Industrial management
Sara Bagherzadeh Rahmani; Javad Rezaeian; Ahmad Ebrahimi
Abstract
In today’s project-based organizations, where multiple projects are executed concurrently within work teams, human resources play a crucial role in the success or failure of these organizations. Consequently, human resources are recognized as one of the most essential resources for these organizations, ...
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In today’s project-based organizations, where multiple projects are executed concurrently within work teams, human resources play a crucial role in the success or failure of these organizations. Consequently, human resources are recognized as one of the most essential resources for these organizations, and their optimization can significantly increase productivity while reducing organizational time and costs. This underscores the importance of effective human resource management and highlights the need for special attention to this area. Therefore, this study presents a mixed-integer nonlinear programming model for the multi-objective project scheduling problem with resource constraints, multi-skilled personnel allocation and the assignment of projects to work teams. The mathematical model of this research includes the multiple objectives of simultaneous minimization of the total costs of setting up work teams and the use of human resources and the total flow time of projects. To make the model more realistic, the effect of learning is also considered. Subsequently, a diverse set of test problems at varying scales was designed. Then, the Multi-Objective Artificial Immune System (MOAIS) algorithm and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) were utilized to solve the problems. The results demonstrate the superior performance of the NSGA-II algorithm compared to the MOAIS algorithm.
Introduction
Human resource management is one of the fundamental pillars of organizational success and, alongside financial and technological resources, plays a crucial role in process optimization and achieving strategic objectives. Optimal workforce allocation and effective project scheduling enhance productivity, reduce costs, and ensure the efficient utilization of resources. Teamwork and knowledge sharing facilitate learning and skill development, which, in complex projects with limited resources, lead to shorter project completion times and improved organizational efficiency.
Accordingly, this study addresses the project scheduling problem by considering human resource constraints, multi-skilled work teams, setup times, varying project start times, and work-team dependent learning effect. The primary objective is to simultaneously minimize the total costs of setting up work teams and the use of human resources, as well as the total project flow time. To achieve this, a Mixed-Integer Linear Programming (MILP) model is developed.
Given that the problem is NP-hard, employing metaheuristic algorithms is essential for obtaining near-optimal solutions within a reasonable computational time. This research utilizes two metaheuristic algorithms, NSGA-II and MOAIS.
The findings of this study provide valuable insights for project managers and decision-makers, aiding in optimized project scheduling, efficient workforce allocation, and enhanced organizational productivity.
Research Background
In this section, we mention only a few of the most relevant studies to the current one. Su et al. (2021) explored team formation in project scheduling and presented a simple mathematical model for task scheduling in single-skilled workgroups with restricted access to resources, aiming to minimize makespan (Cmax). In their model, workers were assigned to fixed groups, and tasks were allocated based on processing time and workforce availability. They proposed a hybrid genetic algorithm with a bin-packing strategy to solve the problem.
Mozhdehi et al. (2024) developed a mixed-integer mathematical model for multi-project scheduling with limited resources and multi-skilled workforce. They considered workforce agility, which improves either through collaborative teamwork and knowledge-sharing with more skilled colleagues or by dedicating more time to skill development. Their results indicated that incorporating workforce agility into project scheduling models significantly reduces project completion time.
Methods
In this study, a Mixed-Integer Linear Programming (MILP) model was developed to address the multi-project scheduling problem with multi-skilled work teams. The model integrates human resource constraints, setup times, and work-team dependent learning effect, ensuring a practical and efficient scheduling framework.
For solving the model, the single-objective version was first handled using the Branch and Bound algorithm in Lingo software. Then, for the multi-objective version, two metaheuristic algorithms, NSGA-II and MOAIS, were implemented to generate high-quality trade-off solutions.
To assess and compare the performance of these algorithms, a set of test problems of different scales (small, medium, and large) was designed and solved. The Taguchi Experimental Design Method was employed to fine-tune the key algorithm parameters, optimizing efficiency and accuracy.
Evaluating the performance of multi-objective metaheuristic algorithms is more complex than that of single-objective optimization due to the presence of non-dominated solutions that cannot be strictly ranked. In this study, the following key metrics were used to assess solution quality and diversity:
Number of Pareto Solutions (NPS)
Mean Ideal Distance (MID)
Diversity Metric (DM)
Spread of Non-dominated Solutions (SNS)
Discussion and Results
Results of sensitivity analysis reveals that increasing the learning rate of work teams significantly reduces project completion time. This finding underscores the importance of incorporating learning effects in multi-skilled workforce scheduling models. With a higher learning rate, teams execute tasks more efficiently and in less time, directly contributing to organizational productivity improvements.
Furthermore, computational results indicate that in small to medium-sized problems, there is no significant performance difference between NSGA-II and MOAIS. However, in large-scale problems, NSGA-II outperforms MOAIS. This superiority is attributed to NSGA-II’s population evolution mechanism, which enables a broader exploration of the solution space and prevents premature convergence to local optima. In contrast, MOAIS, due to its elitist nature, primarily focuses on replicating high-quality solutions, avoiding exploration in other regions of the search space. This increases the likelihood of getting trapped in local optima, thereby reducing search diversity. Furthermore, performance comparison results indicate that NSGA-II demonstrates superior Pareto front coverage and convergence to optimal solutions compared to MOAIS.
Conclusion
This study investigated the project scheduling problem considering human resource constraints, multi-skilled work teams, setup times, varying project start times, and work-team dependent learning effects. The primary objective was the simultaneous minimization of the total costs of setting up work teams and the use of human resources and the total flowtime of projects. To achieve this, a Mixed-Integer Linear Programming (MILP) model was developed, and its performance was evaluated through sensitivity analyses and numerical experiments. The results demonstrated that the proposed model performed effectively under various constraints and exhibited high accuracy and efficiency.
Given the NP-hard nature and multi-objective characteristics of the problem, two metaheuristic algorithms, NSGA-II and MOAIS, were implemented to solve it. The algorithm parameters were fine-tuned using the Taguchi Experimental Design Method, and their performance was compared across different problem sizes. Computational results indicated that while both algorithms performed similarly in small to medium-sized problems, NSGA-II outperformed MOAIS in large-scale instances. Further analysis revealed that MOAIS, due to its elitist-based nature, primarily focuses on replicating high-quality solutions, often avoiding broader exploration within the solution space. This characteristic increases the likelihood of getting trapped in local optima, reducing solution diversity. In contrast, NSGA-II, through its non-dominated sorting mechanism, allows lower-fitness solutions to participate in the evolution process, leading to broader solution space exploration and preventing premature convergence.
For future research, it is recommended to extend the model by incorporating additional operational assumptions, such as activity failure probabilities, simultaneous consideration of learning and forgetting effects, rework processes, and uncertainty of certain parameters in the problem. Furthermore, exploring more advanced heuristic and hybrid metaheuristic algorithms is suggested to enhance the efficiency of the solution approach.
Industrial management
Mina Kazemian; Mohamad Ali Afshar Kazemi; Kiamars Fathi Hafshejani; Mohammad reza Motadel
Abstract
IntroductionThe field of supply chain management has focused on crucial topics such as coordination, cooperation, and competition among chain members. Game theory has emerged as a valuable tool for examining supply chain management issues, as it analyzes various situations and their impact on supply ...
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IntroductionThe field of supply chain management has focused on crucial topics such as coordination, cooperation, and competition among chain members. Game theory has emerged as a valuable tool for examining supply chain management issues, as it analyzes various situations and their impact on supply chain performance (Naimi Sediq et al., 2013; Shafi'i et al., 2018). While every action and performance within the supply chain influences the outcomes of the game, it does not solely determine them. The goal is to balance the parties involved in the supply chain and create satisfaction for the end customer (Metinfer et al., 2018).Although extensive research has been conducted in supply chain management within the steel industry, the impact of sanctions on Nash equilibria and the application of three approaches (Cournot, Stackelberg, and collusion) to achieve game balance in different scenarios have not been thoroughly investigated. This research aims to fill this gap by addressing the mentioned research question. The current study focuses on determining the optimal price using an intelligent decision-making system based on game theory within the steel industry, considering the presence or absence of the sanctions variable.Our country currently possesses several relative advantages in terms of steel production conditions, including abundant and affordable energy, iron ore and refractory raw materials, considerable steel production experience, and a skilled and cost-effective workforce. By acquiring new production technology, these advantages enable our country to play a competitive and influential role in the global steel market. However, the steel industry faces significant challenges such as price fluctuations, extreme price disparities across regions, and delayed delivery due to a lack of efficient supply chain management. Therefore, the main research question aims to provide a model that incorporates key variables, such as the supply and demand of final and intermediate products in the steelmaking industry and the future trends in production and quantity changes.Research methodThis article introduces a composite model that combines artificial neural networks and game theory to assist stakeholders in the steel industry in determining optimal production levels and price levels. To predict the price of steel, three techniques were employed: Bayesian neural networks, support vectors, and Grassberg anti-diffusion. Additionally, to address the issue of binary identification in the neural network, three different network structures were introduced: feedforward network structure, competitive network structure, and backward associative memory network structure.Research findingsThe first scenario is the non-cooperative game (Cournot model scenario) where each participant aims to maximize their profit and would not deviate from their strategy as it would lead to a reduction in their profits. The second scenario is the sequential non-cooperative game (Stackelberg model scenario), in which two chains engage in a confrontation of the Stackelberg game type. The leader's goal is to determine the best strategy while considering all rational strategies that follower players can employ to maximize their income. This scenario demonstrates that the rate of price and profit increase is lower compared to sequential and cooperative game modes. The third scenario is the cooperative game (collusion model scenario). In this scenario, the allocation of profits among the cooperating members is crucial to ensure the stability of their cooperation. The Grassberg anti-diffusion method exhibits higher accuracy due to its higher true positive (TP) and true negative (TN) values compared to other algorithms. Additionally, it has fewer false positives (FP) and false negatives (FN) because a higher TP and TN indicate more accurate predictions in the tested dataset, while FP and FN represent incorrect predictions. The inclusion of the sanctions variable as a moderating factor in the steel price forecasting model accounts for the potential reduction in production and increased cost price. Through the model, it was discovered that the Grossberg method yields more accurate steel price forecasting. Consequently, price forecasting in the model is based on the Grossberg method.Research resultsThe results indicate that transitioning from the Cournot game to the Stackelberg game and from the Stackelberg game to the collusion game in the steel industry's supply chain leads to a $6 increase in price per ton and a product supply ranging from 1500 to 4000 tons. In other words, as collusion in the steel market intensifies, more products are introduced into the market, resulting in an increase in product prices and a decrease in the welfare of steel consumers. According to the findings, increased competition in the industry reduces the profitability and production levels of producers while enhancing consumer welfare. Conversely, higher levels of monopoly exhibit the opposite effect. To maintain a balanced supply chain in the steel industry and prevent potential problems, it is recommended to adopt the Stackelberg game approach, which aligns more closely with reality. It is worth noting that the order in which players enter the game impacts the Nash equilibrium. Therefore, exploring market entry monitoring regulations and rules in this industry becomes crucial since the steel industry involves high entry and exit costs. Policymakers and industry managers should consider monitoring the entry and exit of players, formulate game standards and rules among market participants. Based on the results, the primary recommendation of this research is to increase competition intensity and adopt the Cournot approach in the industry to reduce prices and increase production. Additionally, enhancing international relations and diplomatic efforts will mitigate the impact of sanctions on the industry, leading to cost price improvements and an increase in the level of comparative advantage at the international level.
supply chain management
S.Ali Torabi; Yasin Heidari
Abstract
In a competitive world, one of the most crucial ways to enhance the supply chain performance of manufacturing companies is through integrated scheduling of production and distribution activities. Two significant concerns for dentists and patients include delayed denture deliveries and the multiple production ...
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In a competitive world, one of the most crucial ways to enhance the supply chain performance of manufacturing companies is through integrated scheduling of production and distribution activities. Two significant concerns for dentists and patients include delayed denture deliveries and the multiple production and correction processes for dentures. This research addresses these concerns by developing a mixed-integer linear programming model for solving the integrated production and distribution scheduling problem in a fixed denture supply chain operating under an additive manufacturing environment. The objective functions of this model aim to minimize the cost of production and distribution orders while reducing weighted delays. The Augmented Epsilon Constraint Method is employed to identify Pareto-optimal solutions. To validate the mathematical model, a numerical example and a case study are presented, and various sensitivity analyses are conducted on key model parameters. The numerical results demonstrate substantial improvements in total costs and customer satisfaction levels.IntroductionA supply chain (SC) comprises several interconnected echelons and processes, where an integrated perspective can lead to optimal overall SC performance. Simply improving an organization's internal processes is insufficient for competitiveness in the market; establishing effective relationships with suppliers, distributors, and other SC stakeholders is essential. Achieving maximum value along the SC involves focusing on cost reduction through cost-effective decision-making. In the past decade, the rising adoption of 3-D printing and additive manufacturing technologies in SCs, as a prominent disruptive technology in the Industry 4.0 era, has created numerous opportunities for improving manufacturing SCs compared to traditional production methods. These opportunities include reduced setup and production times, lower safety stock levels, and fewer processing steps. Additive manufacturing has found applications in various fields, particularly in denture production. This research addresses two primary concerns in the field: timely denture delivery and the multiple production and correction processes associated with dentures. A novel mathematical model is developed to tackle these issues, aiming to solve the integrated production and distribution scheduling problem in a fixed denture supply chain operating within an additive manufacturing environment. The objective functions of this model aim to minimize the costs associated with production and order distribution while minimizing the weighted total delays.Materials and MethodsA mixed-integer linear programming model is devised to address the problem outlined in this paper. The Augmented Epsilon Constraint Method is applied to identify Pareto-optimal solutions. To validate the mathematical model, a numerical example and a case study are presented, and several sensitivity analyses are conducted on key model parameters to elucidate their critical roles in the final solutions.Discussion and ResultsA case study is provided to demonstrate the practical applicability of the developed model. Sensitivity analyses on demand data highlight the substantial impact of demand management on final solutions. This research presents a two-objective optimization model to address the simultaneous scheduling of production and order delivery in a three-tier dental prosthesis supply chain. The first tier comprises a dental prosthesis production laboratory, while the second and third tiers include distributors and dentists (final customers). The objective functions include the minimization of total order delivery costs and the average weighted lateness of delivered products from a fixed dental prosthesis production laboratory. Constraints encompass delivery time delays, order allocation to customers, capacity limitations, calculations of time to reach each customer, and vehicle routing. Given that this research problem falls into the category of multi-objective problems, the Augmented Epsilon Constraint Method is employed to obtain Pareto-optimal solutions. To investigate and implement the proposed model, a fixed dental prosthesis production laboratory in Neka City is examined. The numerical results indicate the existence of a trade-off between the problem's objectives.ConclusionsThis paper presents a bi-objective model to address the integrated production and distribution scheduling problem in a three-tier dentures supply chain, aiming to minimize total delivery costs and the average weighted tardiness. The first tier includes a dentures production laboratory, while the second and third tiers comprise distributors and dentists, respectively. Numerical results based on a real case study demonstrate the practical applicability of the model. Several avenues for future research include considering uncertainty in input data and developing efficient meta-heuristic algorithms for solving large-scale instances.
production and operations management
Morteza Saeidi; Mostafa Ebrahimpour Azbari; MohammadRahim Ramazanian
Abstract
The purpose of this article is to design a model for improving the sustainable performance of small and medium food companies in Guilan Province. The aim is to provide managers and decision-makers with insights into the factors, challenges, and consequences associated with enhancing the sustainable performance ...
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The purpose of this article is to design a model for improving the sustainable performance of small and medium food companies in Guilan Province. The aim is to provide managers and decision-makers with insights into the factors, challenges, and consequences associated with enhancing the sustainable performance of these companies in the current business environment. The article falls under the category of applied research in terms of its purpose, exploratory-descriptive research in nature, and field studies based on data collection. It is classified as qualitative research. The Grounded Theory method was employed to create a paradigm model, progressing through three stages of open, axial, and selective coding. The results of data analysis using Grounded Theory led to the identification of 13 main floors, 29 main categories, and 248 concepts. The paradigm model, developed through theoretical coding, encompasses six main classes: pivotal phenomenon (sustainable performance improvement), causal conditions (organizational survival, social and environmental obligation, gaining competitive advantage), contextual conditions (environmental factors, organizational factors), interfering conditions (environmental factors, organizational factors), and strategies (macro level, organizational level). Finally, the economic, social, and environmental consequences of achieving sustainable performance improvement were derived through the implementation of these strategies.
Introduction
The increasing societal focus on sustainability necessitates attention to improving sustainable performance in small and medium-sized companies. Today, achieving a competitive edge and attracting customers goes beyond operational or financial superiority. In the contemporary business landscape, companies are expected to demonstrate responsibility and consider future generations in their activities. SMEs play a pivotal role in the industrial growth of developing economies globally. Enhancing communication and information flow in small and medium-sized companies can lead to more efficient processes and cost reduction. To thrive in the current business environment, SMEs must adopt emerging technologies to improve their sustainable performance. Industry 4.0 and the circular economy, which involve the use of advanced technologies like artificial intelligence and the Internet of Things, can be instrumental in achieving sustainable performance in these companies. The synergy between Industry 4.0 technologies and circular economy methods positively contributes to enhancing sustainable performance. Given that small and medium industries are significant drivers of economic growth in regions like Guilan province, improving the sustainable performance of companies in this sector can have a substantial impact on preserving and maintaining environmental aspects. With the rapid growth of technology and the advancement of Industry 4.0, SMEs are recognized as vital economic foundations contributing to the development and economic growth of societies. It is crucial to investigate and conduct research in the field of Industry 4.0 and the circular economy to improve the sustainable performance of small and medium-sized companies. A conscious analysis of the benefits and challenges of Industry 4.0 on SMEs allows for a better understanding of the needs and opportunities within this sector. Examining the effectiveness and utilization of new technologies in the processes and organizational structures of small and medium companies can lead to increased efficiency, cost reduction, improved quality, enhanced decision-making processes, and expanded market presence.
Literature Review
Performance, in a broad sense, can be defined in accordance with the concept of quality and an organization's ability to achieve internal and external goals. It is important to note that performance encompasses multiple dimensions. Sustainability, on the other hand, is a multidimensional concept that presents a significant challenge in our time. It involves the understanding and management of economic, social, and environmental performance simultaneously. Researchers argue that if the current population and economic growth rates persist, the utilization of the planet's natural resources will surpass its capacity. This issue gives rise to environmental protection concerns, which are addressed under the umbrella of sustainable development. Sustainable development is defined as development that meets the needs of the present without compromising the ability of future generations to meet their own needs. In a broader sense, a sustainable development strategy entails creating harmony among humans and between humans and nature. It implies that sustainability requires a societal approach that manages the environmental perspective alongside the economic perspective in the progress of development and performance improvement. In the present era, there is an expectation from customers and members of society for companies and organizations to be responsible and consider future generations in their activities and operations. Prioritizing the future generation in activities signifies a positive step towards sustainable performance and demonstrates the organization's commitment to a sustainable global economy. While the concept of sustainability is easily understood, operationalizing and concretely expressing it poses challenges. Decision-makers in industries need to evaluate and review their operations considering both internal and external effects. Optimal decisions can only be made when the social, economic, and environmental consequences are taken into account.
Methodology
The Research falls under the category of applied research in terms of its purpose, exploratory-descriptive research in nature, and field studies based on data collection. Grounded theory, a qualitative research approach, is particularly well-suited for exploratory research seeking to theorize or identify patterns. It employs open, axial, and selective coding methods for data analysis. This approach, introduced by Strauss and Corbin in 1990, is based on the three-stage coding process of open, axial, and selective (or theoretical) coding. In this regard, it utilizes the logical paradigm or diagrammatic representation of the theory created. Research based on the grounded theory approach, employing a systematic strategy, culminates in the development of hypotheses and statements that may specify the relationships between classes in the axial coding paradigm.
Discussion and Results
The central category identified is Sustainable Performance Improvement. Two dimensions associated with this central category are Industry 4.0 and the circular economy. The most crucial factors influencing sustainable performance improvement are categorized into three groups: organizational survival (including profitability and maintaining market share), social and environmental requirements (such as attracting public support, considering future generations, and managing limited resources), and gaining competitive advantage (encompassing both temporary and sustainable competitive advantages). These factors contributing to sustainable performance improvement are further classified into two categories: environmental factors and organizational factors. Environmental factors involve challenges such as environmental instability, legal factors, and political factors, while organizational factors encompass financial and cost considerations, along with human resources. Strategies for achieving desirable outcomes are outlined at both the macro level (involving government and industrial estate company activities related to consortium formation, culture, and education) and the organizational level. Small and medium-sized food industry companies are encouraged to pursue innovation, creativity, value chain knowledge, and professional structure to create favorable benefits for themselves. The final segment of the sustainable performance improvement model outlines economic, social, and environmental consequences. Key consequences at the economic level include company development, sustainable profitability, and sustainable productivity. At the social level, consequences encompass the welfare of workers and the social responsibility of the organization. The most critical consequence at the environmental level is the achievement of a sustainable environment.
Conclusion
To design a model for enhancing the sustainable performance of small and medium-sized companies, the grounded theory methodology with the systematic approach of Strauss and Corbin was employed. This involved conducting interviews with experts to gather the necessary conceptual codes. Following the systematic approach, data analysis utilized three stages: open coding, axial coding, and selective coding. The design of the model adhered to the paradigm model of this approach, incorporating six dimensions: the central category, causal conditions, contextual conditions, intervening conditions, strategies, and consequences.
Ali Khatami Firouzabadi; Ali Khoramrooz
Volume 4, Issue 12 , March 2006, , Pages 45-71
Abstract
Experiments have shown the most important factor in leakage of gas pressure stations are taps. Gas leakage in industrial environment has hazardous effects.so, there is important to consider the same factors in these environments. To recognize these factors, it is necessary to represent the importance ...
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Experiments have shown the most important factor in leakage of gas pressure stations are taps. Gas leakage in industrial environment has hazardous effects.so, there is important to consider the same factors in these environments. To recognize these factors, it is necessary to represent the importance of avoiding these events by risk evaluation methods. Fault tree analysis is a method which can recognize the fault points in a system and it is able to obtain the probability of these events. To analyze the problem, fault tree of each region in the gas station is drawn. Then the data of these regions (including the probability of failure parts) are entered to FTA software. The outputs of software can be used to analyze the system. The outputs show that the most part of leakage caused by refnenet gas region which turbines are activated. Finally, some procedures are suggested for preventive of leakage in gas stations.
Mohammad Taghi Taghavifard
Abstract
Nowadays, customers are the most important sources of income for financial institutions and banks. According to the privatization process in the country and financial restrictions of banks, it is necessary to maintain and attract more profitable customers. Though, one of the most important methods to ...
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Nowadays, customers are the most important sources of income for financial institutions and banks. According to the privatization process in the country and financial restrictions of banks, it is necessary to maintain and attract more profitable customers. Though, one of the most important methods to identify profitable customers is the concept of customer lifetime value (CLV) but it is more important to estimate customers’ future conditions because a bank`s future profitability highly depends on the customers situation.In this research, the issues about CLV, the necessity and different classification methods are presented. Then, considering weighting variables using Recency, Frequency, and Monetary (RFM) model, AHP technique, and experts opinion, customers are classified. Using Markov chain and probability matrix, the displacement of customers in different groups and their future status are predicted.One of the major outcomes of this research is the calculation of profitability matrix to predict customers’ displacement in different groups. The probability matrix can also show the reluctance of large number of customers to move to the specified groups (the highest percentage of customers in the main diameter of the probability matrix). In this research, we observed that account balance (M) has the greatest impact on customers grouping and that the number of transactions (F) and recency variable (R) are ranked as the second and third impact factors. Also, the determination coefficient (C) is another result of the research. Finally, the presented research used financial information and proposed a mathematical model (Markov chain) to calculate the probability of customers’ displacement (switching from one group to another). The proposed model can be helpful to facilitate customer relationship management process in the banking system.
supply chain management
Fatemeh Arjmandi; Parvaneh Samouei
Abstract
Planning and scheduling operating rooms and required equipment is very important for hospital managers from the perspective of cost, social, and health principles. Because operating rooms are one of the sources of income for hospitals that can provide the services needed by emergency patients who arrive ...
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Planning and scheduling operating rooms and required equipment is very important for hospital managers from the perspective of cost, social, and health principles. Because operating rooms are one of the sources of income for hospitals that can provide the services needed by emergency patients who arrive randomly and elective patients whose surgery is preplanned. Since some tools used in operating rooms need to be sterilized each time, and in the real world, due to various reasons such as the physical condition of each patient, the duration of surgeries is uncertain, in this research, as well as considering the uncertainty of surgery times for emergency and elective patients, a robust scenario-based integrated mathematical model with two objective functions is presented. In this model, in addition to minimizing the cost of operating rooms, sterile section, and penalties for delay in surgery, the competition time of the last operation is minimized. Solving several problems and different sensitivity analyses confirmed the validation of the presented model from the viewpoint of the hospital managers.
Introduction
The healthcare system is one of the most important topics that has attracted the attention of researchers and hospital managers. Two significant elements of healthcare systems are patients and hospitals, and their planning and scheduling are necessary for providing better services. Hospital managers aim to minimize costs or maximize profits while considering service times. Operating rooms are often the most critical sections of the hospital. Since life and death issues are at stake in the operating rooms, even the slightest delay or lack of resources can endanger human lives. Therefore, careful planning and scheduling of operating rooms and their resources is especially important. Additionally, to prevent potential hospital infections, the sterilization of reusable medical instruments is essential for every operation. Furthermore, patients can be broadly categorized into two groups: emergency patients who arrive randomly, and elective patients whose surgeries are preplanned. Planning and scheduling operating rooms, along with the required equipment, is of utmost importance for hospital managers, taking into account cost, social factors, and health considerations. Some items used in operating rooms need to be sterilized after each use, which incurs both time and cost.
Methodology
Due to high fluctuations of the duration of surgery, the different condition of each patient, and in order to get as close as possible to the real conditions, the duration of each surgery is considered uncertain. In this research, in addition to considering selected patients, considering the uncertainty of the duration of surgery, emergency patients have also been investigated, and dealing with uncertainties, the robust optimization approach is used. This approach is stable against changes, and minimizes the fluctuation of changes and maintains optimal and feasible. Therefore, in this research, as well as considering the uncertainty of surgery times for emergency and elective patients, a robust scenario-based integrated mathematical model with two objective functions is presented. In this model, in addition to minimizing the cost of operating rooms, sterile section, and penalties for delay in surgery, the competition time of the last operation is minimized. There are costs for performing elective and emergency operations, and in order to give priority to emergency patients, the cost of performing the operation of these patients is lower than the cost of performing elective operations surgeries of emergency patients, such as those injured in accidents, must be performed as soon as possible and these patients should be paid less. Also, for emergency patients, a maximum waiting time is considered, which they should not wait more than it. Obviously, surgeries that cannot be scheduled during the working hours of the operating room are postponed. Every patient needs a number of reusable medical tools that require sterilization. The sterile duration of reusable tools and the capacity of each sterile machine are known.
There are various techniques for solving multi-objective problems, one of which is the epsilon constraint method. In this method, one of the objective functions is considered as the main objective function and other objective functions are applied as constraints to the problem. Various developments for the epsilon constraint method have been presented to make it more efficient, among which we can mention the Augmented Epsilon Constrain (AEC) method. Moreover, in order to solve the bi-objective integrated mathematical model, Mulvey's robust method is implemented on the model, and its validation is carried out in the GAMS software with the AEC method.
Results and Conclusion
Different sensitivities all confirm the validity and accuracy of the model from the point of view of hospital managers. According to the obtained results, the changes of the three parameters of surgery delay cost, duration of surgery and sterile duration times have more effects on the both objective functions of the mathematical model than other parameters of the problem. Furthermore, the obtained results show that the first objective function, which includes the total cost, will have the highest value when the delay cost parameters and the duration of the surgical operations increase. For the second objective function, which shows the complete time of the last operation, the most challenging situation occurs when the parameters of the duration of surgical operations and the sterile duration increase. Therefore, the most ideal situation from the point of view of the hospital manager is to reduce all three parameters, which will reduce, the total cost and the completion time of the last operation; Moreover, the results show, the separate costs of planning and scheduling of the operating room and the sterile department are more than when the planning and scheduling of the operating rooms and the sterile department were examined in an integrated manner. The results of this research can be used for the integrated planning and scheduling of the operating rooms and sterilization department in all hospitals as a suitable management tool.
Ehsan Yadegari; Akbar Alem Tabriz; Mostafa Zandieh
Abstract
Over the past decade, due to environmental laws and the competitive environment, development of an effective tactical plan for efficient and integrated supply chain and considering the responsibility of organizations to collect defective goods seems impossible. In this paper a mixed-integer linear programming ...
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Over the past decade, due to environmental laws and the competitive environment, development of an effective tactical plan for efficient and integrated supply chain and considering the responsibility of organizations to collect defective goods seems impossible. In this paper a mixed-integer linear programming is considered to mathematically model the essentially five stages along our supply chain network: suppliers, manufacturers, DCs, customers, and Dismantlers.Delivers raw materials from suppliers to factories and then through distribution centers, delivering the final product to customers. On the other hand, it simultaneously collects recycled goods from customers and enters the cycle of safe reconstruction or destruction.The aim of this model is minimizing the costs of establishing facilities at potential points as well as the optimal flow of materials in the network layers. Since the problem is NP-hard, to solve it, the cloud theory based simulated annealing algorithm has been used. We also used the tree-covering method to show the answer, which uses fewer arrays than other methods in the literature. To analyze the accuracy and speed of the proposed algorithm, we compared its performance with the genetic and simulated annealing algorithm. The results show that the cost function in the cloud-based refrigeration simulation algorithm provides more accurate answers than both algorithms studied in the literature. The results show that the cost function in the cloud-based simulated annealing algorithm provides more accurate answers than both algorithms studied in the literature. Also, in terms of convergence rate criterion, the proposed method has better position than the genetic algorithm, but it is not significantly different from simulated annealing algorithm.
Javad Behnamian; Mohammad Mehdi Bashar
Abstract
Cooperation in supply chain, due to conflicts in goals, is one the most important topics in SCM. With cooperation, players in supply chain echelons have agree with each other to play in supply chain game as a whole. To reach highest profit in the whole supply chain in cooperation condition, using game ...
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Cooperation in supply chain, due to conflicts in goals, is one the most important topics in SCM. With cooperation, players in supply chain echelons have agree with each other to play in supply chain game as a whole. To reach highest profit in the whole supply chain in cooperation condition, using game theory concept and side-contract of partnership in profits and considering marketing cost between manufacture and retailer, a cooperative multi-echelon supply chain is designed. In this paper, for the first time, mathematical modeling in fuzzy environment is presented, taking into account a discount that is approximated to the actual situation where the marketing cost is considered as triangular fuzzy number. The proposed model has been solved using Genetic algorithm (GA), simulated annealing algorithm (SA) and a hybrid algorithm based on GA-SA, for some random examples, and the model has been validated using GAMS software.
modeling and simulation
Navid Nadimi; Abbas Toloei Eshlaghy; Mohammad Ali Afshar kazemi
Abstract
With the tremendous progress in communications in the world, the transformation andbehavior of mobile operators and their digitalization, which in the past were onlyservice providers, as well as the creation of different experiences for customers, isinevitable.The purpose of this study is to create a ...
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With the tremendous progress in communications in the world, the transformation andbehavior of mobile operators and their digitalization, which in the past were onlyservice providers, as well as the creation of different experiences for customers, isinevitable.The purpose of this study is to create a hybrid simulation of systemdynamics and agent based model in order to analyze the revenue of the first operator inthe country to enter the field of digital platform and development of nativeapplications. Using the model proposed, first operators need to enter the digital areaand produce native applications was expressed. Then, the factors that affect the mobileecosystem which, affect the production of applications and the development ofrequired platforms were described. By utilizing hybrid simulation of system dynamicsand agent based modeling, the income of mobile operator in entering and not enteringthe digital arena and producing native applications were examined. The results showthat with the entry of the operator into the field of production of native applicationsand the adoption of digital approach, consumers tended to use more data services, butdue to different tariffs for data and voice, the operator's income up to 2 Next years willnot change much.
Mohsen Jami; Hamidreza Izadbakhsh; Alireza Arshadi Khamseh
Abstract
In the management of the blood supply chain network, the existence of a coherent and accurate program can help increase the efficiency and effectiveness of the network. This research presents an integrated mathematical model to minimize network costs and blood delivery time, especially in crisis conditions. ...
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In the management of the blood supply chain network, the existence of a coherent and accurate program can help increase the efficiency and effectiveness of the network. This research presents an integrated mathematical model to minimize network costs and blood delivery time, especially in crisis conditions. The model incorporates various factors such as the concentration of blood collection, processing, and distribution sites in facilities, emergency transportation, pollution, route traffic (which can cause delivery delays), blood type substitution, and supporter facilities to ensure timely and sufficient blood supply. Additionally, the model considers decisions related to the location of permanent and temporary facilities at three blood collection, processing, and distribution sites, as well as addressing blood shortages. The proposed model was solved for several problems using the Augmented epsilon-constraint method. The results demonstrate that deploying advanced processing equipment in field hospitals, concentrating sites in facilities, and implementing blood type substitution significantly improve network efficiency. Therefore, managers and decision-makers can utilize these proposed approaches to optimize the blood supply chain network, resulting in minimized network costs and blood delivery time.IntroductionOne of the most important aspects of human life is health, which has a significant impact on other aspects of life. In this study, a two-objective mathematical programming model is proposed to integrate the blood supply chain network for both normal and crisis conditions at three levels: blood collection, processing and storage, and blood distribution. The proposed two-objective mathematical model simultaneously minimizes network costs and response time. The model is solved using the augmented epsilon-constraint method. To enhance the responsiveness to patient demand in healthcare facilities and address shortages, the model considers the concentration of levels (collection, processing and storage, and distribution of blood to patients) in facilities, blood type substitution, and supporter facilities. In blood type substitution, not every blood type can be used for every patient. Among several compatible blood groups, there is a prioritization for blood type substitution, allowing for an optimal allocation of blood groups based on the specific needs.Materials and MethodsIn this research, a two-objective mathematical programming model is proposed to design an integrated blood supply chain network at three levels: collection, processing, and distribution of blood in crisis conditions. The proposed model determines decisions related to the number and location of all permanent and temporary facilities at the three levels of blood collection, processing, and distribution, the quantity of blood collection, processing, and distribution, inventory levels and allocation, amount of blood substitution, and transportation method considering traffic conditions. Achieving an optimal solution for the developed two-objective model, which minimizes both objective functions simultaneously while considering the trade-off between the objective functions, is not feasible. Therefore, multi-objective solution methods can be used to solve problems considering the trade-off between objectives. In this research, the augmented epsilon-constraint method is employed to solve the proposed two-objective mathematical model. In this method, all objective functions, except one, are transformed into constraints and assigned weights. By defining an upper bound for the transformed objective functions, they are transformed into constraints and solved.Discussion and ResultsAlthough the two-objective mathematical model is transformed into a single-objective model using the augmented epsilon-constraint method, this approach can still yield Pareto optimal points. Therefore, managers and decision-makers can create a balanced blood supply chain network considering the importance of costs and blood delivery time. Sensitivity analysis was conducted to examine the effect of changes in the weights of the objective functions and the blood referral rate (RD parameter) on the values of the objective functions for three numerical examples. With changes in the weights of the objective functions relative to each other, the trend of changes in the values of the first and second objective functions for all three solved problems is similar. Specifically, when reducing the weight of the first objective function from 0.9 to 0.1, the values of the first objective function increase, while the values of the second objective function decrease when the weight of the second objective function increases from 0.1 to 0.9. The total amount of processed blood in field hospitals and main blood centers was compared for equal weights and time periods for the three problems. Additionally, the amount of processed blood in field hospitals is significantly higher than in main blood centers. This indicates that eliminating the cost and time of blood transfer in field hospitals (due to the concentration of blood collection, processing, and distribution levels) results in an increased amount of processed blood compared to main blood centers (single-level facilities), ultimately leading to a reduction in network costs.ConclusionThis study presents a two-objective mathematical model for the blood supply chain network, integrating pre- and post-crisis conditions. Decisions are proposed for the deployment of four types of facilities, including temporary blood collection centers, field hospitals, main blood centers, and treatment centers, at three levels of blood collection, processing, and distribution. Additionally, inventory, allocation, blood group substitution, blood shortage, transportation mode, and route traffic (delivery delays) are considered for four 24-hour periods in the model. For the first time in this field, knowledge of concentration levels in facilities is utilized, with simultaneous existence of the three levels of blood collection, processing, and distribution in field hospitals. This problem is formulated in a mixed-integer linear programming model with two objective functions aiming to minimize system costs and blood delivery time. The proposed model is solved using the augmented epsilon-constraint evolution method. Sensitivity analysis is conducted for the weights of the objective functions, and additional experiments (RD parameter) are performed. The sensitivity analysis on the weights of the objective functions reveals that reducing the weight of the first objective function leads to a decrease in blood delivery time, while increasing the weight of the second objective function results in an increase in network costs. The investigation of the impact of reducing the amount of additional testing (RD parameter) on the values of the objective functions confirms that advanced equipment at the processing sites of field hospitals reduces network costs and blood delivery time.
supply chain management
Parisa Hosseini; Mehdi Seifbarghy
Abstract
One of the most critical decisions in the supply chain is pricing, playing a vital role in the profitability of the entire supply chain. In this research, a two-tier green supply chain is considered, comprising a producer and a retailer, where two types of products, standard and green, are produced. ...
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One of the most critical decisions in the supply chain is pricing, playing a vital role in the profitability of the entire supply chain. In this research, a two-tier green supply chain is considered, comprising a producer and a retailer, where two types of products, standard and green, are produced. The demand for products is determined as a certain linear function of product prices, delivery time in the online channel, the level of green quality, advertising intensity, and information tracing level. Green products are sold through the online channel, while standard products are distributed through traditional retail channels. The government provides subsidies for the production of green products and the implementation of blockchain technology. The decision-making problem is approached through two centralized and decentralized models. In the decentralized model, a Stackelberg game is employed, with the producer leading the decision-making process. In the centralized model, all supply chain members make decisions in a unified manner. The results indicate that the centralized model yields the highest profitability for the supply chain. Additionally, in the centralized model, all products are observed to have the lowest prices.
supply chain management
Mehdi Seifbarghy; Morvarid Yousefi
Abstract
The evaluation and selection of suppliers is a crucial issue in supply chain management. This problem has grown increasingly uncertain due to the influence of imprecise parameters, and various tools have been proposed for weighting criteria and evaluating supplier scores for each criterion. Neutrosophic ...
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The evaluation and selection of suppliers is a crucial issue in supply chain management. This problem has grown increasingly uncertain due to the influence of imprecise parameters, and various tools have been proposed for weighting criteria and evaluating supplier scores for each criterion. Neutrosophic numbers, a modern tool for handling uncertainty, ambiguity, and inconsistency, are introduced to tackle such challenges. As the most comprehensive form of non-classical logic after fuzzy and intuitionistic fuzzy logic, Neutrosophic logic assigns degrees of membership, non-membership, and hesitation independently between zero and one. In this research, the quality function deployment (QFD) technique is developed in a Neutrosophic environment to identify and weigh supplier evaluation criteria. Additionally, the Neutrosophic EDAS approach is proposed for selecting top suppliers. A numerical study of the pharmaceutical industry demonstrates that geographical location and supplier experience are the two most critical criteria, and the second supplier is chosen as the best.
Abstract
. The control and planning of operating rooms have became more and more important for hospital managers. Nowadays, the operating rooms, as key resources, often lead to a great time waste. So, appropriate scheduling of operations in order to increase the efficiency of the operating rooms, are significant ...
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. The control and planning of operating rooms have became more and more important for hospital managers. Nowadays, the operating rooms, as key resources, often lead to a great time waste. So, appropriate scheduling of operations in order to increase the efficiency of the operating rooms, are significant research topics in medical care. In this study, a new linear programming model has been developed for the assignment of patients to the operating rooms. Since the duration of surgical procedure is a stochastic parameter, in this paper a two-stage stochastic programming model has been presented in addition the deterministic model. These presented modelssimultaneously investigate the scheduling of surgeries and allocating them to the surgery rooms. The aim of this study is to minimize the cost of assigning patients to unspecialized rooms and reduce the cost of doctors’ idle time per working shifts by taking into account the constraints. The suggested models were applied for the surgery department of Ghaem hospital of Mashhad and solved by ILOG CPLEX 12.6.1 Microsoft Visual Studio environment. The results show that the deterministic proposed model increases the average efficiency of hospital to 38.275% and the stochastic model increases the average efficiency to 85.32%.
Seyed Hossein Razavi Hajiagha; Hadi Akrami; Shide Sadat Hashemi
Volume 11, Issue 31 , January 2014, , Pages 35-53
Abstract
Master production scheduling is a midterm phase in planning which translates the long term aggregate production planning to a plan which determines the scheduling and magnitude of different products production. This problem requires investigating a wide range of parameters about demand, manufacturing ...
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Master production scheduling is a midterm phase in planning which translates the long term aggregate production planning to a plan which determines the scheduling and magnitude of different products production. This problem requires investigating a wide range of parameters about demand, manufacturing resource usage and costs. Uncertainty is an intrinsic characteristic of these parameters. In this paper, a model is developed for master production scheduling under uncertainty where demands are considered as stochastic variables, while cost and utilization parameters are expressed as fuzzy numbers. A hybrid approach is also proposed to solve the extended model. The application of the proposed method is examined in a numerical example.
Magbsood Amiri
Volume 2, Issue 7 , December 2004, , Pages 37-55
Abstract
One of the most important issues in inventory control is determination of the reorder point. Defining parameters of this point are usually in forms of crisp or probability. In this article the parameters are considered as fuzzy numbers because of ambiguities in the real world. Then reorder point ...
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One of the most important issues in inventory control is determination of the reorder point. Defining parameters of this point are usually in forms of crisp or probability. In this article the parameters are considered as fuzzy numbers because of ambiguities in the real world. Then reorder point value is calculated using a cut concept extension principle fuzzy arithmetic and fuzzy trapezoidal numbers. Finally concluding remarks are given.
Ali Khatami Firoozabadi; Hossein Mohebbi; Mohammad Zarei Mahmoodabadi
Volume 8, Issue 21 , June 2011, , Pages 39-61
Abstract
Shortest-path problem is one of the well-known optimization problems that has been studied by many scientists in recent years. Applications of this problem such as transportation and communication are generally solved by Dijkstra's Algorithm (Labeling). In this paper, two separate scientific fields, ...
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Shortest-path problem is one of the well-known optimization problems that has been studied by many scientists in recent years. Applications of this problem such as transportation and communication are generally solved by Dijkstra's Algorithm (Labeling). In this paper, two separate scientific fields, electronics and operation research have been linked to each other and a new algorithm has been created for to find the optimization solution of a shortest- path problem by using electric networks and rules. The proposed algorithm can solve the shortest-path problem in directed graphs and no order ones, and also can solve the longest path problems in directed graphs.
In this algorithm, electrical network are used in a way that the resistance value of each branch is equal to each edge weights in the shortest-path problems. Then with using Ohm Law and Kirchhaffs Voltage Law (KVL), the current in each circuit cycle is calculated. Then the branches that contain the most passing current are specified, and according to Ohm’s Law, has the lowest resistance or weight. Thus, the shortest path in the network is achieved. Advantage of this algorithm is faster convergence to the answer and less computing time than the conventional method, especially in networks with more nods. The mentioned algorithm has been described for three examples.
mohammad rahim ramazanian; mohammad hasan gholizadeh; shiva shaban
Volume 11, Issue 29 , July 2013, , Pages 41-59
Abstract
In this paper, based on the background of cost-oriented assembly line balancing problems, a novel mathematical model for scheduling and balancing the assembly line is offered that with the combination of task sequence-dependent setup time, parallel stations and resource constraint, minimizes ...
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In this paper, based on the background of cost-oriented assembly line balancing problems, a novel mathematical model for scheduling and balancing the assembly line is offered that with the combination of task sequence-dependent setup time, parallel stations and resource constraint, minimizes the operational and investment costs of assembly system.
Due to the complexity of the problem, the equations have been proposed to reduce the number of variables of the model, and to achieve the optimal solution in reasonable time using exact algorithms. To clarify and explain the features of the model a numerical example have been used. Also the solutions obtained from model in several examples, have been analyzed using different indexes, and results indicative of appropriate performance of the model
alireza alinezhad; kavoos simiari
Volume 11, Issue 28 , April 2013, , Pages 41-60
Abstract
One of the important issues in project management is project portfolio selection. Issue of project selection and related activities is one of the important activities in most of organizations, especially contract companies and developing project centered companies. Issue of project choosing is a periodical ...
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One of the important issues in project management is project portfolio selection. Issue of project selection and related activities is one of the important activities in most of organizations, especially contract companies and developing project centered companies. Issue of project choosing is a periodical activity for choosing suitable portfolio from suggested projects or organizational in process projects which enforce organizational goals in a desirable manner , without spending additional sources or / and neglecting other limitations. . In project portfolio selection, the most important issue which bring up is that this portfolio should conclude which projects. As much as project portfolio selection be more advisable, fulfilment of organization duties would be more probable. In choosing this combination, distinguishing opportunities, assessing amount of project cooperation with goals and organization structure and expenses, profit analysis and project risks has a great importance.
In this research has been used from qualitative and quantitative assimilated approach for project portfolio selection. In a way that at first we use DEMATEL (Decision Making Trial and Evaluation Laboratory) method to selection criteria and then with use Data Envelopment Analysis and criteria that selected in previous stage, calculate the efficiency for each project and rank all of them
Abolfazl Kazemi; Pasha Pasha Fakhori; Ali Shakourloo,
Volume 13, Issue 38 , October 2015, , Pages 41-69
Abstract
Accurate scheduling of project is one of the main fundamentals and essential success factors of the project management and also accordance of planning and implementation is considered as the main subject of project management. In this paper, it is tried to propose a practical and suitable system in order ...
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Accurate scheduling of project is one of the main fundamentals and essential success factors of the project management and also accordance of planning and implementation is considered as the main subject of project management. In this paper, it is tried to propose a practical and suitable system in order to schedule various projects more accurately. Currently Program Evaluation and Review Technique (PERT) is used extensively for management of large- scaled projects. In traditional methods of PERT technique, implementation time of each activity is obtained in the form of definite figures or through Beta Distribution but in the real world; it is very hard to estimate the practical implementation time of each activity in an accurate way. By using fuzzy logic in order to overcome the problems related to uncertainty, fuzzy expert system is designed which many limitations and factors affecting the project completion time have been considered in this article. In this regard, output and input parameters of the system have been acquired by questionnaire and validation of the results has been analyzed. The obtained results indicate high power of the system in controlling different situations affecting the project completion time.
M. Momeni; A. Atashsooz
Volume 2, Issue 4 , March 2004, , Pages 41-74
Abstract
QFD starts with the house of quality (HOQ) which is a planning matrix translating the customer needs into measurable product technical requirements (PTRs). As with any other tool the quantum of benefits obtained from the use of QFD is proportional to the effectiveness of its use. A robust analytic method ...
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QFD starts with the house of quality (HOQ) which is a planning matrix translating the customer needs into measurable product technical requirements (PTRs). As with any other tool the quantum of benefits obtained from the use of QFD is proportional to the effectiveness of its use. A robust analytic method should consider the interrelationships among customer needs and PTRs while determining the importance levels of PTRs in the HOQ. This research employs the analytic network process (ANP) to fulfill this requirement. Furthermore the proposed analytic procedure should take into account the multi objective nature of the problem and thus incorporate other goals such as resource limitation extendibility and manufacturability of software technical requirements (STRs). This research presents a zero one goal programming methodology that includes importance levels of STRs derived using the ANP human resource limitation extendibility level and manufacturability level goals to determine the STRs to be considered in designing the software. Finally with regard to the result of the implemented model it is proposed some solutions for the company to reach a better software design to enhance the customer satisfaction.
Noor Mohammad Yaghubi; Roghayeh Sadat Kuchakzadeh
Volume 3, Issue 9 , June 2005, , Pages 41-56
Abstract
The reasons of organizations success of third millennium in competitiopns are motion toward knowledge management and knowledge base a supportive role of IT eases the process of knowledge management a makes it as a competitive advantage. Accordingly the authors has describe the supportive factors of IT ...
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The reasons of organizations success of third millennium in competitiopns are motion toward knowledge management and knowledge base a supportive role of IT eases the process of knowledge management a makes it as a competitive advantage. Accordingly the authors has describe the supportive factors of IT and its use in knowledge management process in this paper first come the knowledge management and TI discussions a then the role of IT in knowledge management is added. After that the parameters which have an important influence on knowledge management are stated. It supporters in making knowledge process in another theme discussion of the study and finally the role of it as a support and ease knowledge management process has been studied.
Mahmood Alborzi; Seyed Amir Reza Abtahi
Volume 4, Issue 13 , June 2006, , Pages 41-66
Abstract
This paper uses neural network to predict corrosion rate. Corrosion can not modeled easily, because of wide range of causes either known or unknown. In mechanistic approach, physical, chemical and electrochemical reactions and processes are considered to model and predict. ...
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This paper uses neural network to predict corrosion rate. Corrosion can not modeled easily, because of wide range of causes either known or unknown. In mechanistic approach, physical, chemical and electrochemical reactions and processes are considered to model and predict. But as stated before this models are not practically successful in prediction because of unknown parameters.
This paper uses genetic optimized neural network to predict corrosion rate. Among different neural networks, multi layer neural network with gradient descent learning algorithm has been selected. After developing the network, learning process has been done, using an oil refinery'S data. Then evaluation and test have been performed. After preparing the network Garson's algorithm and sensitivity analysis have been used for knowledge extraction.
According to results, neural network approach can predict corrosion rate with acceptable correlation coefficient (R) and mean squared error (MSE). Sensitivity analysis depicts the strength of each oil parameter influence on corrosion rate. Among these results, salt and sulphur are the most affecting parameters in corrosion rate.
Seddigheh Khorshid
Volume 8, Issue 18 , September 2010, , Pages 41-69
Abstract
Globalization and competitiveness have undergone earthquakes, tremors and aftershocks on global manufacturing environment, that have forced manufacturing organizations to restructure and change way of doing their business. Furthermore, manufacturing organizations have realized that agility is essential ...
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Globalization and competitiveness have undergone earthquakes, tremors and aftershocks on global manufacturing environment, that have forced manufacturing organizations to restructure and change way of doing their business. Furthermore, manufacturing organizations have realized that agility is essential to survive and competition. But, the ability to build agile manufacturing companies has developed less than anticipated. Because, manufacturing organization managers faced with this problem that how much agility an organization currently possess, determining how much is needed. In this article, an hierarchical model based on fuzzy weighted average technique in order to support managers' decisions for building manufacturing agile is Presented. This model can capture the vague and uncertainty of agility indicators, and the expert's knowledge and judgment. The model was performed in Khozestan's Steel industry.
Mehdi Yazdani; Mostafa Zandieh; Reza Tavakkoli-Moghaddam
Volume 12, Issue 33 , July 2015, , Pages 43-74
Abstract
In this paper, the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) with objective of minimizing the makespan is investigated. Under studied problem is NP-hard and mainly includes three sub-problems. The first one is to assign each operation to a machine out ...
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In this paper, the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) with objective of minimizing the makespan is investigated. Under studied problem is NP-hard and mainly includes three sub-problems. The first one is to assign each operation to a machine out of a set of capable machines, the second one is to determine a worker among a set of skilled workers for processing each operation on the selected machine and the third one deals with sequencing the assigned operations on the machines considering workers in order to optimize the performance measure. In this paper, we provide a mathematical model for this problem and then propose a hybrid meta-heuristic algorithm for solving the problem. The proposed hybrid algorithm uses variable neighborhood search and simulated annealing algorithms to search in the solution space. Computational study with randomly generated test problems is performed to evaluate the performance of the proposed algorithm. The results show the proposed algorithms are effective approaches for solving the DRCFJSP.
