Neda Nikakhtar; Shahram Saeidi
Abstract
The Cellular Manufacturing System (CMS) is one of the most efficient systems for production environments with high volume and product variety which takes advantage of group technology. In the cellular production system, similar parts called part families are assigned to a production cell having similar ...
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The Cellular Manufacturing System (CMS) is one of the most efficient systems for production environments with high volume and product variety which takes advantage of group technology. In the cellular production system, similar parts called part families are assigned to a production cell having similar production methods, and the needed machines are dedicated to cells. Determining part families and allocating the necessary machines to the production cell is known as the Cell Formation Problem (CFP) which is known as an NP-Hard problem. Safaei and Tavakkoli-Moghaddam (2009a) proposed a model that is widely used in literature which suffers some killer weaknesses highly affecting subsequent researches. In this paper, the mentioned model is modified and revised to fix these major issues. Besides, due to the NP-Hard nature of the problem, a meta-heuristic algorithm based on Gray Wolf Optimization (GWO) approach is also developed for solving the revised model on the sample examples and the results are compared. Simulation results indicated that the proposed method can reduce the total cost of the manufacturing system by 3% in comparison with the base model. Furthermore, simulation results of five sample problems indicate the better performance of the proposed method comparing with Lingo and PSO.
Seyed Mohammad Hadian; Hiwa Farughi; Hasan Rasay
Abstract
In this paper, a mathematical model is presented for the integrated planning of maintenance, quality control and production control in deteriorating production systems. The simultaneous consideration of these three factors improves the efficiency of the production process and leads to high-quality products. ...
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In this paper, a mathematical model is presented for the integrated planning of maintenance, quality control and production control in deteriorating production systems. The simultaneous consideration of these three factors improves the efficiency of the production process and leads to high-quality products. In this study, a single machine produces a product with a known and constant production rate per time unit and the production process has two operational states, i.e. in-control state and out-of-control state, and the probability of the state transition follows a general distribution. To monitor the process, sampling inspection is conducted during a production cycle and a proper control chart is applied. In the developed model, there is no restriction on the type of the control chart. Therefore, different control charts can be applied in practice for quality control. The lot size produced in each production cycle is determined with respect to the production rate of the machine and the proportion of conforming and non-conforming items produced in each cycle. In this study, preventive maintenance and corrective maintenance as perfect maintenance actions and minimal maintenance as imperfect maintenance action are applied to maintain the process in a proper condition. The objective of the integrated model is to plan the maintenance actions, determine the optimal values of the control chart parameters and optimize the production level to minimize the expected total cost of the process per time unit. To evaluate the performance of this model, a numerical study is solved and a sensitivity analysis is conducted on the critical parameters and the obtained results are analyzed.
Ommolbanin Yousefi; Nooshin Shirani
Abstract
Warranty and maintenance contracts play an important role in product life cycle. Different failures which occur during useful life cycle of products in additional reducing reliability, make expense for consumer and service agent. This study considers warranty periods and maintenance services costs ...
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Warranty and maintenance contracts play an important role in product life cycle. Different failures which occur during useful life cycle of products in additional reducing reliability, make expense for consumer and service agent. This study considers warranty periods and maintenance services costs from service agent and consumer viewpoints under two-dimensional warranty policy. By regarding agent service and consumer decisions, the interactions between them are modeled during the base warranty and extended warranty periods. Maintenance policies are performed as preventive maintenance (PM) in specific interval with fixed level, and corrective maintenance (CM) is carried out as home and road repairs. Finally, for making equilibrium between profits of consumers with different usage rates and agent services, preventive maintenance number and warranty services price are investigated by the Stackelberg equilibrium. A real case study from a truck after sales services agent of Iran is presented to illustrate the application of the proposed model.
Ali Bozorgi Amiri; Mostafa Akbari; Iman Dadashpour
Abstract
Quick response to the relief needs right after disasters through efficient emergency logistics distribution is vital to the alleviation of disaster impact in the affected areas. In this paper, by focusing on the distribution of relief commodities after disaster, the best possible allocation for the affected ...
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Quick response to the relief needs right after disasters through efficient emergency logistics distribution is vital to the alleviation of disaster impact in the affected areas. In this paper, by focusing on the distribution of relief commodities after disaster, the best possible allocation for the affected areas is specified and shortest path to vehicle transporting is determined. The objective of the proposed model is the minimization of the maximum distance traveled by each vehicle in order to achieve fairness in response to the wounded. In our proposed model, the location of demand is uncertain and determined by the simulation approach. The proposed approach solves the proposed model and determines appropriate allocation and best route for vehicles according to the allocation, simultaneously. Consequently, using genetic algorithm with two-part chromosome structure in routing and allocation problems. Computational results show the efficiency and effectiveness of the proposed model and algorithm for solving real decision-making problems.
Alireza Ariyazand; Hamed Soleimani; Farhad Etebari; Esmaeil Mehdizadeh
Abstract
Scheduling is a vital part of daily life that has been the focus of attention since the 1950s. Knowledge of scheduling is a very important and applicable category in industrial engineering and planning of human life. In the field of education, scheduling, and timetabling for best results in classroom ...
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Scheduling is a vital part of daily life that has been the focus of attention since the 1950s. Knowledge of scheduling is a very important and applicable category in industrial engineering and planning of human life. In the field of education, scheduling, and timetabling for best results in classroom teaching is one of the most challenging issues in university programming. As each university has its own rules, policies, resources, and restrictions a unique model of scheduling and timetabling cannot implement. This can cause more complexity and challenging point which needs to be considered scientifically. This study presents a sound scientific model of timetabling and classroom scheduling to improve faculties’ desirability based on days, times, and contents preferences. A sample in Parand branch of Islamic Azad university chooses using the Bat metaheuristic algorithm. By considering the limitations, some unchangeable constraints regarding the specific rules and minimal linear delimitation of the soft constraints of the model, using the appropriate meta-heuristic algorithm to reduce the model run time to a minimum. The results show that the algorithm achieves better results in many test data compared to other algorithms due to meeting many limitations in the problem coding structure. The Bat algorithm is compared with four other algorithms while comparing the results of solving the proposed mathematical model with five metaheuristic algorithms to evaluate the performance. In this research, a multi-objective model is presented to maximize the desirability of professors and to solve the model using Bat, Cuckoo Search, Artificial bee colony, firefly, and Genetic algorithms. In this research 40 different runs of each algorithm were compared, and conclusions were drawn. Modeling has been solved with GAMS and MATLAB software and using the bat meta-heuristic algorithm. It is concluded that in this model, the bat algorithm is the most appropriate algorithm with the shortest time, which has caused the satisfaction of the professors of the educational departments of this academy.
Milad Hematian; Mirmehdi Seyyedesfahani; Iraj Mahdavi; Nezam Mahdavi Amiri; Javad Rezaeian
Abstract
One of the most important aspects of human resource management is the allocation of the workforce to activities. Human resource assignment to project activities for its scheduling is one of the most real and common issues in project management and scheduling. This becomes even more significant when human ...
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One of the most important aspects of human resource management is the allocation of the workforce to activities. Human resource assignment to project activities for its scheduling is one of the most real and common issues in project management and scheduling. This becomes even more significant when human resource assignment to multiple projects simultaneously is considered. On the one hand, workforces can have multi skills due to technological and scientific development so that they can be assigned to project activities based on their skill level. On the other hand, the learning effect is also taken into account to make the model more realistic. These factors can affect completion time, total cost and execution quality of projects. In this study, a multi-objective optimization model for multi-project scheduling and multi-skilled human resource assignment problem based on the learning effect and activities' quality is presented. A mixed-integer linear programming model (MILP) is developed for the proposed problem and solved by the ε-constraint method in GAMS software. Managers can select a solution based on their priority. Finally, a sensitivity analysis is done on the learning and forgetting effect to investigate their impacts on each objective function.
Alireza Abbaszadeh Molaei; Abdollah Arasteh; Mir Saman Pishvaee
Abstract
In today’s growing world, the Green Supply Chain (GSC) is a new approach to include environmental impacts and economic goals in a supply chain network. This paper continues previous research studies by designing a new green supply chain network considering different social, economic, environmental, ...
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In today’s growing world, the Green Supply Chain (GSC) is a new approach to include environmental impacts and economic goals in a supply chain network. This paper continues previous research studies by designing a new green supply chain network considering different social, economic, environmental, service level, and shortage aspects. This study introduces a fresh, comprehensive tradeoff model that considers factors such as overall expenses, quality of service, environmental pollution levels, and societal impacts within a sustainable supply chain. The proposed model is formulated as a multi-product multi-objective mixed-integer programming model to assist in planning a green supply chain. The suggested model has three objective functions: maximizing social responsibility, minimizing the cost of carbon dioxide (CO2) emissions, and minimizing economic costs. The model allows for shortages in the form of backorders and seeks to maximize service level in addition to the mentioned objective functions. Robust Possibilistic Programming (RPP) was employed to deal with the problem's uncertain input parameters in the solution approach. Also, a multi-objective model of the problem was solved using Fuzzy Goal Programming (FGP). To examine and evaluate the model in a simple framework, the proposed mathematical model of the problem was implemented in an industrial unit in the real world, and the results obtained from it were analyzed. Among the results that the output of the model provides to managers and decision-makers, it is possible to mention the determination of the optimal amount of production of each product in the manufacturing plants, quantity of products and parts transported between facilities, and also the determination of the of network's carbon emissions which is equal to 51.59 tons.
Seyyed Abdollah Razavi; Hossein Motavali
Abstract
The oil and gas industry is probably the most important industry in the world. By growing demands of energy, the need for executing oil and gas projects becomes more than ever. Mega projects in this industry have certain characteristic such as being investment intensive, multi objective, owners, investors, ...
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The oil and gas industry is probably the most important industry in the world. By growing demands of energy, the need for executing oil and gas projects becomes more than ever. Mega projects in this industry have certain characteristic such as being investment intensive, multi objective, owners, investors, vendors and contracts, risk and uncertainties and etc. Nowadays, knowledge-based organizations play important role in oil and gas industry. Due to the expansion and growth of project-oriented knowledge-based organizations, one of the important issues in these organizations is the optimal selection of the project portfolio. The problem is how to choose the optimal project portfolio. In this research you will find how to establish an optimal project portfolio and with respect to organization constraints. At the end, the methodology is applied as a case study in TEC company- an active project-oriented knowledge-based organization in upstream oil and gas industry in Iran.
Sara Salimi; Ali Hajiha; Hamidreza Saeednia; Kambiz Heidarzadeh
Abstract
The purpose of this study is to design a post-purchase regret model and determine online business strategies. Regret is a state of mind in which the customer is hesitant to buy a product or service. This hesitation can be due to paying a high price for the quality received, comparing the quality of the ...
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The purpose of this study is to design a post-purchase regret model and determine online business strategies. Regret is a state of mind in which the customer is hesitant to buy a product or service. This hesitation can be due to paying a high price for the quality received, comparing the quality of the goods or services received with competing companies, or the result of various risks that may arise in online shopping. To design the regret model, the qualitative research method was used utilizing the grounded theory strategy and the Strauss-Corbin systematic design. The sampling method was judgmental and to collect information and achieve theoretical saturation, 14 semi-structured interviews were conducted with university professors and managers of online commerce and web-based businesses. The key points of the interviews were analyzed during the three stages of open, axial, and selective coding. For the validity and reliability of the research, the members` review, participatory, triangulation, and retest methods were used. The results were extracted in a paradigm model with 20 categories and 76 concepts. The Delphi method was used to prioritize the constructive factors of the model and the opinion of experts was determined in 2 stages and converged with a standard deviation of less than 0.05. The results of the research help online business activists to gain an accurate understanding of post-purchase regrets in online shopping behavior.
Morteza Karimi; Tahmoores Sohrabi; Hasan Mehrmanesh
Abstract
In this study, the problem of simultaneous determination of order acceptance, scheduling and batch delivery considering sequence-dependent setup and capacity constraint has been presented. This problem is a combination of the three problems of order acceptance, scheduling and batch delivery. The most ...
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In this study, the problem of simultaneous determination of order acceptance, scheduling and batch delivery considering sequence-dependent setup and capacity constraint has been presented. This problem is a combination of the three problems of order acceptance, scheduling and batch delivery. The most important innovation of this research is the simultaneous optimization of profits and the total weighted earliness and tardiness as two conflicting objectives in the problem of combining order, scheduling and batch delivery. Another innovation of this research is the use of multi-objective Grey Wolf Optimization (GWO) algorithm, which has not been used in studies of this field so far. It has also been shown that the multi-objective Grey Wolf Optimization algorithm is comparable to the exact solution methods. The second part of the numerical results compares the results of the ε-constraint method, NSGA-II and the multi-objective Grey Wolf Optimization algorithm. The results of this section show that by increasing the scale of the problem, the efficiency of the multi- objective Grey Wolf Optimization algorithm is better displayed, and in general, this method has a significant advantage relative to NSGA-II and ε-constraint in terms of DM, SNS and NPS indicators. Also, the solving time of this method is very shorter than that of the ε-constraint. Therefore, from a managerial point of view, a tool called the multi-objective Grey Wolf Optimization algorithm can be used as an efficient tool for supply and production managers, which is able to provide several optimal solutions with different profits, earliness and tardiness.
Mehran Khalaj; Fereshteh Khalaj
Abstract
This paper presents an approach for the fault diagnosis in the state of fault in a machine by using a combination of the Dempster–Shafer (D-S) theory. At the first, feature extractions in each state have been combined based on evidential reasoning (ER) using kind of sensor information such as ...
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This paper presents an approach for the fault diagnosis in the state of fault in a machine by using a combination of the Dempster–Shafer (D-S) theory. At the first, feature extractions in each state have been combined based on evidential reasoning (ER) using kind of sensor information such as vibration, acoustic, pressure, and temperature, to detect and diagnose machine failure. Then, the main fusion will be obtained. In this process, the mass function assignment of any sensors to feature extraction, respectively, in every state of the machine is fused to indicate state quality. Within this framework, we propose a new way for main fusion to derive a consensus decision for fault diagnosis. In this paper, an approach developed to apply the evidential reasoning by defining adaptively weights into the improvement of the D–S evidence theory instead of the probability theory and the D–S evidence theory alone. Instead of using the evidential reasoning approach, this new approach applies entropy weighting in the D-S theory, in which all available data are used for making a decision. Entropy weighting can measure the uncertainty level of the fault decision and assist in obtaining a less uncertain fault decision. It is defined adaptively weights based on ambiguity measures associated with information obtained from each sensor. The ambiguity measure is defined by Shannon’s entropy. Many industries use old machines due to cost savings or lack of purchasing power. Maintenance policies in these factories are based on determining their fault experimentally and traditionally. Therefore, the main goal of this paper uses the improved evidence reasoning algorithm using a kind of sensor information to carry out fault diagnosis in these industrials. Then, a numerical example and a case study involving the ball mill machine in fault diagnosis are presented to show the rationality and efficiency of the proposed method.
Mojtaba Sedighi; Mahdi Madanchi Zaj
Abstract
Forecasting the stock price index volatility is considered a strategic and challenging issue in the stock markets, and it is momentous for traders and investors in the decision-making process. Hence, the presentation of an efficient model for forecasting the stock price index volatility is a crucial ...
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Forecasting the stock price index volatility is considered a strategic and challenging issue in the stock markets, and it is momentous for traders and investors in the decision-making process. Hence, the presentation of an efficient model for forecasting the stock price index volatility is a crucial and hard task because stock market data and price fluctuations have high volatility and nonlinearity characteristics. To beat this challenge, this paper proposes a new hybrid model by applying artificial intelligence algorithms to forecast the stock price index. It incorporates four phases to provide a dynamic and exact model: (1) Select popular and key technical indicators as input variables (2) Apply Adaptive Neuro-Fuzzy Inference System (ANFIS) for designing a substructure to provide a high-quality and quick solution (3) Use Modified Particle Swarm Optimization (MPSO) to enhance predictive accuracy by simultaneously and adjusting the length of each interval in the discourse universe and the appropriate degree of membership (4) Employ Parallel Genetic Algorithm (PGA) to solve complex issues with computational weight optimization and adjusting the decision vectors employing genetic operators. The stock market data of “Tehran Stock Exchange (TSE)” from 01/01/2011 to 31/12/2021 are utilized to examine the functionality of the proposed model. In comparative assessments, the overall performance of the ANFIS-MPSO-PGA model based on 5 criteria achieved 81.45%, which was significantly better than other methods.
Sajed Rastbin; Mehrdad Gholami Shahbandi; Pouya Soudmand
Abstract
Fast growth of motorized transportation infrastructures in the cities is a consequence of the urbanization process. Despite the undeniable benefits of the developments, some unwelcome social-environmental damages have been occurred. On top of the list, the movements of the pedestrians and their participation ...
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Fast growth of motorized transportation infrastructures in the cities is a consequence of the urbanization process. Despite the undeniable benefits of the developments, some unwelcome social-environmental damages have been occurred. On top of the list, the movements of the pedestrians and their participation in social activities have dramatically reduced as a result of the vehicles dominancy. Pedestrianization and walking-friendly schemes are the key answer to preserve the valuable element of the urban lifestyle. This need motivated the researchers to study and propose mathematical methods to model the dynamics and behavior of the pedestrians in response to their surroundings. However, most of the models in the literature are suitable for limited small-size area and cannot be applied for a large scale urban zone. In this paper, a fuzzy macroscopic pedestrian assignment model is proposed which is applicable for a large scale network and useful for urban master plans as a decision making framework. In addition, a bi-level mixed integer programming model is presented to optimize the pedestrian walking network via selecting some projects on the network, considering the behavior of the pedestrians. Finally, the problem is solved for a large scale pedestrian network in the city of Tehran. The results show the efficiency of the algorithm where spending half of the maximum possible cost has led to a welfare gain of 82.6 percent. The problem was efficiently solved within 12.5 days which is fairly acceptable for the strategic planning of such a large scale network. The numerical results verify the necessity of the model for urban master plan horizon.
Alireza Homayounmehr; Taha-Hossein Hejazi
Abstract
The management and design of supply chain networks in various dimensions are so critical today that managers' decisions significantly impact the configuration and flow of material in the network. Above all, supply chain management intends to reduce costs. The inability to accurately predict certain features, ...
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The management and design of supply chain networks in various dimensions are so critical today that managers' decisions significantly impact the configuration and flow of material in the network. Above all, supply chain management intends to reduce costs. The inability to accurately predict certain features, such as demand, can complicate the cost estimation process. To that end, an essential parameter is the reliability of supply chain networks. Considering the reliability of the supply chain network brings the model closer to reality, and the wellness or failure of its elements under different scenarios increases the enthusiasm to face unpredictable events in managers and helps network performance. Furthermore, appropriate management and design of the supply chain network can increase customer satisfaction and reduce costs in the long term. In this research, a four-tier supply chain network was designed to reduce the costs through a two-stage stochastic programming attitude. The combined metaheuristic method (genetic and simulated annealing algorithms) was used to solve the model. By treating the reliability of entities and routes and its effect on reducing cost as an essential criterion in the mentioned problem, it was showed that a reliable system has lower costs than an unreliable system.
Mohammad Reza Zahedi; Ehsan Vaziri Godarzi
Abstract
The appropriate organizational structural capital is one of the most important issues to emerge innovation in knowledge-based companies. The purpose of this paper is to design and implement a structural capital model in knowledge-based companies. The research is developed based on qualitative and quantitative ...
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The appropriate organizational structural capital is one of the most important issues to emerge innovation in knowledge-based companies. The purpose of this paper is to design and implement a structural capital model in knowledge-based companies. The research is developed based on qualitative and quantitative research methods. Firstly, the paper has used the Grounded Theory method to develop the structural capital model based on the 10 experts' interview data. The experts are related to the organizational structural capital subject. Secondly, the model is applied to a knowledge-based company. Therefore, the re-searcher-made questionnaire is used to assess the status of structural capital in the knowledge-based company. So, the reliability was estimated at 0.94%. This paper presents a model for measuring structural capital in knowledge-based companies. The nature of knowledge-based companies has made it necessary to utilize these organizations' measuring to examine the status of infrastructure, processes, and all elements of structural capital.
Alireza Ariyazand; Hamed Soleimani; Farhad Etebari; Esmaeil Mehdizadeh
Abstract
Scheduling and timetabling for university system have been a source of attention and an important challenge for the people in charge of administrations. The regulations and infrastructures are very diverse between universities, making it impossible to come up with a universal model for all. We, in this ...
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Scheduling and timetabling for university system have been a source of attention and an important challenge for the people in charge of administrations. The regulations and infrastructures are very diverse between universities, making it impossible to come up with a universal model for all. We, in this research, focused on coming up with an algorithm to help with timetabling of class courses for Islamic Azad university of Robat Karim. Our goal was to define an algorithm that could improve teacher satisfaction, and overall efficiency of the university timetabling. Instead, we managed to come up with an efficient algorithm.This research considers different factors such as teacher satisfaction, knowledge and skillset, categorizes students based on undergraduate versus post graduate degree, their research background, their scores and finally student satisfaction as well. This multi-objective mathematic model accounts for all the rules, regulations, and limitations of the university setting while following challenging confinements that guarantee the feasibility of the solution. Using metaheuristic algorithm of Whale and Genetic, while avoiding any breach of the soft limitations, we managed to come up with a system that provides the most satisfaction between the teachers and students. In our research, we compared Whale and Genetic algorithm with 4 other metaheuristic algorithms. We concluded that the results of Whale and Genetic algorithm are superior to other algorithms in regards to: Improved function goals, less run time, more Pareto front averages, more efficient solutions and results.
Ali Shahabi; Sadigh Raissi; Kaveh Khalili-Damghani; Meysam Rafei
Abstract
Avoiding the passengers extra waiting time is a vital task for rail planners. The current research focused on minimizing the passenger waiting time on the presence of real frequently random occurred disturbances. Details of the proposed model are on the 1st line of Tehran underground rail rapid transit. ...
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Avoiding the passengers extra waiting time is a vital task for rail planners. The current research focused on minimizing the passenger waiting time on the presence of real frequently random occurred disturbances. Details of the proposed model are on the 1st line of Tehran underground rail rapid transit. All fitness functions are validated using the analysis of variance (ANOVA) by applying the hypothesis testing method. Also, a validated discrete-event computer simulation model is applied to examine the average waiting time per passenger as the key performance measure under different scenarios generated using full factorial design of experiments. The validity of the obtained optimal solution, i.e., train headway times is confirmed at a 95% level of reliability. Also, simulation outcomes indicated that the proposed response surface meta-model could efficiently provide a more reliable train operation plan to ensure a desirable level of system resiliency on the presence of random disturbances. The numerical results indicated that wait time could be reduced by 14.8% for passengers as compared with the baseline train headway plan.
Omid Shafaghsorkh; Ashkan Ayough
Abstract
The purpose of this systematic review is to identify and categorize the application of soft operations research methods in healthcare settings. A systematic review was conducted to identify published papers on the application of soft operations research methods in the healthcare setting, using Google ...
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The purpose of this systematic review is to identify and categorize the application of soft operations research methods in healthcare settings. A systematic review was conducted to identify published papers on the application of soft operations research methods in the healthcare setting, using Google Scholar, Scopus, PubMed, Emerald, Elsevier, Web of Science, and ProQuest databases through December 2021. A total of 69 papers met our selection criteria for the systematic review. Soft operations research methods were used in a wide range of healthcare fields, including healthcare management, health informatics, e-health, and medical education, for identifying requirements, problem-solving, system design and implementation, process improvement, policymaking, knowledge management, and managing resilience, and marketing. This study contained restrictions on access to the full text of some articles and dissertations that had little impact on the study’s quality. The present study demonstrates the use of soft operations research methods in various areas of the healthcare system to better understand problematical situations. This paper can help to use soft operations research methods further in the healthcare problems, especially in the design and implementation of e-health and emerging new technology.
S. Farid Mousavi; Adel Azar; S Hamid Khodadad
Abstract
Considering the role and importance of innovation in the performance of organizations in general and banking institutions in particular, the current work aims at identifying effective factors in the success of innovation management system in Iranian Banks, about which exists a scarcity of research in ...
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Considering the role and importance of innovation in the performance of organizations in general and banking institutions in particular, the current work aims at identifying effective factors in the success of innovation management system in Iranian Banks, about which exists a scarcity of research in comprehensively identifying these organizational factors. Having examined several potentially suitable research methodologies, the Grounded Theory is chosen as a suitable approach to determine a comprehensive understanding of the main drivers of innovation management success in Iranian Banks. Theoretical and snowball sampling are used to recruit fifteen participants from across the country. The result of this study is a theory that explains the main drivers of innovation management success in Iranian banks. Innovation supportive leadership, market and customer orientation, information technology management, intellectual opportunities, as well as innovation opportunities and process management are the main factors for innovation management success in Iran’s banking industry. These factors contribute to the common factors mentioned by other studies, including communication, cost, and HR management, and offer a more specific approach to innovation management. Findings can help banks in the evaluation of effective factors in innovation management and provide the necessary ground for designing practices for improvement.
Mojtaba Salehi; Hamid Tikani
Abstract
This paper introduces a two stage stochastic programming to address strategic hub location decisions and tactical flight routes decisions for various customer classes considering uncertainty in demands. We considered the airline network with the arc capacitated single hub location problem based on complete–star ...
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This paper introduces a two stage stochastic programming to address strategic hub location decisions and tactical flight routes decisions for various customer classes considering uncertainty in demands. We considered the airline network with the arc capacitated single hub location problem based on complete–star p-hub network. In fact, the flight routes are allowed to stop at most two different hubs. The first stage of the model (strategic level) determines the network configuration, which does not change in a short space of time. The second stage is dedicated to specify a service network consists of determining the flight routes and providing booking limits for all itineraries and fare classes after realization of uncertain scenarios. To deal with the demands uncertainty, a stochastic variations caused by seasonally passengers’ demands through a number of scenarios is considered. Since airline transportation networks may face different disruptions in both airport hubs and communication links (for example due to the severe weather), proposed model controls the minimum reliability for the network structure. Due to the computational complexity of the resulted model, a hybrid algorithm improved by a caching technique based on genetic operators is provided to find a near optimal solution for the problem. Numerical experiments are carried out on the Turkish network data set. The performance of the solutions obtained by the proposed algorithm is compared with the pure GA and Particle Swarm Optimization (PSO) in terms of the computational time requirements and solution quality.
Mohammad Javad Jafari; M. J. Tarokh; Paria Soleimani
Abstract
Customer churn prediction has been gaining significant attention due to the increasing competition among mobile service providers. Machine learning algorithms are commonly used to predict churn; however, their performance can still be improved due to the complexity of customer data structure. Additionally, ...
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Customer churn prediction has been gaining significant attention due to the increasing competition among mobile service providers. Machine learning algorithms are commonly used to predict churn; however, their performance can still be improved due to the complexity of customer data structure. Additionally, the lack of interpretability in their results leads to a lack of trust among managers. In this study, a step-by-step framework consisting of three layers is proposed to predict customer churn with high interpretability. The first layer utilizes data preprocessing techniques, the second layer proposes a novel classification model based on supervised and unsupervised algorithms, and the third layer uses evaluation criteria to improve interpretability. The proposed model outperforms existing models in both predictive and descriptive scores. The novelties of this paper lie in proposing a hybrid machine learning model for customer churn prediction and evaluating its interpretability using extracted indicators. Results demonstrate the superiority of clustered dataset versions of models over non-clustered versions, with KNN achieving a recall score of almost 99% for the first layer and the cluster decision tree achieving a 96% recall score for the second layer. Additionally, parameter sensitivity and stability are found to be effective interpretability evaluation metrics.
Ruhollah Ebrahimi Gorji; Hamed Soleimani; Behrouz Afshar-Nadjafi
Abstract
In today's competitive market, reducing costs and time is one of the most important issues that has occupied the minds of managers and researchers. This issue is especially important in the field of supply chain management and transportation because by reducing time and cost, manufacturers and service ...
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In today's competitive market, reducing costs and time is one of the most important issues that has occupied the minds of managers and researchers. This issue is especially important in the field of supply chain management and transportation because by reducing time and cost, manufacturers and service providers can gain a competitive advantage over competitors. Accordingly, vehicle routing issues are one of the most important issues in this field because it is directly related to the time of service or product delivery and also by optimizing the network, reduces the cost of the entire network. Therefore, in this study, the intention was to evaluate the problem of vehicle routing (trucks) by considering the time constraints and using a multi-objective approach. Therefore, we discussed each of the factors separately based on the issue. The results of this study show. In this research, the model with two objective functions will be solved by two metaheuristic algorithms NSGA-II and MOPSO Managers are concerned with time and cost management in today's competitive markets, which is seen as a source of competitive advantage. The present study aims to find a solution to a bi-objective function model by employing two metaheuristic algorithms, NSGA-II and MOPSO. Additionally, a criterion for comparing algorithms is presented. The findings show that the MOPSO algorithm yields the optimal solution. The contribution of the present study in comparison with other previous studies can be summarized as follows: Environmental protection based on reducing pollution and its effects as well as reducing costs. Finding the desired route taking into account the complexity and difficulty of the route. Managers are concerned with time and cost management in today's competitive markets, which is seen as a source of competitive advantage. The present study aims to find a solution to a bi-objective function model by employing two metaheuristic algorithms, NSGA-II and MOPSO. Additionally, a criterion for comparing algorithms is presented. The findings show that the MOPSO algorithm yields the optimal solution. The contribution of the present study compared to other previous studies can be environmental protection and cost reduction that the two factors are compared and the results of the two methods are analyzed.
Mozhde Nikounam Nezami; Abbas Toloie Eshlaghy; Seyed-Javad Iranban
Abstract
The primary goal of this study is to design an agent-based model of the supply chain for perishable goods during the occurrence of specific disruptions. This study is practical in terms of aim and qualitative in terms of data collection method. To validate the model, the views of the statistical population ...
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The primary goal of this study is to design an agent-based model of the supply chain for perishable goods during the occurrence of specific disruptions. This study is practical in terms of aim and qualitative in terms of data collection method. To validate the model, the views of the statistical population including prominent university professors and manufacturers of perishable goods and experts with experience and expertise in the area of specific disruptions of the perishable goods supply chain were used. Additionally, the snowball method was used to select the sample. In total, the views of 18 experts were used. Agent-based modeling was done using NetLogo software. In this modeling, all supply chain disruptions of perishable goods such as disruptions at the macro level (change in consumer behavior), demand, production, supply, transportation, information, and Financial were considered. Also, according to each disruption, strategies to mitigate the effects such as blockchain, robotics, etc. were determined. The results of agent-based modeling show that the simultaneous use of different strategies in the perishable goods supply chain during the occurrence of specific disruptions significantly reduces the effects of specific disruptions on the perishable goods supply chain. Vaccination along with the application of other strategies such as the use of blockchain, robotics, discounts, subsidy, online purchase methods, non-cash payment methods, awareness of product safety, green packaging, and employee safety and health have significantly reduced the effects of specific disruptions on the perishable non-necessary goods supply chain. In addition, according to the findings of the research, among the various strategies, the discount has played the most significant role in reducing the influences of specific disruptions on the supply chain of non-necessary perishable goods.
Iman Baradari; Maryam Shoar; Navid Nezafati; Mohammadreza Motadel
Abstract
The importance of IT services in the life of businesses has led organizations to seek continuous evaluation of the quality of their IT services. In this regard, IT service management best practices such as Information Technology Infrastructure Library (ITIL), have introduced several processes for management ...
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The importance of IT services in the life of businesses has led organizations to seek continuous evaluation of the quality of their IT services. In this regard, IT service management best practices such as Information Technology Infrastructure Library (ITIL), have introduced several processes for management of IT services and defined different KPIs for evaluation of each process, so that organizations can evaluate and analyze the quality of their IT services through these KPIs. Despite this fact that evaluation of each ITIL process using mentioned KPIs requires considerable time and money, organizations are looking for solutions to invest on the most effective KPIs to improve their ITSM processes in pursuit of their business requirements. Although there are some researches over process evaluation methods in different areas, there is no scientific research in ITSM process evaluation. This study proposes the unique method for ITSM evaluation using ITIL KPIs based on defined critical success factors (CSF). In addition to that, Simultaneous Evaluation of Criteria and Alternatives (SECA) model as one of the newest MCDM methods has been used for KPI prioritization. Based on the results, we recommended the order of KPIs in ITSM process performance evaluation. This research helps organizations to improve their ITSM processes by investment on the most effective KPIs.
Sha-aban Ali Hoseinpour
Abstract
In order to be competitive, it is an obligation for companies and service centers to identify, evaluate and control risk and environmental aspects of their activities. Due to technical and financial constraints, it is required to prioritize the risks and control measures with greater accuracy. In the ...
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In order to be competitive, it is an obligation for companies and service centers to identify, evaluate and control risk and environmental aspects of their activities. Due to technical and financial constraints, it is required to prioritize the risks and control measures with greater accuracy. In the framework of the HSE-MS system, for the first time, risk evaluation of industrial activities and services, has been implemented using fuzzy Quality Function Deployment. In this approach, characteristics such as mutual effects of different risks and environmental aspects of industrial activities, risk estimation, and positive and negative aspects of activities have been considered in RPN computation. The application of fuzzy logic reduces the ambiguity of the linguistic parameters. In the case study of the Iran barrit falat it appears, that operation and impact of risk assessment methods and environmental aspects of activities, evaluation criteria and the priority actions has been performed more precisely in comparison with traditional methods of risk assessment.