts have been paying attention to intelligent oil fields owing to their advantages. Intelligent oil fields and operational integrated systems can reduce the number of drilling operations and as a result, lower the costs of drilling. On the other hand, intelligent equipment will decrease water production from the field, improve the quality of the produced oil, and reduce the costs of water production. This paper focuses on generalizing production data values and exploitation costs of the oil field, from the conventional field to an intelligent one. To this end, a model of the system dynamic was developed in VENSIM software to assess the effective factors on intelligent oil fields, the value of reducing water production, and lowering drilling cost in the intelligent state. Eventually, it was found out that the costs of drilling and water production are dependent on the price variation of each oil barrel, the percentage of the lack of equipment, and the power of current suppliers. The variation of each factor has a remarkable effect on the costs of drilling and water production with oil.

The industry life highly depends on spare parts since it is vital to perform maintenance operations, especially in strategic industries. The expensive and low-demand spare parts are a must for the continuation of the production; therefore, they are held in warehouses to meet unexpected demand. These spare parts cause high inventory costs also they require human resources, energy, and budget for the repair operations. It is important to point out that separate optimization of decisions in spare part supply chain leads to sub-optimality so, an integrated mathematical model can outperform a routine model. In this paper, we present a network design and planning model that is integrated with the METRIC model (Multi-Echelon Technique for Recoverable Item Control) that formulates inventory management decisions of the repairable spare parts. This model covers different decisions such as supplier order assignment, stock level in warehouses, flows among the facilities, and location of facilities. Due to the np-hardness of the problem, a hybrid approach is presented that incorporates heuristic and meta-heuristic methods. This approach is used to solve the proposed model that has been never applied in previous researches for such a model.

Growing global pressures for higher industrial sustainability has persuaded organizations to implement new managerial techniques at all organizational levels to achieve supply chain sustainability. However, such novel management practices should be measured in terms of the extent to which they may promote sustainability performance. The purpose of this study was, thus, to examine the dynamic behavior of LARG supply chain management practices and their impact on sustainability in the auto parts supply chain. Expert views were utilized in the present study to prioritize the LARG practices to achieve sustainability through fuzzy DEMATEL and the fuzzy analysis network process to present an integrated approach to LARG practices. Finally, the system dynamic approach was employed to assess the sustainability performance of LARG supply chain management practices. The finding indicated theAuto parts Supply Chain, Fuzzy Analytic Network Process, Fuzzy DEMATEL, System Dynamics, Sustainability significant role of improvement scenarios in the implementation of comprehensive quality management and punctual production, and thereby, in supply chain sustainability. The proposed model can assist industrial managers and decision makers to identify and adopt effective practices on sustainability in LARG supply chain management.

Productions of the industries around the world depend on using equipment and machines. Therefore, it is vital to support the supply of equipment and spare parts for maintenance operations, especially in strategic industries that separate optimization of inventory management, supplier selection, network design, and planning decisions may lead to sub-optimal solutions. The integration of forward and reverse spare part logistics network can help optimize total costs. In this paper, a  mathematical model is presented for designing and planning an integrated forward-reverse repairable spare parts supply chain to make optimal decisions. The model considers the uncertainty in demand during the lead-time and the optimal assignment of repairable equipment to inspection, disassembly, and repair centers. A METRIC (Multi-Echelon Technique for recoverable Item Control) model is integrated into the forward-reverse supply chain to handle inventory management. A case study of National Iranian Oil Company (NIOC) is presented to validate the model. The non-linear constraints are linearized by using a linearization technique; then the model is solved by an iterative procedure in GAMS. A prominent outcome of the analyses shows that the same policies for repair and purchase of all the equipment and spare parts do not result in optimal solutions. Also, considering supply, repair, and inventory management decisions of spare parts simultaneously helps decision-makers enhance the supply chain's performance by applying a well-balanced repairing and purchasing policy.

Recently, renewable energy resources such as solar energy have been significantly utilized in various sectors, regarding world population growth and the increasing use of fossil fuel and non-renewable resources, and consequently, the increased rate of environmental pollution. Implementing photovoltaic systems is regarded as one of the methods of using solar energy, which countries have highly considered in recent years. With the limited lifetime of photovoltaic systems, addressing the forward and reverse supply chain of these systems plays a significant role in increasing their efficiency. To this end, the present study seeks to develop a two-objective mixed-integer non-linear model, including minimizing total costs and minimizing the negative environmental impacts, aiming to design a closed-loop supply chain network for photovoltaic systems. In this study, the augmented ε-constraint method was employed to convert the current two-objective programming model into a single-objective one. Finally, the proposed model was implemented in a case study in Iran to evaluate the model's efficiency. The results indicated that solar power plants should be built in areas with higher solar energy and lower cost. Also, the model dynamics could increase the number of constructed solar power plants and their electricity generation capacity over the time horizon, followed by increased demand for annual electricity generated by solar energy.

A Location-Arc Routing Problem (LARP) is a practical problem, while a few mathematical programming models have been considered for this problem. In this paper, a mixed non-linear programming model is presented for a multi-period LARP with the time windows under demand uncertainty. The time windows modeling in the arc routing problem is rarely. To the best our knowledge, it is the first time that the robust LARP model is verified and an optimal solution is presented for it. For this purpose, the CPLEX solver is used for solving the treasury location problems of a bank as a case study. These problems are node-based with close nods and can be transformed into arc-based. Therefore, the method LRP and LARP models can be used to solve these problems. The comparing results of the LRP and LARP models prove that the LARP has a better performance regarding timing and optimal solution. Furthermore, comparing the results of deterministic and robust LARP models for this case study shows the validity of the robust optimization approach.

In this research, the main purpose of managing disorders that occurs in the supply chain of Iran Khodro, in such a way that the supply chain can keep the service provided to the customer in the event of a disturbance, or created the lowest changes in the customer’s service. . For this purpose, first examines the concepts and theories associated with disruption and recovery in the supply chain, and then the research mathematical model is designed and presented. Finally, the validation of the model and solving a model based on data collected from Iran Khodro is presented in one of the real issues of the company. Based on the results, it is determined that the occurrence of an impairment in one of the suppliers can disturb the entire supply of supply chain members to disturb, and when the disorder is corrected, the retarded production process to the next periods. Transferred and its recovery in subsequent periods will continue with the production disturbance of subsequent periods to 15 periods, and then its impact will be resolved, which can be expressed by taking 1 year production periods that the impact of sanction and lack of production of production pieces It has been remained in the system for 15 years and can disrupt this system without increasing production capacity and will recover the production system for 15 years.

This paper aims to conduct a research on the labor-management negotiation in chicken evolutionary game models through catastrophe theory. The both players can compromise or not during the negotiation. The "no compromise" strategy for labor means threat to strike and for management is ignoring labors' demands. Since the model of this research is chicken game, if on player decides to dig in, the optimum decision for other is to compromise, however it is costly to be calling a chicken by the rivals. In the process of evolution, players reevaluate their options to update the payoffs in case of gradual and continuous changes which may happen in effective variables of strategy selection. The continuous changes could cause a catastrophic change in system’s state and its collapse by a strike or lockout. ESS analysis and determining catastrophe threshold in the chicken evolutionary game will be done with the aim of giving managerial insights that help the players to prevent making decisions that could cause unsuccessful negotiation.

Real time and accurate fault detection has attracted an increasing attention with a growing demand for higher operational efficiency and safety of industrial gas turbines as complex engineering systems. Current methods based on condition monitoring data have drawbacks in using both expert knowledge and quantitative information for detecting faults. On account of this reason, this paper proposes an online fault detection and isolation method based on belief rule base (BRB), which can deal with modeling behavior of complex systems when semi-quantitative information is available. Although it is difficult to obtain accurate and complete quantitative information, some expert knowledge can be collected and represented by a BRB, which is essentially an expert system. As such, a new BRB based diagnosis model is proposed to detect and isolate faults of the system in real-time when aiming at isolating various damages and determining the severity of each. Moreover, a recursive algorithm is developed for online updating the parameters of the fault diagnosis model. Equipped with the recursive algorithm, the proposed diagnosis model can determine the severity of fault in real-time when two types of faults are dependent and competitive. To prove its potential application, experimental results demonstrate that the proposed model can track the fault severity very well, and the faults can be diagnosed accurately in real time. Thus, R2 values of 0.99782 and 0. 99782 were obtained for the fault estimation of fouling and erosion, respectively, indicating the accurate performance of the proposed model.

Improving sustainability in supply chains is one of the strategic objectives in the current business. Hence, firms have adopted new management strategies to achieve sustainability in their supply chain. Therefore, it is essential to assess the sustainability performance of the newly implemented management strategies. The purpose of this paper is to provide a model for assessing the sustainability performance of LARG supply chain management practices in the automotive supply chain using the dynamics system.

LARG supply chain management practices were identified, by reviewing the literature and interviewing industry experts, prioritized using fuzzy analysis network process, and were eventually presented as an integrated approach to LARG supply chain management practices. Finally, the dynamic system has been used to assess the dynamics of LARG supply chain management practices and their impact on sustainable performance in the supply chain.

The research findings show that improved measures in the implementation of total quality management, just in time, flexible transportation lead to a more sustainable supply chain. The results of these measures show sustainability improvement in the supply chain.

The results show that a lean strategy is very important for achieving sustainability in the supply chain. The proposed model helps industry managers and decision makers identify the results achieved from implementing LARG supply chain management practices and improving effective practices on sustainability in the supply chain by adopting policies.

Environmental monitoring and sustainability improvement while maintaining productivity and profitability are accepted as strategic objectives in in current business. Therefore, it is necessary to implement management practices that not only improve the performance and efficiency of the supply chain. But also focus on economic, environmental and social dimensions.

The purpose of this paper is to provide an integrated approach to the lean, agile, resilient and green supply chain management in automotive industry in Iran to achieve sustainability.

This paper aimed to present a new LARG strategy by reviewing the studies on lean, agile, resilient, green supply chain management. This paper is presented an analytical-hybrid model based on fuzzy DEMATEL and fuzzy analytic network process for integrating of LARG supply chain management practices in the supply chain in automotive industry in Iran. Using the fuzzy DEMATEL, the relationships between the criteria and the sub-criteria were identified and then the weights were calculated by the fuzzy analytic network process and their significance was determined.

The integrated LARG supply chain management approach includes: Strategic stock (resilient), just in time (lean), Supplier relationships (lean), Total Quality Management(lean), Ability to Change delivery times of Suppliers order (Agile), lead Time reduction (resilient), Flexible Transportation (resilient), and to Reduce Energy Consumption (Green).

according to the findings, LARG integrated strategy for achieving sustainability in the supply chain is proposed. Moving towards sustainability through implementing LARG supply chain practices is a key strategy in automotive industry in Iran.

Recently, many studies have been conducted in the open innovation context at the enterprise level to maximize the benefits and to control the challenges. Open innovation portfolio management studies are needed to bring integration between project and firm level. However, there is a few researches at project and portfolio level. This paper used previous theoretical foundations and a multiple case study method at ten ICT companies, utilizing a qualitative content analysis approach to analyze 23 semi-structured interviews and to derive a qualitative conceptual framework for open innovation portfolio management. The research findings are presented in five main categories including: strategic management in open innovation portfolio management; culture management in open innovation portfolio management; process management in open innovation portfolio management; stakeholder management in open innovation portfolio management and technology management in open innovation portfolio management. In this study, the area of technology management as a finding that has not been addressed in the literature and also the concept of charismatic management in the area of cultural management, the concepts of knowledge management, integrated market management and processes of the R&D unit for the area of process management can be categorized as less mentioned concepts in the literature.

As the importance of supply chain management becomes more evident to the industry owners, the role of cooperation and integration of supply chain different components has become more vivid in creating competitive advantage. This paper proposes a comprehensive mathematical model for location-inventory-routing problem of perishable products given shortage, shipping time, and environmental considerations under uncertainty. To this purpose, an accurate solution was proposed by formulating the problem as non-linear programming of mixed integer using scenario-based stochastic approach. This approach simultaneously minimizes the sum of system costs (the cost of locating centers with certain level of capacity, operational cost of centers, transportation costs, maintaining inventory, and/or shortage of combined center of production/inspection), the sum of maximum time in the chain and emission of pollutants in the whole network. As the problem is NP-hard, a genetic algorithm approach has been proposed to solve the model. For validation, the results of the proposed algorithm in the small size examples were compared to the results of precise solution method. The obtained results revealed the capability of the proposed algorithm in reaching a solution with acceptable percentage difference and in a very shorter time compared to precise solution method. Additionally, the results from algorithm performance were investigated based on standard indicators. The computational results show the efficiency of the proposed model and solution method.

Determination of degradation status and estimating remaining useful life are two main activities in prognostics and health management. These two main activities can be perceived as the problem of multi-sensor data fusion. These sensors contain information including speed, pressure, and temperature. In terms of evidence theory, the information obtained from each of these sensors can be regarded as a part of the evidence and determination of degradation status and estimating remaining useful life based upon this information can be considered as the problem of multi-sensor data fusion. In this article, the Dempster-Shafer theory has been employed as a tool for modeling and multi-sensor data fusion as an indicator of the health status of the turbofan. In this regard, initially, the theory of evidence has been reviewed and then how to model the remaining useful life estimation problem by multi-sensor data fusion within the framework of the concepts of this theory including mass function, focal elements and rules of evidence composition is explained. This paper has introduced a new approach for combining the results of different estimates of remaining useful life through determining the weighs. Furthermore, it has offered two different scenarios to specify the similarity between the system under study and the available evidence. Finally, to appraise the proposed method, the turbofan engines data set (C-MAPSS) has been used as it has been extensively addressed in literature by researchers. According to the results, the proposed method revealed better score and performance compared with other available methods in the literature.

Using the services available in different organizations, you make use of the international organization, providing consulting services. Internet activities, given access to the island, cannot be reorganized into international organizations and can present you with a competitor and thus become Brazil. Paying attention to the dedicated task and switching specific features as a vehicle, body, sensitive and gravitational imposes a lot of costs on the organization and you can send us many problems as well. Use the best of the most important reasons This is useful, you can use actual services, use specialists on the Internet and use specialists on the Internet, using specialized facilities, access to specialists, feasibility, usability skills, quality, Synchronization using modified methods, and a variety of contractors can use these applications and their applications, applications and scheduling, with good quality, synchronization using modified methods, and Be a variety of contractors. Imagine. This research has come to a conclusion in two phases: At the proposed level, the metrics of outsourcing and the economics of resistance are published with the study of scientific literature and the free consulting firm Advertising Distribution and we offer its basics. Secondly, with the help of empowerment economic criteria, outsourcing criteria using different criteria accessibility techniques (TOPSIS model) are also prioritized. By visiting your site, you can display your information by visiting your site.

The world economic image has been changing. It has been a long time since the sole important resources of an organization were financial and physical. In the newly-emerged global economy, knowledge and information are accounted as the most significant resources in order to produce value. Obviously, as the demand for knowledge to reach success is increasing day by day, maintenance and enhancement of an organizations’ inner knowledge possesses a high degree of importance. This knowledge is used by the individuals inside the organizations. In other words, human resource plays a vital role in an organization’s knowledge management. Additionally, due to the complexity of working conditions and advent of fierce competitions, a sheer number of organizations have moved towards project-oriented activities in an attempt to detract from the costs and to boost flexibility. Accomplishment of project-oriented activities is met in the form of a matrix organizational structure and therefor, upon completing any project, the human resource will immediately become involved in the next project or are even likely to leave the organization. Hence, maintenance of their knowledge and experience and transferring them to the next projects tend to be a matter of discussion.

Estimation of remaining useful life (RUL) is one of most interesting subjects in prognostic and health management. Performing an analysis of the results of such estimation can increase the reliability and the safety of the system, and reduce the unnecessary costs. In this paper, a similarity-based combination method is proposed to combine several run-to-failure historical datasets in order to directly estimate the RUL. In this method, reference datasets are clustered and the initial RUL is calculated based on the artificial neural networks trained by the reference datasets. By using the extended Dempster-Shafer, the similarity between the initial RUL and the average RUL for each dataset is obtained. The proposed methodology is tested and validated on Commercial Modular Aero-Propulsion System Simulation (C-MAPSS), test-bed developed by NASA. The results of the evaluation show that the proposed method outperforms other methods in the literature.

During the last three decades, the concept of decision-making integrity in the supply chain has become one of the most important dimensions in the supply chain management. This concept examines the dependence between the location of facilities, allocation of flow between facilities, the structure of transport system and inventory control system. This paper presents a new form of locating- routing- inventory problem in a closed-loop supply chain network for perishable products with respect to environmental considerations, in such a way that the aggregate system costs, the aggregate maximum transportation time and emission of pollutants throughout the network should be minimized. The research problem is formulated in the form of a multi-objective mixed integer nonlinear programming model and a genetic algorithm approach is proposed to solve the model. In order to validate, the results of the proposed algorithm are compared in small-scale examples with the exact solution method using GAMS software. The mean error of the proposed algorithm for the objective function is fewer than 4% as compared to the exact solution. In addition, the results of the algorithm's performance are discussed based on standard indices. The computational results indicated the efficiency of the algorithm for a wide range of problems with different sizes. The locating, routing and inventory decisions are interdependent and determining the optimal values for these variables is in interaction with each other which can lead to an optimal system with the least possible cost.

In this research, the intelligence sharing effect of high occupancy vehicle had been evaluated. To achieve this aim, firstly, HOV lane in base condition with uninterrupted flow and without any bottlenecks had been analyzed. The proposed model had been considered as a function of speed, volume and delay of traffic flow. The devel opment of evaluation model was considered for bottleneck conditions. In this thesis, three different scenarios for base condition and bottleneck determined which were consisted of sharing all vehicles, sharing vehicles up to three passengers and sharing vehicles with more than three passengers. In all scenarios, the portion of air pollution in unshared HOV lane with sharing strategies was compared. Regarding uncertainty the location of bottleneck, three possible location in highways had been considered. Since there was no actual bottleneck data in case study, the initial parameters of bottleneck had been simulated in AIMSUN software program. In these models, the effect of lane changing due to access to HOV lane had been calculated. The multiple regression model was used to evaluation air pollution reduction. The result of evaluation model shown that in base condi tion and where bottleneck was located on first lane and third lane, the second scenario which was about sharing vehicles up to three passengers was the best approach with orientation toward air pollution reduction. When the bottleneck was on the mid lane, the first scenario which was about sharing all vehicles was the best scenario.)

Numerous researchers and practitioners are attracted to sustainability in supply chains (SCS) and it has become one of the favorite topics among academics and industries. The purpose of this paper is to review and analyze the research studies that have been published in the field of sustainable supply chain management (SSCM). A total 242 articles published between 2000 and 2019 reviewed. Content analysis and descriptive analysis were applied in order to analyze the modeling approaches, evolution of models and methodologies, as well as sustainability metrics. The content analysis in terms of modeling approaches is based on analytic categories that provided by Brandenburg et al. (2014). The present study identified gaps and potential opportunities in SSCM to develop knowledge. The results indicate that methods such as genetic algorithm, nonlinear programming, goal programming, dynamics programming are less used by researchers. The multi-criteria decision making (MCDM) technique has the most application in the field of SSCM research. Researchers are more inclined to use analytical and mathematical models. The sustainability metrics with the highest frequency include “energy consumption”, “cost”, “health & safety”.

This research is collaborative investigation with the general-purpose motor manufacturer. To review construction method in production process, we applied the parameter design method of quality engineering and tried to approach the optimization of construction method. Conventionally, press-fitting method has been adopted in process of fitting rotor core and shaft which is main component of motor, but quality defects such as core shaft deflection occurred at the time of press fitting. In this research, as a result of optimization design of “shrink fitting method by high-frequency induction heating” devised as a new construction method, its construction method was feasible, and it was possible to extract the optimum processing condition.