ORIGINAL_ARTICLE
Evaluating Parameter Estimation Effect on the Polynomial Profile Monitoring Methods’ Phase II Performance
In some statistical process monitoring applications, the quality of a product or process can be determined by a linear or nonlinear regression relationship called "profile". Basically, standard monitoring methods involve two phases: Phase I and Phase II. Usually, it is assumed that the process parameters are known, however this condition in many applications is not met and parameters are estimated using the in-control data set collected in Phase I. The present study evaluates and compares some Phase II control chart approaches for monitoring the second order polynomial profiles when the process parameters are estimated. These methods includes Orthogonal, MEWMA and dEWMA-OR control charts. The performance of each control chart is measured in terms of ARL, SDRL, AARL and SDARL metrics using Monte Carlo simulation approach. The results showed that the in-control and out-of-control performance of control charts is strongly affected by parameter estimation, especially when only a few Phase I samples are used to estimate the parameters. Moreover, the superior overall performance of the Orthogonal method rather than the other competing methods is shown. Furthermore, we concluded that F estimation method leads to better performance of control charts in Phase II.
https://aie.ut.ac.ir/article_84376_5dd2a90a542abd031d60755003a0d7c9.pdf
2021-04-01
133
150
10.22059/jieng.2021.326559.1785
Profile monitoring
Polynomial profile
Estimation Effect
Control chart
Run Length
Statistical Process Control
Zohre
Ghasemi Eshkaftaki
zohre.ghasemi@in.iut.ac.ir
1
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran.
AUTHOR
Ali
Zeinal Hamadani
hamadani@cc.iut.ac.ir
2
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran.
AUTHOR
Ahmad
Ahmadi Yazdi
a.ahmadiyazdi@yazd.ac.ir
3
Department of Industrial Engineering, Yazd University, Yazd, Iran
LEAD_AUTHOR
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53
ORIGINAL_ARTICLE
Hybrid Medical Data Mining Model for Identifying Tumor Severity in Breast Cancer Diagnosis
Purpose: This study proposes a methodology for detecting tumor severity using data mining of databases relating to breast imaging modalities. In doing so, it proposes creating a software application that can serve as an efficient decision-making support system for medical practitioners, especially those in areas where there is a shortage of modern medical diagnostic devices or specialized practitioners, such as in developing countries.Method: we investigated the data of approximately 3754 screened women by using “BI-RADS” categories as a quality assessment tool to screening, measure, and identify the size and location of lesions, determine the number of lymph nodes, collect biopsy samples, determine final diagnoses, prognoses, and age which were all available from the screening registry. Result: The application of each algorithm on BI-RADS values 4 and 5 for Invasive Ductal Carcinoma lesions was assessed, and the following accuracy was acquired: CART: 84.71%. In order to get the best result, four optimum clusters based on tumor size were applied to constructing simple rules with significant confidence. Conclusion: This study presents a hybrid approach - a combination of k-means with GRI and CART decision tree - to better assess breast cancer data sets.
https://aie.ut.ac.ir/article_84377_3991e2d869817d530418c7e80cec346d.pdf
2021-04-01
151
164
10.22059/jieng.2021.326775.1789
Breast Cancer Prediction
Mammography
Ultrasonography
Medical Data Mining
Invasive Ductal Carcinoma
Faeze
Araghi Niknam
s.araghi1357@gmail.com
1
School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran.
AUTHOR
Rouzbeh
Ghousi
ghousi@iust.ac.ir
2
School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran.
LEAD_AUTHOR
AmirHossein
Masoumi
amir.hossein.masumi@gmail.com
3
School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran.
AUTHOR
Alireza
Atashi
smatashi@yahoo.com
4
Department of Medical Informatics, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran
AUTHOR
Ahmad
Makui
amakui@iust.ac.ir
5
School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran.
AUTHOR
[1] Keleş, M.K., Breast cancer prediction and detection using data mining classification algorithms: a comparative study. Tehnički vjesnik, 2019. 26(1): p. 149-155.
1
[2] Ghousi, R., Applying a decision support system for accident analysis by using data mining approach: A case study on one of the Iranian manufactures. Journal of Industrial and Systems Engineering, 2015. 8(3): p. 59-76.
2
[3] Sohrabei, S. and A. Atashi, Performance Analysis of Data Mining Techniques for the Prediction Breast Cancer Risk on Big Data. Frontiers in Health Informatics, 2021. 10(1): p. 83.
3
[4] Diz, J., G. Marreiros, and A. Freitas, Applying data mining techniques to improve breast cancer diagnosis. Journal of medical systems, 2016. 40(9): p. 1-7.
4
[5] Masoumi, A., et a, A quantitative scoring system to compare the degree of COVID-19 infection in patients’ lungs during the three peaks of the pandemic in Iran. Journal of Industrial and Systems Engineering, 2021. 13(3): p. 61-69.
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[6] Higa, A., Diagnosis of breast cancer using decision tree and artificial neural network algorithms. cell, 2018. 1: p. 10.
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32
ORIGINAL_ARTICLE
A Hybrid Approach for Home Health Care Routing and Scheduling Using an Agent-Based Model
Home health care systems, as a growing economic system in the field of health systems, face various problems and issues such as routing, scheduling and allocation. Given that a growing number of home health care workers in health care systems around the world tend to work for themselves instead of hospitals or other health care institutions. As a result, centralized and one-factor models are not responsible for solving these problems. Therefore, this paper focuses on situations by designing an agent-based planning system that is simulated in a decentralized environment and using the Fuzzy C-Means clustering algorithm and the repetitive suggestion mechanism (Vickery) as a negotiation protocol focuses on situations that a home health care agency needs to schedule a home visit among a group of independent physicians. The goal of the home health care agency is to minimize the overall cost of the service by covering all patients by qualified physicians. The results of the implementation of the proposed algorithm for real geographical data in the city of Tehran in GAMS show that this framework achieves high efficiency of optimal solutions.
https://aie.ut.ac.ir/article_84378_e277079acaade2f6ced8e91ae0bfad89.pdf
2021-04-01
165
176
10.22059/jieng.2021.327043.1791
agent based model
Home Health Care
decentralized
fuzzy c-means
Iterative Bidding
Routing
Scheduling
Reza
Ramezanian
ramezanian@kntu.ac.ir
1
Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.
LEAD_AUTHOR
Mitra
Hallaji
mitra.hallaji@email.kntu.ac.ir
2
Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.
AUTHOR
[1] Castelnovo, C., et al., A multi agent architecture for home care services. Reforming Health Systems: Analysis and Evidence: Strategic Issues in Health Care Management, 2006: p. 135-151.
1
[2] Itabashi, G., et al. A support system for home care service based on multi-agent system. in 2005 5th International Conference on Information Communications & Signal Processing. 2005. IEEE.
2
[3] López-Santana, E.R., J.A. Espejo-Díaz, and G.A. Méndez-Giraldo. Multi-agent approach for solving the dynamic home health care routing problem. in Workshop on Engineering Applications. 2016. Springer.
3
[4] Mohammadi, A. and E.S. Eneyo, Home health care: a multi-agent system based approach to appointment scheduling. J. Innov. Res. Technol, 2015. 2(3): p. 37-46.
4
[5] Mutingi, M. and C. Mbohwa, A home healthcare multi-agent system in a multi-objective envir SAIIE25 Proceedings, 2013: p. 636.
5
[6] Stojanova, A., et al., Agent-based solution of caregiver scheduling problem in home-care context. 2017.
6
[7] Becker, C.A. and I.J. Timm, Planning and scheduling for cooperative concurrent agents with different qualifications in the domain of home health care management. 2019.
7
[8] Becker, C.A. and I.J. Timm, Planning and Scheduling for Cooperative Concurrent Agents in the Domain of Home Health Care Management. 2019.
8
[9] Hamdani, F.E., A. El Mhamedi, and D. Monticolo. Agent-based Approach of Multi-Structures Homecare Planning Problem. in 2019 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA). 2019. IEEE.
9
[10] Widmer, T. and M. Premm. Agent-based decision support for allocating caregiving resources in a dementia scenario. in German Conference on Multiagent System Technologies. 2015. Springer.
10
[11] Xie, Z., N. Sharath, and C. Wang. A game theory based resource scheduling model for cost reduction in home health care. in 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). 2015. IEEE.
11
[12] Xie, Z. and C. Wang. A periodic repair algorithm for dynamic scheduling in home health care using agent-based model. in 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD). 2017. IEEE.
12
[13] Bajo, J., et al., The THOMAS architecture in Home Care scenarios: A case study. Expert Systems with Applications, 2010. 37(5): p. 3986-3999.
13
[14] Miyamoto, S., et al., Algorithms for fuzzy clustering. 2008: Springer.
14
[15] Miyamoto, S., H. Ichihashi, and K. Honda, BasicMethods for c-Means Clustering, in Algorithms for Fuzzy Clustering. 2008, Springer. p. 9-42.
15
ORIGINAL_ARTICLE
Model-Based Monitoring of Patient Response to Staged Thyroidectomy
The goal of this study is to develop a model-based control chart for monitoring patient behavior in a staged thyroidectomy considering risk factors and clinical prescription. prospectively collected data are gathered from thyroid surgery unit of a hospital located in Tehran, Iran for 80 staged thyroidectomy patients discharged from 2009 to 2013. A risk adjusted state space model is developed based on the staged thyroidectomy. Variables to be included in the model are determined as a part of the model building process. Performance criteria, clinical prescription and patient risk factors are three variable components for the model. The appropriate risk factors are directly involved in the model and no scoring system is used for the model construction. Model identification is performed in two steps; model order selection and parameter estimation. In the first step, Hankel singular value decomposition (HSVD) is used for detecting the model order and in the second step, unknown parameters are estimated by the prediction error minimization (PEM) method. For monitoring patient responses, a group individual (GI) control chart is introduced and applied to a real-world problem. Results indicate that the suggested control chart can monitor the staged thyroidectomy patient’s behavior with an acceptable accuracy. Also, computer aided diagnosis (CAD) systems can be developed based on the proposed identification and monitoring method.
https://aie.ut.ac.ir/article_84379_49a9b3c55a17ac3b508f377295147430.pdf
2021-04-01
177
189
10.22059/jieng.2021.327936.1794
Surgical Operation
Staged Thyroidectomy
Risk Adjustment
Model Identification
Model-Based Control Chart
Mohammad
Rasouli
mhrsana@gmail.com
1
Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran.
AUTHOR
Kamran
Heidari
heidari-k@sbmu.ac.ir
2
Skull Base Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Science, Tehran, Iran.
AUTHOR
Yaser
Samimi
y_samimi@kntu.ac.ir
3
Industrial Engineering Department, K.N. Toosi University of Technology, Tehran, Iran.
AUTHOR
Rassoul
Noorossana
rassoul@iust.ac.ir
4
Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran.
LEAD_AUTHOR
[1] Cardoso J, Van der Aalst W. (2009). Handbook of research on business process modelling. Information Science Reference.
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[2] Hu, J., Zhao, N., Kong, R., Wang, D., Sun, B., & Wu, L. (2016). Total thyroidectomy as primary surgical management for thyroid disease: surgical therapy experience from 5559 thyroidectomies in a less-developed region. World Journal of Surgical Oncology, 14(1), 1-7.
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[3] Dionigi, G., Frattini, F. (2013). Staged thyroidectomy: Time to consider intraoperative neuromonitoring as standard of care. Thyroid, 23(7), 906-908.
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[4] Rosen, K., Reid, R., Broemeling, A., & Rakovsky, C. (2003). Applying a risk-adjusted framework to primary care: Can we improve on existing measures? Annals of Family Medicine, 1(1), 44-55.
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61
ORIGINAL_ARTICLE
Prediction of Accident Occurrence Possibilityby Fuzzy Rule-Based and Multi-Variable Regression (Case Study: Lift Trucks)
Uncertain and stochastic conditions of accidents could affect the risk and complexity of decisions for managers. Accident prediction methods could be helpful to confront these challenges. Fuzzy inference systems (FIS) have developed a new attitude in this field in recent years. As lift truck accidents are one of the main challenges that industries face worldwide, this paper focuses on predicting the possibility of these types of accidents. At first, the data collection is done by using interviews, questionnaires, and surveys. A FIS approach is proposed to predict the possibility of lift truck accidents in industrial plants. Furthermore, our approach is validated using data from many real cases. The results are approved by the multivariate logistic regression method. Finally, the output of the fuzzy and logit models is compared with each other. The re-validation of the fuzzy control model and high consistent of the output of these two models is presented.
https://aie.ut.ac.ir/article_84380_d1db5df754611238499cca82bb15436f.pdf
2021-04-01
191
201
10.22059/jieng.2021.328736.1797
Accident Prediction
Fuzzy Inference System (FIS)
Multivariate Logistic Regression
Lift Truck Accident
Rouzbeh
Ghousi
ghousi@iust.ac.ir
1
School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran.
LEAD_AUTHOR
AmirHossein
Masoumi
amir.hossein.masumi@gmail.com
2
School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran.
AUTHOR
Ahmad
Makui
amakui@iust.ac.ir
3
School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran.
AUTHOR
[1] Zhou, J. and G. Reniers, Petri net simulation of multi-department emergency response to avert domino effects in chemical industry accidents. Process Safety and Environmental Protection, 2021. 146: p. 916-926.
1
[2] Santana, J.A.D., et al., A new Fuzzy-Bayesian approach for the determination of failure probability due to thermal radiation in domino effect accidents. Engineering Failure Analysis, 2021. 120: p. 105106.
2
[3] Attwood, D., F. Khan, and B. Veitch, Occupational accident models—Where have we been and where are we going? Journal of Loss Prevention in the Process Industries, 2006. 19(6): p. 664-682.
3
[4] Amin, M.T., F. Khan, and M.J. Zuo, A bibliometric analysis of process system failure and reliability literature. Engineering Failure Analysis, 2019. 106: p. 104152.
4
[5] Choi, M., S. Ahn, and J. Seo, VR-Based investigation of forklift operator situation awareness for preventing collision accidents. Accident Analysis & Prevention, 2020. 136: p. 105404.
5
[6] Kim, K.W., Characteristics of forklift accidents in Korean industrial sites. Work, 2021(Preprint): p. 1-9.
6
[7] Gajendran, C., et al., Different Methods of Accident Forecast Based on Real Data. Journal of Civil & Environmental Engineering, 2015. 5(4): p. 1.
7
[8] Driss, M., et al. A fuzzy logic model for identifying spatial degrees of exposure to the risk of road accidents (Case study of the Wilaya of Mascara, Northwest of Algeria). in 2013 International Conference on Advanced Logistics and Transport. 2013. IEEE.
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[9] Zarei, E., et al., Safety analysis of process systems using Fuzzy Bayesian Network (FBN). Journal of loss prevention in the process industries, 2019. 57: p. 7-16.
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[10] Kabir, S. and Y. Papadopoulos, A review of applications of fuzzy sets to safety and reliability engineering. International Journal of Approximate Reasoning, 2018. 100: p. 29-55.
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[11] Gallab, M., et al., Risk assessment of maintenance activities using fuzzy logic. Procedia computer science, 2019. 148: p. 226-235.
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[12] Maraj, E. and S. Kuka, Prediction of road accidents using fuzzy logic. Journal of Multidisciplinary Engineering Science and Technology (JMEST), 2019. 6: p. 12.
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[13] Zaied, A.N.H. and W. Al Othman, Development of a fuzzy logic traffic system for isolated signalized intersections in the State of Kuwait. Expert Systems with Applications, 2011. 38(8): p. 9434-9441.
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[14] MENG, X.-h., L. ZHENG, and G.-m. QIN, Traffic Accidents Prediction and Prominent Influencing Factors Analysis Based on Fuzzy Logic [J]. Journal of transportation systems engineering and information Technology, 2009. 2: p. 015.
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[15] Driss, M., et al., Traffic safety prediction model for identifying spatial degrees of exposure to the risk of road accidents based on fuzzy logic approach. Geocarto international, 2015. 30(3): p. 243-257.
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[16] Jalali, N.S., Makui, A. and Ghousi, R. An approach for accident forecasting using fuzzy logic rules: a case mining of lift truck accident forecasting in one of the iranian car manufacturers. International journal of industrial engineering and production research, 2012. 23(1).
16
[17] Pourjavad, E. andV. Mayorga, A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system. Journal of Intelligent Manufacturing, 2019. 30(3): p. 1085-1097.
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[18] Li, J. and Z. Gong, SISO Intuitionistic Fuzzy Systems: IF-t-Norm, IF-R-Implication, and Universal Approximators. IEEE Access, 2019. 7: p. 70265-70278.
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[21] Ull,, et al., Injuries after Forklift Trucks Accidents–Injury Patterns, Therapy and Outcome in the Context of the Statutory Accident Insurance. Zeitschrift für Orthopädie und Unfallchirurgie, 2021.
21
ORIGINAL_ARTICLE
Dynamic Analysis of Lean and Green Supply Chain Policies in Sustainability of CHOUKA Iran Wood & Paper Industries Inc.
Sustainability of supply chain in economic systems and green supply chain management has widely attracted researchers’ attention by raising awareness of environmental effects. On the other hand, the lean supply chain is another concept whose implementation in organizations is expected to result in improved sustainability in industries. The present study aims to analyze the policy-based roles of the lean supply chain and green supply chain concepts in the corporation sustainability by designing a system dynamics model of the supply chain in CHOUKA Iran Wood & Paper Industries Inc. To this end, after a review of the literature with collaborations from decision-makers and CHOUKA data, the system dynamics model was designed in Vensim, and the model was simulated in a 10-year horizon after verification. Considering the behavior of the variables and the Monte Carlo sensitivity analysis, the model was simulated in the horizon, the green supply chain policies, the lean supply chain management and CHOUKA's business profitability were designed and applied on the model both in separate and integrative manners, the results were compared, and the behaviors of the policies were analyzed. As for the findings of the model simulation, the selected combination of the policies of lean supply chain management and green supply chain management, as well as CHOUKA’s business profitability, was proposed as the best integrative sustainability policy for CHOUKA Iran Wood & Paper Industries supply chain management. The results generally indicated that simultaneous implementation of lean and green supply chain policies leads to synergy in supply chain sustainability
https://aie.ut.ac.ir/article_84381_eba702d8fc755d28f86dd2317de7fc2f.pdf
2021-04-01
203
218
10.22059/aie.2021.330673.1806
Green supply chain
supply chain sustainability
System Dynamics
CHOUKA Iran Wood & Paper Industries
Mohammad Ali
Enayati Shiraz
m.alienayatishiraz@iauandimeshk.ac.ir
1
Department of Industrial Administration, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Seyed
Heydariyeh
a.heidariyeh@semnaniau.ac.ir
2
Department of Industrial Administration, Semnan Branch, Islamic Azad University, Semnan, Iran.
LEAD_AUTHOR
Mohammad Ali
Afshar Kazemi
m_afsharkazemi@iauec.ac.ir
3
Department of Industrial Administration, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
[1] Xu, J., Cao, J., Wang, Y., Shi, X., & Zeng, J. (2020). Evolutionary game on government regulation and green supply chain decision-making. Energies, 13(3), 620.
1
[2] Reche, A. Y. U., Junior, O. C., Estorilio, C. C. A., & Rudek, M. (2020). Integrated product development process and green supply chain management: Contributions, limitations and applications. Journal of Cleaner Production, 249, 119429.
2
[3] Jemai, J., Do Chung, B., & Sarkar, B. (2020). Environmental effect for a complex green supply-chain management to control waste: A sustainable approach. Journal of Cleaner Production, 277, 122919.
3
[4] Wang, C., Zhang, Q., & Zhang, W. (2020). Corporate social responsibility, Green supply chain management and firm performance: The moderating role of big-data analytics capability. Research in Transportation Business & Management, 37, 100557.
4
[5] Tafreshi Motlagh, A., Olfat, L., Bamdad Soufi, J., Amiri, M. (2016). Presenting an Integrated Model to Explain the Effects of Both Lean and Green Supply Chain on Corporate Sustainability. Iranian journal of management sciences, 11(44), 1-24.
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[11] Malviya, R. K., & Kant, R. (2016), Hybrid decision making approach to predict and measure the success possibility of green supply chain management implementation, Journal of Cleaner Production,135, 387–409.
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[12] Fercoq, A., Lamouri, S., & Carbone, V. (2016), Lean / Green integration focused on waste reduction techniques, Journal of Cleaner Production,137, 567–578.
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