@article { author = {Ghasemi Eshkaftaki, Zohre and Zeinal Hamadani, Ali and Ahmadi Yazdi, Ahmad}, title = {Evaluating Parameter Estimation Effect on the Polynomial Profile Monitoring Methods’ Phase II Performance}, journal = {Advances in Industrial Engineering}, volume = {55}, number = {2}, pages = {133-150}, year = {2021}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {10.22059/jieng.2021.326559.1785}, abstract = {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.}, keywords = {Profile monitoring,Polynomial profile,Estimation Effect,Control chart,Run Length,Statistical Process Control}, url = {https://aie.ut.ac.ir/article_84376.html}, eprint = {https://aie.ut.ac.ir/article_84376_5dd2a90a542abd031d60755003a0d7c9.pdf} } @article { author = {Araghi Niknam, Faeze and Ghousi, Rouzbeh and Masoumi, AmirHossein and Atashi, Alireza and Makui, Ahmad}, title = {Hybrid Medical Data Mining Model for Identifying Tumor Severity in Breast Cancer Diagnosis}, journal = {Advances in Industrial Engineering}, volume = {55}, number = {2}, pages = {151-164}, year = {2021}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {10.22059/jieng.2021.326775.1789}, abstract = {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.}, keywords = {Breast Cancer Prediction,Mammography,Ultrasonography,Medical Data Mining,Invasive Ductal Carcinoma}, url = {https://aie.ut.ac.ir/article_84377.html}, eprint = {https://aie.ut.ac.ir/article_84377_3991e2d869817d530418c7e80cec346d.pdf} } @article { author = {Ramezanian, Reza and Hallaji, Mitra}, title = {A Hybrid Approach for Home Health Care Routing and Scheduling Using an Agent-Based Model}, journal = {Advances in Industrial Engineering}, volume = {55}, number = {2}, pages = {165-176}, year = {2021}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {10.22059/jieng.2021.327043.1791}, abstract = {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.}, keywords = {agent based model,Home Health Care,decentralized,fuzzy c-means,Iterative Bidding,Routing,Scheduling}, url = {https://aie.ut.ac.ir/article_84378.html}, eprint = {https://aie.ut.ac.ir/article_84378_e277079acaade2f6ced8e91ae0bfad89.pdf} } @article { author = {Rasouli, Mohammad and Heidari, Kamran and Samimi, Yaser and Noorossana, Rassoul}, title = {Model-Based Monitoring of Patient Response to Staged Thyroidectomy}, journal = {Advances in Industrial Engineering}, volume = {55}, number = {2}, pages = {177-189}, year = {2021}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {10.22059/jieng.2021.327936.1794}, abstract = {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.}, keywords = {Surgical Operation,Staged Thyroidectomy,Risk Adjustment,Model Identification,Model-Based Control Chart}, url = {https://aie.ut.ac.ir/article_84379.html}, eprint = {https://aie.ut.ac.ir/article_84379_49a9b3c55a17ac3b508f377295147430.pdf} } @article { author = {Ghousi, Rouzbeh and Masoumi, AmirHossein and Makui, Ahmad}, title = {Prediction of Accident Occurrence Possibilityby Fuzzy Rule-Based and Multi-Variable Regression (Case Study: Lift Trucks)}, journal = {Advances in Industrial Engineering}, volume = {55}, number = {2}, pages = {191-201}, year = {2021}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {10.22059/jieng.2021.328736.1797}, abstract = {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.}, keywords = {Accident Prediction,Fuzzy Inference System (FIS),Multivariate Logistic Regression,Lift Truck Accident}, url = {https://aie.ut.ac.ir/article_84380.html}, eprint = {https://aie.ut.ac.ir/article_84380_d1db5df754611238499cca82bb15436f.pdf} } @article { author = {Enayati Shiraz, Mohammad Ali and Heydariyeh, Seyed and Afshar Kazemi, Mohammad Ali}, title = {Dynamic Analysis of Lean and Green Supply Chain Policies in Sustainability of CHOUKA Iran Wood & Paper Industries Inc.}, journal = {Advances in Industrial Engineering}, volume = {55}, number = {2}, pages = {203-218}, year = {2021}, publisher = {University of Tehran}, issn = {2783-1744}, eissn = {2783-1744}, doi = {10.22059/aie.2021.330673.1806}, abstract = {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}, keywords = {Green supply chain,supply chain sustainability,System Dynamics,CHOUKA Iran Wood & Paper Industries}, url = {https://aie.ut.ac.ir/article_84381.html}, eprint = {https://aie.ut.ac.ir/article_84381_eba702d8fc755d28f86dd2317de7fc2f.pdf} }