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1 تخمین نقطۀ تغییر پله‌ای در نمودارهای کنترل g و h در حوزۀ بهداشت و درمان Step Change Point Estimation in g and h Control Charts in Healthcare https://aie.ut.ac.ir/article_66285.html 10.22059/jieng.2018.134403.1008 1 Estimating the real time of a change point in process is one of the main purposes in the statistical process control. On the other hand, it is important to estimate the change point in healthcare processes regarding the relation between quality engineering and hospital epidemiology. Hence, in this paper, maximum likelihood estimators are proposed in g and h control charts for healthcare systems. We applied Monte Carlo simulation to assess the proposed approaches in terms of accuracy and precision. In addition, there are provided the corresponding cardinality sets and coverage probabilities based on logarithm of the likelihood function. Results indicate that the proposed estimators have a satisfactory performance under different shifts. 0  یکی از اهداف اصلی کنترل فرایند آماری کشف زمان دقیق وقوع تغییر در فرایندها، تحت عنوان نقطة تغییر است. با توجه به رابطة مهندسی کیفیت و اپیدمیولوژی بیمارستانی، تخمین نقطة تغییر در فرایندهای بهداشت و درمان اهمیت بسزایی دارد؛ از این‌رو در این پژوهش، ضمن ارائة نمودارهای کنترل g و h برای مراقبت‌های درمانی، به تخمین نقطة تغییر پله‌ای با استفاده از برآورد حداکثر درست‌نمایی پرداخته شده است. به‌منظور ارزیابی عملکرد روش‌های پیشنهادی از شبیه‌سازی مونت‌کارلو براساس معیارهای صحت و دقت استفاده شده، همچنین تعداد اعضای مجموعة اطمینان و احتمال پوشش آن‌ها، براساس لگاریتم تابع درست‌نمایی ارائه شده است. نتایج شبیه‌سازی حاکی از آن است که تخمین‌زننده‌های پیشنهادی تحت شیفت پله‌ای، عملکردی رضایت‌بخش تحت انواع شیفت‌ها دارند. 1 12 امیرحسین امیری Amir Hossein Amiri Department of Industrial Engineering, Shahed University, Tehran, Iran Iran amirhossein.amiri@gmail.com فاطمه سوگندی Fatemeh Sogandi Department of Industrial Engineering, Shahed University, Tehran, Iran Iran f.sogandi@shahed.ac.ir آزاده رفیعی طباطبایی Azadeh Rafiei Tabatabaei Department of Industrial Engineering, Shahed University, Tehran, Iran Iran a.tabatabaee@yahoo.com Healthcare g and h Control Charts Maximum Likelihood Estimator Statistical Process Control Step Change Point Estimation 1. Hajahmadi, F., and Noorossana, R. (2015). “Profiles non-parametric monitroring and make a dicision on quality process using mix effects models”, Journal Industrial Engineering , Vol. 50, No. 1, PP. 13-22.##2. aminnayeri, M., Mohammadi, B., and Ayoubi, M. (2013). “Change point estimation on multiple profiles under drift shifts under mean process”, Journal Industrial Engineering , Vol. 48, Special Issue, PP. 63-71.##3. Taghizadeh, A. and Noorossana, R. (2011). “The necessity of re-conceptualizing the European foundation for quality management’s (EFQM) excellence model for the health care sector and its use in the Iranian national productivity and excellence award”, Hospital , Vol. 10, No. 2, PP. 46-61.##4.Mohammed, M.A., Worthington, P., and Woodall W.H. (2008). “Plotting basic control charts: tutorial notes for healthcare practitioners”,Quality and Safety Health Care, Vol. 17, No. 1, PP. 137-145.##5. Noyes, L. (2009). “Cusum techniques and funnel plots. A review of methods for monitoring performance in healthcare”, Interactive cardiovascular and thoracic surgery, Vol. 9, No. 3, PP. 494-499.##6. Tennant, R., and et al., (2013). “Monitoring patients using control charts: a systematic review”, International journal for quality in healthcare, Vol. 19, No. 4, PP. 187-194.##7. Fatahi, A.A., et al., (2011). “A review on statistical monitoring of rare events and their necessity in healthcare area”, Payesh, Vol. 10, No. 4, PP. 429- 437.##8. Assareh, H., Smith, I., and Mengersen, K. (2011). “Bayesian change point detection in monitoring cardiac surgery outcomes”, Quality Management in Healthcare, Vol. 20, No. 3, PP. 207-222.##9. Grigg, O., and Farewell, V. (2004). “An overview of risk-adjusted charts”, Journal of the Royal Statistical Society: Series A (Statistics in Society), Vol. 167, No. 3, PP. 523-539.##10. Kaminsky, F.C., Benneyan J.C., and Davis R.D. (1992). “Statistical control charts based on Geometric distribution”, Journal of Quality Technology, Vol. 24, No. 2, PP. 63-69.##11. Benneyan, J.C. (1998). “Statistical quality control methods in infection control and hospital epidemiology, Part 2: chart use, Statistical Properties and Research Issues”, Statistics for Hospital Epidemiology, Vol. 19, No. 4, PP. 265-283.##12. Benneyan, J.C., (1998). “Statistical quality control methods in infection control and hospital epidemiology, Part 1: Introduction and Basic Theory”, Statistics for Hospital Epidemiology, Vol. 19, No. 3, pp. 194-214.##13. ____________, (2001). “Number-between g-type statistical quality control charts for monitoring adverse events”, Health Care Management Science, Vol. 4, No. 4, PP. 305-318.##14. Benneyan, J.C., Lloyd, R.C., and Plsek, P.E. (2003). “Statistical process control as a tool for research and healthcare improvement”, Quality Safety in Health Care, Vol. 12, No. 6, PP. 458-464.##15. Woodall, W.H. 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Fatahi, A.A., Noorossana, R., and Dokouhaki, P., (2012). “Copula-Based Bivariate ZIP Control Chart for Monitoring Rare Events”, Communications in Statistics - Theory and Methods, Vol. 41, No. 15, PP. 2699-2716.##21. Duclos, A., and Voirin, N. (2010). “The p-control chart: a tool for care improvement”, International Journal for Quality in Health Care, Vol.4, No, 2. PP. 1-6.##22. Noorossana, R. and Shadman, A, (2009). “Estimating the change point of a normal process mean with a monotonic change”, Quality and Reliability Engineering International, Vol. 25, No. 1, PP. 79-90,##23. Box, G. and Cox, D., (1964). “An analysis of transformations”, Journal of the Royal Statistical Society B, Vol. 26, No. 2, PP. 211–243.##

1 رویکرد احتمالی در ارائۀ مدل برنامه‌ریزی ریاضی مسئلۀ تخصیص مازاد یک سیستم سری- موازی با به‌کارگیری سیاست تخفیف Probabilistic Approach in Mathematical Programming Model to Solve Redundancy Allocation Problems in a Series-Parallel System with All Unit Discount Policy https://aie.ut.ac.ir/article_66286.html 10.22059/jieng.2018.230815.1355 1 Nowadays, designing and implementing systems with premier features and higher reliability is deemed to be a basic principle for the engineers and users. Because regarding this point can result in the proper use of a system during its lifetime. Reliability refers to a measure of quality versus time factor that is computed by a probability of working without any failure in a given time and under some specific conditions. Since a system consists of many items, which withstand several stress factors, manufacturers commonly employ different solution approaches to increase the reliability. One of the main strategies in this regard is to consider some additional items in parallel to original ones. This is also called redundancy allocation. Different items have different reliability, cost, weight, and importance level. So, making decision on assigning redundant would be of interest under limited budget and volume or weight in system development.  Some redundant item is not turned on until the main item works correctly. Hence, a switch and sensors would be considered to monitor the status of the main item and to decide when the redundant must start working. This type is called standby item. In today’s competitive world, offering a system with lower total expense, given that its high reliability is maintained, can make the company popular with the customers. Although in recent years, research on optimization have been presented with all unit discount for components of a system, this paper not only addresses the active redundancy strategy, but also it discusses a combination of components with active or cold redundancy strategy. It uses a system that generates the all unit discount to the sum of the components with the two strategies mentioned. Additionally, in order to make the problem more realistic to the real world, failure rate and cost parameters were considered uncertain. To solve two models with the aims of maximizing the reliability and minimizing the cost, chance constrained approach was employed for the constraints of cost and reliability. The proposed model was solved with accurate method using GAMS software. With regard to the model’s proper treatment of changes in effective factors proposed in the model, it is concluded that this model is exploitable for optimizing stability in mass production industries where applying the global discount policy leads to some benefits. 0 امروزه طراحی و به‌کارگیری سیستم‌هایی با خصوصیات برتر و قابلیت اطمینان بالاتر برای مهندسان و کاربران، اصلی اساسی به‌شمار می‌رود؛ زیرا توجه به این مسئله در استفادة مناسب از یک سیستم در طول دورة عمر آن تأثیرگذار است، همچنین در دنیای رقابتی امروز عرضة سیستمی با هزینة تمام‌شدة کمتر به‌طوری‌که قابلیت اطمینان زیاد برای آن حفظ شود، شرکت را در میان مشتریان محبوب می‌کند. هرچند در سال‌های اخیر پژوهش‌هایی در زمینة بهینه‌سازی پایایی با درنظرگرفتن تخفیفات کلی برای اجزای یک سیستم ارائه شده، نوآوری این تحقیق در آن است که نه‌تنها راهبرد مازاد فعال، بلکه ترکیبی از اجزا با راهبرد مازاد فعال یا آماده‌به‌کار سرد را می‌توان در یک سیستم به‌کار برد، به‌گونه‌ای که تخفیفات کلی به مجموع اجزاء با دو راهبرد مذکور تعلق بگیرد. علاوه‌بر این، به‌منظور نزدیک‌ترکردن شرایط مسئله به دنیای واقعی، پارامترهای نرخ خرابی و هزینه به‌صورت غیرقطعی درنظر گرفته شده است که برای حل دو مدل با اهداف حداکثرسازی پایایی و حداقل‌سازی هزینه به ترتیب رویکرد محدودیت احتمالی بر روی محدودیت مربوط به هزینه و پایایی استفاده می‌شود. مدل ارائه‌شده با روش دقیق و با استفاده از نرم‌افزار GAMS حل شده که با توجه به رفتار مناسب آن در تغییر عوامل مؤثر در مسئلة مورد بررسی نتیجه می‌گیریم که می‌توان از این مدل به‌منظور بهینه‌سازی پایایی و حداقل‌سازی هزینه در صنایع تولیدی با تولیدات بالا که به‌کارگیری سیاست تخفیفات کلی مزیتی را برای آن‌ها دارد، بهره‌برداری کرد. 13 23 طه حسین حجازی Taha Hossein Hejazi Department of Industrial Engineering, Amirkabir University of Technology, Garmsar Campus, Iran Iran t.h.hejazi@gmail.com محسن باقری Mohsen Bagheri Department of Industrial Engineering, Amirkabir University of Technology, Garmsar Campus, Iran Iran m_bagheri@sadjad.ac.ir حانیه جمشیدی Hanieh Jamshidi Department of Industrial Engineering, Amirkabir University of Technology, Garmsar Campus, Iran Iran jamshidih666@gmail.com All Unit Discounts Probabilistic Programming reliability Redundancy Allocation Problem Series-Parallel System Islami Baladeh, A.A., Seyed Esfahani, M.M. and Farsi, M.A. (2014). "A Scenario-Based Model for Redundancy Allocation with Choice of Redundancy Strategies", Journal of Industrial Engineering, Vol. 48, No. 1, PP. 91-98.##Kuo, W. and Wan, R. (2007). "Recent advances in optimal reliability allocation", Chapter, Computational Intelligence in Reliability Engineering, Vol. 37, No. 2 of the series Studies in Computational Intelligence, PP. 1-36.##Abouei Ardakan, M. and Hamadani Z. (2014). “Reliability optimization of series-parallel systems with mixed redundancy strategy in subsystems”,Reliability Engineering and System Safety, Vol. 130, No. 1, PP. 132–139.##Shaghaghi Nazarloo, F., Amiri, M. and Azimi, P. (2014). "Development of a simulation-based method to solve the problem of efficient allocation of surplus repairable systems", the tenth International Conference on Industrial Engineering,Iran Industrial Engineering Society, Amir Kabir University of Technology, Tehran, Iran, PP. 27-28.##Yahyatabar Arabi, A.A and Eshraghniaye Jahromi, A. (2013). “Availability optimization of a series system with multiple repairable load sharing subsystems considering redundancy and repair facility allocation”, International Journal of System Assurance Engineering Management, Vol.4, No 3, PP. 262–274.##Soltani, R., Sadjadi S.J. and Tavakkoli-Moghaddam, R. (2014). “Interval programming for the redundancy allocation with choices of redundancy strategy and component type under uncertainty: Erlang time to failure distribution”, Applied Mathematics and Computation, Vol. 244, No. 1, PP. 413-421.##Ghazi Mir Saeed, M., Najafi, A.A. and Shahryari, H. (2014). "Providing exact solution of k of n on the issue of allocation strategy excess surplus", Industrial Management, Vol. 6, No. 1, PP. 97-110.##Feizollahi, M.J., Soltani, R. and Feyzollahi, H. (2015). “The Robust Cold Standby Redundancy Allocation in Series-Parallel Systems With Budgeted Uncertainty",IEEE Transactions on Reliability., Vol. 64, No 2, PP. 1-9.##.Zhang, E. and Chen, Q. (2015) “Multi-objective reliability redundancy allocation in an interval environment using particle swarm optimization”, Reliability Engineering and System Safety, Vol. 145, No. 1, PP. 83–92.##Kong, X et al. (2015). “Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization”, Reliability Engineering and System Safety, Vol. 144, No. 1, PP. 147–158.##Latif Shabgahi, G. R., Aslansefat, K. and Bahar Gogani, M. (2015). "Reliability and Safety Modelling in Reliable Systems Supported with Cold Standby Spares by a Markov Model", Journal of Industrial Engineering, Vol. 49, No. 2, PP. 273-285.##Pourkarim Guilani, P. et al. (2016). “Redundancy allocation problem of a system with increasing failure rates of components based on Weibull distribution: a simulation-based optimization approach”, Reliability Engineering and System Safety, Vol. 152, No. 1, PP. 187–196.##Chatwattanasiri, N., Coit, D.W. and Wattanapongsakorn, N. 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1 ارائۀ روشی برای مدل‌سازی سیستم با مجموعه دادۀ کوچک به کمک شبکۀ عصبی به‌منظور بهینه‌سازی آن Modeling for System Optimization with Small Dataset Using Neural Network https://aie.ut.ac.ir/article_66288.html 10.22059/jieng.2018.232557.1371 1 The shortage of data is one of the most important problems in system modeling and optimization in industrial applications. Typical modeling techniques are unable to properly model a system with a limited dataset. In this paper, a modeling method for optimization of these systems is proposed. The proposed method has two main steps. In the first step, the model is employed to generate data using neural network. This model determines the correspondence input of each output. In the second step, optimization of the generated model is performed using genetic algorithm. Inputs leading to the specified output can be estimated using the proposed system. Optimality of the system can be explained by an evaluation function. The proposed method was evaluated in two different experiments on a time series and a real data. Results of the experiments were analyzed using mean square error. The experimental results show the capability of the proposed method in system modeling and optimization. 0 کمبود داده از مهم‌ترین مشکلات در مدل‌سازی و بهینه‌سازی سیستم‌های واقعی در کاربردهای صنعتی است. روش‌های معمول مدل‌سازی، با داشتن مجموعه دادة اندک از یک سیستم، توانمندی لازم را برای مدل‌کردن آن ندارند. در این مقاله روشی برای مدل‌سازی این نوع سیستم‌ها به‌منظور بهینه‌سازی ارائه‌ شده که از دو مرحلة اصلی تشکیل شده است. در مرحلة اول به کمک شبکة عصبی، مدلی برای تولید داده‌ها ایجاد می‌شود که با دریافت هر خروجی دلخواه از سیستم، تعیین می‌کند این خروجی ناشی از اعمال چه ورودی‌ای به سیستم بوده است. در مرحلة دوم، به کمک الگوریتم ژنتیک روشی برای بهینه‌سازی مدل تولیدشده ارائه می‌شود. در این مقاله، به کمک روش پیشنهادشده می‌توان ورودی‌های منجر به تولید خروجی بهینه را یافت. بهینه‌بودن عملکرد سیستم در تابعی موسوم به تابع برازش بررسی می‌شود. روش ارائه‌شده بر روی یک سری زمانی غیرخطی متغیر با زمان، به‌وسیلة معادلة ریاضی مشخص، و یک مجموعه داده واقعی از صنعت کشاورزی ارزیابی شده است. تحلیل نتایج آزمایش‌ها نیز با معیار میانگین مربعات خطا صورت گرفته است. نتایج ارزیابی با این معیار توانمندی این روش را در مدل‌سازی و بهینه‌سازی مجموعه داده‌های این مقاله نشان می‌دهد. 25 35 حمید حسن‌پور Hamid Hassanpour Department of Image Processing and Data Mining Lab, Shahrood University of Technology, Semnan, Iran Iran h.hassanpour@shahroodut.ac.ir محمدمهدی علیان‌نژادی Mohammad Mahdi Alyannezhadi Department of Image Processing and Data Mining Lab, Shahrood University of Technology, Semnan, Iran Iran alyan.nezhadi@gmail.com System modeling Optimization Multi-layer neural network Genetic algorithm 1.         K. 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Gosavi, 2015"Simulation-Based Optimization: An Overview", in Simulation-Based Optimization. vol. 55, ed: Springer US, , pp. 29-35.##[7]       R. H. Myers, D. C. Montgomery, and C. M. Anderson-Cook, Response surface methodology: process and product optimization using designed experiments vol. 705: John Wiley & Sons, 2009.##[8]       K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators," Neural Networks, vol. 2, pp. 359-366, 1989.##[9]       A. Gosavi, "Parametric Optimization: Response Surfaces and Neural Networks," in Simulation-Based Optimization. vol. 55, ed: Springer US, 2015, pp. 37-69.##[10]     Gholipoor, M. et al., (2012)."The optimization of root nutrient content for increased sugar beet productivity using an artificial neural network", International Journal of Plant Production, Vol. 6, No. 4, pp. 429-442.##[11]     Khazaii, J. (2016). "Genetic Algorithm Optimization", in Advanced Decision Making for HVAC Engineers, ed: Springer, pp. 87-97.##[12]     Ali M. Z., et al., (2017). "An improved رده of Real-Coded Genetic Algorithms for Numerical Optimization", Neurocomputing,##[13]     Bakirtzis A., and Kazarlis, S. (2016). "Genetic algorithms," Advanced Solutions in Power Systems: HVDC, FACTS, and Artificial Intelligence: HVDC, FACTS, and Artificial Intelligence, pp. 845-902.##[14]     J. Tang, C. Deng, and G.-B. Huang, (2016). "Extreme learning machine for multilayer perceptron", IEEE transactions on neural networks and learning systems, vol. 27, No. 4, pp. 809-821.##[15]     Abo-Hammour, Z. e., et al., (2013)."A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems", Mathematical Problems in Engineering, Vol. 2013, p. 12.##[16]     A. L. E. Will, (2016). 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1 مسئلۀ مکان‎یابی معکوس 2- مرکز برای شبکه‌های درختی بی‌وزن: مطالعۀ موردی Inverse 2-Center Location Problem for Unweighted Tree Networks (A Case Study) https://aie.ut.ac.ir/article_66289.html 10.22059/jieng.2018.208881.1145 1 This paper studies the inverse 2-center location problem by increasing and decreasing the edge length on unweighted tree networks. The goal is to increase and decrease the edge lengths at minimum total cost subject to the given modification bounds such that the predetermined vertices becomes absolute 2-center. In order to demonstrate the practical application of this issue, we consider Bojnourd urban network and two important fire stations of the city as center locations. Moreover, an example is generated for computational analysis. Results show that when the predetermined vertices are close to the ends of the tree, higher cost will be imposed. Yet, in most cases, this condition is impossible. 0 در این مقاله مسئلة مکان‌یابی معکوس 2- مرکز با افزایش و کاهش طول کمان‌ها روی درخت بدون وزن بررسی شده است. هدف مسئله، افزایش و کاهش طول کمان‌ها در حدود داده‌شده و در کمترین هزینة کل است؛ به‌طوری‌که دو رأس از پیش تعیین‌شده، به دو رأس مرکزی تبدیل شوند. به‌منظور نشان دادن کاربرد عملی این مسئله، شبکة شهری بجنورد و محل دو آتش‌نشانی مهم این شهرستان به‌عنوان مکان‌های مرکز درنظر گرفته شده است، همچنین به‌منظور تحلیل محاسباتی مثالی درنظر گرفته شده و نتایج حاصل از محاسبات این مفهوم مشخص می‌شود که چنانچه دو گره انتخابی به نقاط انتهایی درخت نزدیک‌تر باشند، هزینة بیشتری برای مرکزی‌شدن آن‌ها باید متحمل شد، البته باید توجه داشت که در بیشتر موارد این کار انجام‌نشدنی است. 37 48 زهرا داستانی Zahra Dastani Department of Industrial Engineering, University of Bojnourd, North Khorasan, Iran Iran z.dastani@yahoo.com حسین کریمی Hossein Karimi Department of Industrial Engineering, University of Bojnourd, North Khorasan, Iran Iran hhh.karimi@yahoo.com Bojnourd Center Location Inverse Location Unweighted Tree 1. Hakimi, S. L. (1965). "Optimum distribution of switching centers in a communication network and some related graph theoretic problems." Operations Research, Vol. 13, No. 3, pp. 462-475.##2. Daskin, M. (1997). Network and discrete location:Modeles, algorithms and applications. Wiley, New York.##3. Drezner, Z., Hamacher, H. W. (Eds.). (2001). Facility location: applications and theory. Springer Science and Business Media.‌##‌4. Love, R. F., Morris, J. G., and Wesolowsky, G. O. (1988). Facilities location. Chapter, 3, pp. 51-60.‌##5. Burton, D., and Toint, P. L. (1992). "On an instance of the inverse shortest paths problem." Mathematical Programming, Vol. 53, No. 1, pp. 45-61.‌##6. Heuberger, C. (2004). "Inverse combinatorial optimization: A survey on problems, methods, and results." Journal of combinatorial optimization, Vol. 8, No. 3, pp. 329-361.##7. Cai, M. C., Yang, X. G., and Zhang, J. Z. (1999). "The complexity analysis of the inverse center location problem." Journal of Global Optimization,‌ Vol. 15, No. 2, pp. 213-218.##8. Yang, X., and Zhang, J. (2008). "Inverse center location problem on a tree." Journal of Systems Science and Complexity, Vol. 21, No. 4, pp. 651–664.##9. Burkard, R. E., Pleschiutschnig, C., and Zhang, J. (2004). "Inverse median problems." Discrete Optimization, Vol. 1, No. 1, pp. 23-39.##10. Burkard, R. E., Pleschiutschnig, C., and Zhang, J. (2008). "The inverse 1-median problem on a cycle." Discrete Optimization, Vol. 5, No. 2, pp. 242-253.##11. Alizadeh, B., Burkard, R. E., and Pferschy, U. (2009). "Inverse 1-center location problems with edge length augmentation on trees." Computing, Vol. 86, No. 4, pp. 331-343.‌##12. Alizadeh, B., Burkard, R. E. (2011). "Combinatorial algorithms for inverse absolute and vertex 1‐center location problems on trees." Networks, Vol. 58, No. 3, pp. 190-200.##13.Hartman, J. M., and Kincaid, R. K. (2014). "P-Median Problems with Edge Reduction." Systems and Information Engineering Design Symposium, pp. 159-161.##14. Nguyen, K. T., Sepasian, A. R. (2016). "The inverse 1-center problem on trees with variable edge lengths under Chebyshev norm and Hamming distance."  Combinatorial Optimization, Vol. 32, No. 3, pp. 872-884.##15. Nguyen, K. T., Chassein, A. (2015). "The inverse convex ordered 1-median problem on trees under Chebyshev norm and Hamming distance." European Journal of Operational Research, Vol. 247, No. 3, pp. 774-781.##16. Nguyen, K. T., and Anh, L. Q. (2015). "Inverse k-centrum problem on trees with variable vertex weights." Mathematical Methods of Operations Research, Vol. 82, No. 1, pp. 19-30.##17. Nguyen, K. T. (2016). "Reverse 1-center problem on weighted trees." Optimization, ‌Vol. 65, No. 1, pp. 253-264.##18. Wolsey, L., Nemhauser, G. (1999). "Integer and Combinatorial Optimization."##19.Rashidifard, N., et al., (2014). "Optimal locate fire stations in urban traffic networks to aid the earthquake (Case study: Dehdasht)." Scientific- Research Quarterly of Geograghical data (SEPEHR), Article 6, Vol. 23, pp. 48-53, (In Persian).##

1 زمان‌بندی خط مونتاژ جریان کارگاهی دومرحله‌ای با درنظرگرفتن اثر کهولت در زمان پردازش، محدودیت دسترسی به کارها و نگهداری و تعمیرات پیشگیرانه Two Stage Assembly Flow Shop Scheduling Problem with Aging Effect, Limit Access to Work, and Preventive Maintenance https://aie.ut.ac.ir/article_66290.html 10.22059/jieng.2018.221166.1262 1 This paper studies the two-stage assembly flow shop problem (TAFSP) considering aging effects of the machines and preventive maintenance activities. At the first stage, m-1 parallel machines process parts of each jobs, and at the second stage, related parts of the jobs are assembled by one assembly machine. As the machines work on the jobs, their tools get aged. Aging effects on the machines causes that they will not be able to complete the jobs in the same time could as they were new or when they are operating jobs immediately after their preventive maintenance activity. Processing times of the job are related to the positions, in which it is located after the last preventive maintenance. The job that is operated in a position immediately after the preventive maintenance activity on a machine has its standard processing time. However, the processing time of the jobs operated in the further positions increase based on the number of the positions. The machines return to the initial condition after each preventive maintenance activity. The objective is to schedule the jobs on the machines and determine when the preventive maintenance activities get done on them in order to minimize the total weighted tardiness and maintenance costs. An integer mathematical model is presented for the problem and its validation is shown by solving an example in small scale. Since two-stage assembly flow shop problem is NP-hard, in order to solve the problem in medium and large scale two meta-heuristic algorithms, hybrid genetic algorithm (HGA) and hybrid particle swarm optimization (HPSO) are proposed. These algorithms are the hybrid version of genetic algorithm and particle swarm optimization representatively with simulated annealing. The algorithms are tuned by using Taguchi method, and are used to solve many numerical examples. Finally, the statistical analysis illustrates that the performance of HPSO is better than HGA.   0 رقابت بین‌المللی و توانایی پاسخ به تغییرات بازار از ویژگی‌های کلیدی در طراحی سیستم کارآمد تولیدی است. جریان کارگاهی مونتاژ دومرحله‌ای، سیستمی ترکیبی است که در آن قطعات مختلف بر روی ماشین‌های موازی به‌صورت غیرمستقل تولید می‌شود، این سیستم روشی است که برای تولید طیف وسیعی از کالاها با مونتاژ و ترکیب قطعات مختلف به‌کار می‌رود. در این تحقیق مسئلة زمان‌بندی خط جریان مونتاژ دومرحله‌ای با درنظرگرفتن اثر استهلاک ماشین‌ها و فعالیت‌های نگهداری و تعمیرات بررسی شده است. برای مسئلة مورد نظر ابتدا یک مدل ریاضی عدد صحیح ارائه‌ شده است، همچنین با حل یک نمونه کوچک عملکرد آن نمایش داده و تحلیل حساسیت‌های مختلف برای آن ارائه شده است. برای حل در ابعاد متوسط و بزرگ نیز الگوریتم‌های فرا ابتکاری HGA و HPSO دریافت شده است که به‌ترتیب ترکیبی از الگوریتم‌های ژنتیک و شبیه‌سازی تبرید و الگوریتم تجمع پرندگان هستند. تنظیم پارامترهای دو الگوریتم نیز با استفاده از روش آماری تاگوچی انجام شده است. نتایج دو الگوریتم نشان می‌دهد الگوریتم HPSO در مقایسه با الگوریتم HGA، از نظر به‌دست‌آوردن پاسخ‌های باکیفیت‌تر (براساس سنجة مقدار تابع هدف) در مسائلی با ابعاد بزرگ کیفیت بیشتری دارد. 49 60 عادله رزقی Adeleh Rezghi Department of Industrial Engineering, Mazandaran University of Science and Technology, Mazandaran, Iran Iran rezghi_babol1367@yahoo.com جواد رضائیان Javad Rezaeian Department of Industrial Engineering, Mazandaran University of Science and Technology, Mazandaran, Iran Iran j.rezaeian@ustmb.ac.ir Aging Effect Genetic Algorithm Preventive maintenance Simulated Annealing Two-Stage Assembly Flow Shop Problem Potts, C. N. et al. (1995). "The two-stage assembly scheduling problem:complexity and approximation", Operations Research,Vol. 43, No. 2, PP. 346– 355.##Al-Anzi, F. S. and Allahverdi, A. (2006). "A hybrid tabu search heuristic for the two-stage assembly scheduling problem", International Journal of Operational Research, Vol. 3, No. 2, PP.109- 119.##Al-Anzi, F. S., and Allahverdi, A. (2009). "Heuristics for a two stage assembly flow shop with criteria of maximum lateness and makespan", Computers and Operations Research, Vol. 36, No. 9, PP. 2682-2689.##Berrichi, A. et al. (2008). "Bi-objective optimization algorithms for joint production and maintenance scheduling: Application to the parallel machine problem", Journal of Intelligent Manufacturing, Vol. 20, No. 4, PP. 389–400.##Bachman, A. and Janiak, A. (2004). "Scheduling jobs with position-dependent processing times", Journal of Operational Research Society, Vol. 55, No. 3, PP. 257-264.##Mosheiov, G. (2001). "Parallel machine scheduling with a learning effect", Operational Research Society, Vol. 52, No. 10, PP. 1165-1169.##Gordon, V. S. et al. (2008). "Single machine scheduling models with deterioration and learning: handling precedence constraints via priority generation", Journal of Scheduling, Vol.11, No. 5, PP.357-370.##Mozdgir, A. et al. (2013). "Two stage assembly flow-shop scheduling problem with non-identical assembly machines considering set up times", International Journal of Production Research, Vol. 51, No.12, PP. 3625-3642.##Yang Dar-Li. et al. (2012). "Unrelated parallel-machine scheduling with aging effects and multi-maintenance activities", Computers and Operations Research, Vol. 39, No. 17, PP.1458-1464. 10. Salehi Mir, M.S, and Rezaeian J. (2016). "A robust hybrid approach based on particle swarm optimization and genetic algorithm to minimize the total machine load on unrelated parallel machines", Applied Soft Computing, Vol. 41, No .???, PP. 488-504.##11. Torabzadeh, E., and Zandieh, M. (2010). "Cloud theory-based simulated annealing approach for scheduling in the two-stage assembly flow shop", Advances in Engineering Software, Vol. 41, No.10-11, PP. 1238-1243.##12. Yaser Z. et al. (2015). "Minimization of makespan for the single batch-processing machine scheduling problem with considering aging effect and multi-maintenance activities", International Journal of Advanced Manufacturing Technology, Vol. 76, No. 9, PP.1879-1892.##13. Chou-Jung H. et al. (2013). "Unrelated parallel-machine scheduling problems with aging effects and deteriorating maintenance activities", Information Sciences, Vol. 253, No??? , PP.163-169.##14. Behnam Vahedi N., Fattahia p, and Ramezanian R.(2013). "Hybrid firefly-simulated annealing algorithm for the flow shop problem with learning effects and flexible maintenance activities", International Journal of Production Research, Vol. 51, No. 12, PP.3501-3515.##15. Abdollahpour S., and Rezaeian J. (2015). "Minimizing makespan for flow shop scheduling problem with intermediate buffers by using hybrid approach of artificial immune system", Applied Soft Computing, Vol. 28, No??? , PP. 44-56.##

1 مدل ریاضی تخصیص ناوگان، همراه با زمان‌بندی فعالیت‌های تعمیر، نگهداری و رمپینگ هواپیما Mathematical Model for Fleet Assignment with Maintenance and Aircraft Ramping Scheduling https://aie.ut.ac.ir/article_66291.html 10.22059/jieng.2018.207009.1121 1 The problem of routing and maintenance programming is one of the most important complex issues of aviation systems. Therefore, the factors that increase the delays and costs and make the passengers unsatisfied must be identified. Among these factors, is the time required for airplane ramping, inspection and maintenance operations that directly affect flight delay and related costs. This paper provides two new models for maintenance programming based on flight hours and scheduling of airplane ramp operations, which minimizes costs and delays. Two mathematical models are solved in GAMS and sensitivity analysis is performed for each. Results of sensitivity analysis show that an increase in the number of aircraft in maintenance model reduces costs, and an increase in the number of machines in ramping model reduces delays. So, based on the result, good performance of the models reduces the costs and delays. 0 مسئلة مسیریابی و برنامه‌ریزی تعمیرات هواپیما از جمله مسائل مهم و پیچیده سیستم‌های حمل‌ونقل هوایی است؛ از این‌رو باید عواملی که بر افزایش تأخیرها و هزینه‌ها تأثیرگذار است و موجب نارضایتی مسافران می‌شود شناسایی شوند. ازجمله این عوامل می‌توان به زمان مورد نیاز برای انجام عملیات رمپینگ هواپیما و انجام عملیات بازرسی و تعمیر و نگهداری آن اشاره کرد که مستقیماً بر روی تأخیرات پروازی و هزینه‌ها اثرگذار است، این پژوهش به ارائة دو مدل جدید برای برنامه‌ریزی تعمیرات هواپیما براساس ساعات پرواز (نه روزهای پرواز) و زمان‌بندی فعالیت‌های رمپینگ هواپیما می‌پردازد؛ به‌طوری‌که هزینه‌ها و تأخیرات را مینیمم می‌کند. دو مدل ریاضی ارائه‌شده در نرم‌افزار گمز حل شده و برای هرکدام تحلیل حساسیت انجام گرفته است که نتایج این تحلیل نشان می‌دهد در مدل تعمیر و نگهداری، افزایش تعداد هواپیماها سبب کاهش هزینه‌ها و در مدل رمپینگ افزایش ماشین‌آلات سبب کاهش جزئی تأخیرات می‌شود. نتایج به‌دست‌آمده حاکی از کارایی مناسب مدل‌ها در کاهش هزینة تعمیر و نگهداری و تأخیرات پروازی است. 61 72 علیرضا رشیدی کمیجان Alireza Rashidi Komeijan Department of Industrial Engineering, Firuzkuh Branch, Islamic Azad University Firuzkuh, Tehran, Iran Iran rashidi@azad.ac.ir مهسا شبانکاره Mahsa Shabankareh Department of Industrial Engineering, Firuzkuh Branch, Islamic Azad University Firuzkuh, Tehran, Iran Iran mahsa_sh701@yahoo.com Aircraft Routing maintenance Mathematical Modeling Ramping 1. Abara. J. (1989). “Applying integer linear programming to the fleet assignment problem”, Interfaces, Vol. 19, No. 4, PP. 20-28.##2. Desaulniers, G. et al., (1997). “Daily aircraft routing and scheduling”, Management Science, Vol. 43, No. 6, PP. 841-855.##3. Yan, S. Y. and Tseng, C. H. (2002). “A passenger demand model for airline flight scheduling and fleet routing”, Computers and Operations Research, Vol. 29, No. 11, PP. 1559-1581.##4. Sherali, H. D. and Zhu, X. (2008). “Two-stage fleet assignment model considering stochastic passenger demands”, Operations Research, Vol. 56, No. 2, PP. 383- 399.##5. Sherali, H. D., Bae, K. H. and Haouari, M. (2010). “Integrated airline schedule design and fleet assignment: Polyhedral analysis and Bender’s Decomposition approach”, INFORMS Journal on Computing, Vol. 22, No. 4, PP. 500-513.##6. Tran, Van Hoai. (2013). “Airline fleet assignment”, Faculty of Computer Science and Engineering HCMC University of Technology, PP.1-15.##7. Wang, Y., Sun, H., Zhu, J. and Zhu, B., (2015). “Optimization model and algorithm design for airline fleet planning in a multiairline competitive environment”, Mathematical Problems in Engineering, Vol. 13, No. 1 , PP. 1-13.##8. Shao, S., Sherali, H. D. and Haouari, M., (2015). “A novel model and decomposition approach for the integrated airline fleet assignment, aircraft routing and crew pairing problem”, Transportation Science, Vol. 51, No.1, PP. 233-249.##9. Jamili, A. (2017). “A robust mathematical model and heuristic algorithms for integrated aircraft routing and scheduling with consideration of fleet assignment problem”, Journal of Air Transport Management, Vol. 58,                        No. 1, PP. 21-30.##10. Gurkan, H., Gurel, S. and Akturk, M. S. (2016). “An integrated approach for airline scheduling, aircraft fleeting and routing with cruise speed control”, Transportation Research, Part C: Emerging Technologies, Vol. 68, No. 1, PP. 38-57.##11. Gopalan, R. and Talluri, K. T. 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L., et al., (2012). “Airline planning and schedule development”, International Series in Operations Research & Management Science, Vol.169, No. 1, PP. 35-99.##17. Basdere, M. and Bilge, U. (2014). “Operational aircraft maintenance routing problem with remaining time consideration”, European Journal of Operational Research, Vol. 235, No. 1, PP. 315-328.##18. Wijk, O., Andersson, P., Block, J. and Righard, T., (2017). “Phase out maintenance optimization for an aircraft fleet”, International Journal of Production Economics, Vol. 188, No. 1, PP. 105-115.##19. Ben Ahmed, M., Zeghal Mansour, F. and Haouari, M. (2016). “A two level optimization approach for robust aircraft routing and retiming”, Computers and Industrial Engineering, PP. 1-24.##20. Feighan, A. and Feighan, K. (1997). “Airport services and airport charging systems: A critical review of the EU common framework”, Transportation Research Part E: Logistics and Transportation Review, Vol. 33, No. 4, PP. 311-320.##21. 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(2014). “The cost allocation approach of airport service activities”, Journal of Air Transport Management, Vol. 38, No. 1, PP. 48-53.##27. Selinka, G., Franz, A. and Stollets, R. (2016). “Time dependent performance approximation of tuck handling operations at an air cargo terminal”, Computers and Operations Research, Vol. 65, No. 3, PP. 164-173.##28. Studic, M., Majumdar, A. and Schuster, W. (2017). “A systematic modeling of ground handling services using the functional resonance analysis method”, Transportation Research, Part C, Vol. 74, No. 4, PP. 245-260.##

1 یک مدل بهینه‌سازی استوار سناریو - محور برای مسئلۀ مسیریابی وسایط نقلیۀ دوره‌ای با پنجرۀ زمانی تحت شرایط عدم قطعیت با استفاده از الگوریتم تکامل تفاضلی A Scenario-Based Robust Optimization Model for a Periodic Vehicle Routing Problem with Time Windows under Uncertainty by Using a Differential Evolution Algorithm https://aie.ut.ac.ir/article_66292.html 10.22059/jieng.2018.229667.1345 1 This paper provides a model for evaluating the efficiency of a periodic vehicle routing problem (PVRP) to get the short routes with maximum sale by providing suitable services to customers before delivering the goods by other competitor distributors. In the goods distribution with short lifetime that customers need a special device for keeping them, the arriving time to customers influence on the sales amount, in which classical VRPs are unable to calculate this kind of assumptions. According to real world applications, the arriving time of the competitors is uncertain because of customer demands, traffic, weather conditions, and the like. A scenario approach is employed to handle the uncertainty of the arriving time of rivals. The purpose of this paper is to solve this problem by optimizing the sale of products to customers before delivering the products to other competitor distributors in an uncertain condition by robust optimization. To evaluate the presented model, a number of test problems are solved by two strategies of a differential evolution (DE) algorithm. Results are compared with those obtained by the CPLEX method in GAMS in small and medium sizes. To evaluate the proposed algorithm for solving large-scale problems, some solutions are implemented, and the results are compared in term of their accuracy. The computational results represent the capability of the proposed DE strategies in solving large-scale problems in a reasonable time. 0  در این مقاله یک رویکرد ریاضی به‌منظور بررسی و ارزیابی مسئله مسیریابی دوره‌ای وسایط نقلیه با پنجرة زمانی در محیط رقابتی با توجه به عدم قطعیت در زمان سرویس‌دهی رقبا به مشتریان ارائه خواهد شد. هدف از این مقاله، ارائة یک مدل دوهدفه شامل کمینه‌سازی هزینة حمل‌ونقل از طریق انتخاب کوتاه‌ترین مسیر و بیشینه‌کردن سود ناشی از توزیع کالا با درنظرگرفتن عدم قطعیت زمان سرویس‌دهی رقبا به مشتریان با استفاده از رویکرد بهینه‌سازی استوار تحت سناریوست. به‌منظور ارزیابی کارایی مدل ارائه‌شده از دو راهبرد کارای الگوریتم تکامل تفاضل استفاده، و نتایج به‌دست‌آمده در ابعاد کوچک و متوسط با نتایج حاصل از روش حل دقیق شد، همچنین به‌منظور ارزیابی عملکرد راهبردهای پیشنهادی، تعدادی مسئله نمونه در ابعاد بزرگ ایجاد، و نتایج مقایسه و بررسی شد. نتایج محاسباتی نشان می‌دهد که راهبردهای پیشنهادی عملکرد مناسبی در حل مدل پیشنهادی دارد. 73 86 علیرضا سلامت بخش ورجوی Ali Reza Salamat Bakhsh Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Iran alirezasalamatbakhsh@gmail.com رضا توکلی مقدم Reza Tavakkoli-Moghaddam Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Iran tavakoli@ut.ac.ir مهدی علینقیان Mehdi Alinaghian Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Iran alinaghian@iust.ac.ir اسماعیل نجفی Ismaeil Najafi Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Iran najafi1515@yahoo.com Differential Evolution Periodic Vehicle Routing Problem Robust optimization uncertainty 1.   Kos, C., and Karaoglan, I. 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1 مدل‌سازی ریاضی و حل مسئلۀ زمان‌بندی تولید کار کارگاهی انعطاف‌پذیر با جریان‌های معکوس Mathematical Modeling and Solving Flexible Job-Shop Production Scheduling with Reverse Flows https://aie.ut.ac.ir/article_66293.html 10.22059/jieng.2018.228481.1333 1 One of the important issues in the field of flexible job-shop production scheduling is reverse flows within a single production unit, as is the case in the assembly/disassembly plants. This paper studies the flexible job-shop scheduling by employing reverse flows approach, which consists of two flows of jobs at each stage in opposite directions. The problem can be used only if you have two flows: the first one going from first stage to last stage, and the second flow going from last stage to first stage. Then, a mathematical model of problem is provided to minimize the maximal completion time of the jobs (i.e., the makespan). Because of the complexity solving and proving that this problem ranked on NP-hard problems, we proposed meta-heuristic algorithm genetic (GA). Also, The parameters of these algorithm GA and their appropriate operators are obtained by the use of the Taguchi experimental design. The computational results validate outperforms proposed algorithm GA. 0 یکی از مسائل مهم در سیستم‌های تولید کارگاهی انعطاف‌پذیر، توجه به جریان‌های معکوس درون شبکه مونتاژ/ جداسازی است. در این پژوهش، مسئلة زمان‌بندی تولید کار کارگاهی انعطاف‌پذیر با رویکرد جریان‌های معکوس که از دو جریان کارها (مستقیم و معکوس) در هر مرحله متشکل است، بررسی می‌شود. این مسئله زمانی کاربرد دارد که شما با دو جریان مواجه باشید که جریان (کار) رفت از مرحلة اول به آخر و جریان (کار) برگشت از مرحلة آخر به اول به‌کار برده شود سپس یک مدل ریاضی از مسئله با هدف کمینه‌سازی معیار بیشینة زمان تکمیل کارها یا به عبارتی Cmax ارائه می‌شود. با توجه به پیچیدگی حل و Np-hard‌بودن این مسئله، از الگوریتم ژنتیک بهره می‌گیریم. همچنین با استفاده از طراحی آزمایش‌ها و روش تاگوچی، مقدار مناسب پارامترهای الگوریتم ژنتیک را برآورد می‌کنیم. تحلیل نتایج، بیانگر کارایی الگوریتم ژنتیک برای حل مدل پیشنهادی است. 87 96 فاطمه سلیمانی‌نیا Fatemeh Soleimaninia Department of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran Iran fatemesoleimaninia@gmail.com اسماعیل مهدی زاده Esmaeil Mehdizadeh Department of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran Iran emqiau@yahoo.com Flexible Job-Shop Genetic Algorithm Production Scheduling Mathematical Modeling Reverse Flows Taguchi Method 1-        Beheshtinia, M.A., Ghasemi, A. and Farokhnia, M. (2016). “Production and Transportation Scheduling and Allocation of Orders in the Supply Chan”, Journal of Industrial Engineering,University of Tehran, Vol.50, No.2,PP. 191-203.##2-        YousefiBabadi, A. and Shishebori, D. (2015). “Robust Optimization of integrated reverse logistic network design at uncertain conditions”, Journal of Industrial Engineering,University of Tehran, Vol.49, No.2,PP. 299-313.##3-        Kim, H.J., Lee, D.H. and Xirouchakis, P. (2007). ‌“Disassembly scheduling: literature review and future research directions.”International Journal of Production Research, Vol.45, No.18-19, PP.4465-4484.##4-        Brennan, L., Gupta, S.M. and Taleb, K.N.  (1994). “Operations planning issues in an assembly/disassembly environment, International Journal of Operations & Production Management, Vol.14, No.9, PP.57-67.##5-        Duta, L., Filip, F.G. and Popescu, C. (2008), Evolutionary programming in disassembly decision making”, Int. J. Comput. Commun. Control, Vol.3, No.3, PP.282-286.##6-        Dondo, R.G. and Méndez, C.A. (2016). “Operational planning of forward and reverse logistic activities on multi-echelon supply-chain networks”, Computers and Chemical Engineering, Vol.88, No.??? , PP.170-184.##7-        Osmani, A. and Zhang, J. (2017), Multi-period stochastic optimization of a sustainable multi-feedstock second generation bioethanol supply chain− A logistic case study in Midwestern United States”, Land Use Policy, Vol.61, No.1, PP.420-450.##8-        Giri, B.C., Chakraborty, A. and Maiti, T. (2017). Pricing and return product collection decisions in a closed-loop supply chain with dual-channel in both forward and reverse logistics”, Journal of Manufacturing Systems,Vol.42, No.??? , PP.104-123.##9-        Gungor, A. and Gupta, S.M. (2001). A solution approach to the disassembly line balancing problem in the presence of task failures”, International Journal of Production Research, Vol.39, No.7, PP.1427-1467.##10-      Gupta, S.M., McGovern, S.M. and Kamarthi, S.V,J .(2003). “Solving disassembly sequence planning problems using combinatorial optimization”, In Proceedings of the 2003 Northeast Decision Sciences Institute Conference (PP. 178-180).##11-      Kongar, E. and Gupta, S.M. (2002), A multi-criteria decision making approach for disassembly-to-order systems”, Journal of Electronics Manufacturing, Vol.11, No.2, PP.171-183.##12-      González, B. and Adenso-D. B. (2006). “A scatter search approach to the optimum disassembly sequence problem”, Computers and Operations Research, Vol.33, No.6, PP.1776-1793.##13-      Ziaee, M. and Sadjadi, S.J.  “Mixed binary integer programming formulations for the flow shop scheduling problems. A case study: ISD projects scheduling”, Applied mathematics and computation, Vol.185, No.1, PP.218-228.##14-      Ilgin, M.A. and Gupta, S.M. (2011). “Recovery of sensor embedded washing machines using a multi-kanban controlled disassembly line”. Robotics and Computer-IntegratedManufacturing,Vol.27, No.2, PP.318-334.##15-      Gonnuru, V.K. (2013). “Disassembly planning and sequencing for end-of-life products with RFID enriched information”, Robotics and Computer-Integrated Manufacturing, Vol.29, No.3, PP.112-118.##16-      Li, X. et al. (2015). “Assembly oriented control algorithm of collaborative disassembly and assembly operation in collaborative virtual maintenance process”, Journal of Manufacturing Systems, Vol.36, No.1 , PP.95-108.##17-    Nonomiya, H. and Tanimizu, Y. (2017). “Optimal Disassembly Scheduling with a Genetic Algorithm”, Procedia CIRP,Vol.61, No.1, PP.218-222.##18-    Abdeljaouad, M.A. et al. (2015). “Job-shop production scheduling with reverse flows”, European Journal of Operational Research, Vol.244, No.1, PP.117-128.##19-    Molla-Alizadeh-Zavardehi, S., Hajiaghaei-Keshteli, M. and Tavakkoli-Moghaddam, R. (2011). “Solving a capacitated fixed-charge transportation problem by artificial immune and genetic algorithms with a Prüfer number representation”, Expert Systems with Applications, Vol.38, No.8, PP.10462-10474.##20-      Taillard, E. (1993). “Benchmarks for basic scheduling problems”. European Journal of Operational Research, Vol.64, No.2, PP.278-285.##

1 توزیع منصفانۀ پاداش مشترک میان واحدهای تصمیم‌گیری با استفاده از تحلیل پوششی داده‌ها The Fair Revenue Allocation among the Units of an Organization Using DEA https://aie.ut.ac.ir/article_66294.html 10.22059/jieng.2018.203268.1093 1 To develop an organization, its units must be continuously assessed, and awarded according to the assessment results. Otherwise, the inefficient units are located on the margin of safety, and the efficient units get disappointed. In this paper, a way is presented to fairly distribute a common reward among the units of organization. Using data envelopment analysis, this method establishes the share of each unit relative to its performance. To this end, first the minimum and maximum possible shares of each DMU are established. Then, a combination of them is used for identifying its final share. In this method, the shares are objectively determined regardless of personal tastes. 0  برای پیشرفت یک سازمان باید کارایی واحدهای تصمیم‌گیری آن به‌صورت مستمر ارزیابی، و هر واحد متناسب با کارایی خود تشویق شود. در غیر این صورت، واحدهای ناکارا در حاشیة امنیت قرار می‌گیرند و واحدهای کارا دلسرد می‌شوند. در این پژوهش، روشی برای توزیع منصفانة پاداش ثابت میان واحدهای یک سازمان ارائه می‌شود. این روش با استفاده از فن تحلیل پوششی داده‌ها سهم هر واحد را متناسب با کارایی آن تعیین می‌کند. برای این کار ابتدا کمترین و بیشترین پاداش ممکن هر واحد تعیین، سپس تلفیقی از آن‌ها به‌عنوان سهم پایانی آن واحد درنظر گرفته می‌شود. در این روش، سلیقة شخصی انسان در تعیین پاداش واحدها دخالتی ندارد. 97 101 اسفندیار لشنی Esfandiar Lashni Department of Mathematics, Islamic Azad University, Doroud Branch, Lorestan, Iran Iran elashani@yahoo.com کوروش آریاوش Kourosh Aryavash Department of Mathematics, Islamic Azad University, Doroud Branch, Lorestan, Iran Iran k.aryavash@yahoo.com Data Envelopment Analysis Efficiency Revenue Allocation Azadeh, A., Ghaderi, S.F. and Omrani, H. (2009). “A deterministic approach for performance assessment and optimization of power distribution units in Iran”, Energy Policy, Vol. 37, No. 1 , PP. 274-280.##Charnes, A., Cooper, W.W. and Rhodes, E. (1978). “Measuring the efficiency of DMUs”, European Journal of Operational Research, Vol. 2,No. 6, PP. 429- 444.##Khodabakhshi, M. and Aryavash, K. (2012). “Ranking all units in data envelopment analysis”, Applied Mathematics Letters, Vol.25, No. 12, PP. 2066- 2070.##Khodabakhshi, M. and Aryavash, K., (2014). “The fair allocation of common fixed cost or revenue using DEA concept”, Annals of Operations Research, Vol. 214, No. 1, PP. 187-194.##Khodabakhshi, M. and Aryavash, K., (2015). “Aggregating preference rankings using an optimistic-pessimistic approach”, Computers and Industrial Engineering, Vol. 85, No. 1, PP. 13–16.##Beasely, J.E. (2003). “Allocating fixed costs and resources via DEA”, European Journal of Operational Research, Vol.147, No. 1, PP. 198–216.##Cook, W.D. and Kress, M. (1999). "Characterizing an equitable allocation of shared costs: a DEA approach”, European Journal of Operational Research, Vol.119, No. 4, PP. 652–661.##Cook, W.D. and Zhu, J. (2005). “Allocation of shared costs among decision making units: a DEA approach”, Computers and Operations Research, Vol. 32, No. 8, PP. 2171–2178.##Jahanshahloo, G.R., Hossienzadeh Lotfi, F. and Morady, M. (2005). “A DEA approach for fair allocation of common revenue”, Applied Mathematics and Computation, Vol. 160, No. 3, PP. 719–724.##Li, Y., et al., (2009). “Allocating the fixed cost as a complement of other cost inputs: a DEA approach”, European Journal of Operational Research, Vol.197 No. 1, PP. 389–401.##

1 زمان‌بندی اتاق عمل برای جراحی الکتیو با درنظرگرفتن واحدهای مراقبت بعد از عمل به کمک برنامه‌ریزی آرمانی Operating Room Scheduling for Elective Surgeries Considering Downstream Care Units Using Goal Programming https://aie.ut.ac.ir/article_66295.html 10.22059/jieng.2018.218436.1247 1 In a hospital, operating rooms produces a large part of the costs, on the one hand, and a large part of the income, on the other hand. One of the most impressive ways to increase the operating rooms efficiency is using effective ways for programming and scheduling. While this department has a close relation with other departments in the hospital, improving its efficiency will raise patients satisfaction and the whole hospital efficiency and performance. In this paper, a stochastic integer programming model has been developed for operating rooms programming and scheduling, with the aim of minimizing the cost of underutilization and overcapacity in downstream units, including intensive care unit and wards. The model aims to provide a cyclic master surgery scheduling at the tactical level based on hospital strategic decisions. First, it allocates blocks to specialties based on block scheduling strategy, and secondly determines each surgeon’s surgery schedule. In addition, to improve the efficiency and reduce the complexity of the model for the large scale cases, the convolution stochastic distribution parts of the model have been exchanged with the expected values and variances of the capacities needed for different days of the week in a corresponding integer goal programming model. Then, different case studies have been generated by changing some of the parameters to show the objective function sensitivity to the changes. Innovation of this research compared to the previous studies is providing the schedules for the surgeons rather than the specialties. This saves time, expenses, and computational operations. Also, positive half variance of the number of patients in downstream units in each day of the week has been used directly rather than the variance of the number of patients in these units. 0 در این مقاله، یک مدل برنامه‌ریزی آرمانی با متغیرهای صحیح برای زمان‌بندی اتاق‌های عمل به‌صورت بلوکی ارائه شده که در آن زمان‌های مورد نیاز بستری بیماران تصادفی درنظر گرفته شده است. هدف برای زمان‌بندی اصلی جراحی، کمینه‌کردن هزینه‌های واحدهای مراقبت پایین‌دستی است. برای کاهش پیچیدگی محدودیت‌های ظرفیت، از مقادیر مورد انتظار و نیم‌انحراف معیار مثبت تعداد بیماران در واحدهای مراقبت پایین‌دستی استفاده، و مثال‌ها با کمک نرم‌افزار گمز حل شده است. نوآوری پژوهش در این است که اولاً به هر دو معیار کمبود ظرفیت و خالی‌ماندن تخت توجه شده و ثانیاً جراحان برای رسیدن به این اهداف، زمان‌بندی شده‌‌اند. با استفاده از مدل پیشنهادی می‌توان روز (های) مناسب برای هر جراح را به‌نحوی تعیین کرد که کارایی واحدهای مراقبت پایین‌دستی در حداکثر سطح ممکن باشد و برای این کار باید به سطح خدمت موردنظر و محدودیت بلوک‌های تخصیصی به سرویس‌ها توجه شود. 103 112 ناهید محمودیان Nahid Mahmoudian Department of Management, University of Isfahan, Isfahan, Iran Iran nahidmahmoodian3000@yahoo.com سعیده کتابی Saeedeh Ketabi Department of Management, University of Isfahan, Isfahan, Iran Iran sketabi@yahoo.com آرزو عتیقه چیان Arezo Atighechian Department of Management, University of Isfahan, Isfahan, Iran Iran a.atighehchian@yahoo.com Block Scheduling Strategy Downstream Units Goal Integer Programming Master Surgery Scheduling Tactical Surgery Scheduling 1. Cardeon, B., Demeulemeester, E. and Belien, J. (2010). “Operating room planning and scheduling”, A literature review. European Journal of Operational Research, Vol. 201, No. 3, PP. 921-932.##2.Min, D. and Yih, Y. (2010). “Scheduling elective surgery under uncertainty and downstream capacity constraints”, European Journal of Operational Research, Vol. 206, No. 3, PP. 642-652.## 3. Hans, E.W., van Houdenhoven, M., and Hulshof, P.J.H. (2011). “A framework for health care planning and control”, In R. Hall (ED), Handbook of health care systems scheduling, Springer.international series in operations research and management science, Vol. 168, PP.303-320, New York: Springer.##4. Van Oostrum, J.M. et al., (2008). “A master surgery scheduling approach for cyclic scheduling in operating room departments”, OR Spectrum, Vol. 30, No. 2, PP. 355–374.##5. Carter, M. and Ketabi, S. (2013). “Bed Balancing in Surgical Wards via Block Scheduling”, Journal of Minimally Invasive Surgical Sciences. 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1 رویکردی برای برنامه‌ریزی طرح درمان در پرتودرمانی با شدت تنظیم‌شده (IMRT) An Approach for Treatment Programming in Intensity Modulated Radiation Therapy (IMRT) https://aie.ut.ac.ir/article_66296.html 10.22059/jieng.2018.225242.1302 1 Intensity modulated radiation therapy is one of the most commonly procedures used for delivering radiation to cancerous tissues. It aims to deliver the prescribed dose in the target volume while minimizing damage to the nearby healthy organs. In this procedure, two decisions being very important are selecting the beam angles and calculating the beam intensities. Although beam angle selection (beam angle optimization) is one of the most important decisions in this procedure, it is often be made manually and based on radio therapist experience and intuition. In order to overcome this drawback, this paper proposes a hybrid approach for automated beam angle selection and intensity computation. The proposed approach first finds a good feasible solution, and then use this solution as a starting point in local neighborhood search to find the local optimal solution. As the results of numerical experiments demonstrate, the proposed hybrid-approach, compared to its corresponding stand-alone methods, finds a better solution quickly and consistently. 0 پرتودرمانی با شدت تنظیم‌شده روشی رایج برای انتقال اشعه به بافت سرطانی، با هدف انتقال دوز تجویزشده به حجم هدف و کاهش آسیب به اندام‌های سالم اطراف تومور است. به‌طورمعمول، در این فرایند دو تصمیم اهمیت زیادی دارد: انتخاب زوایای پرتو و محاسبة شدت پرتوها. علی‌رغم اینکه انتخاب زوایای پرتو (بهینه‌سازی زوایای پرتو) در این مسائل بسیار اهمیت دارد، اغلب براساس تجربه انجام شده است و دقت کافی ندارد. برای حل این مشکل، پژوهش حاضر بر آن است تا چارچوبی ترکیبی برای انتخاب خودکار زوایای پرتو و محاسبة شدت پرتوها در این روش رادیوتراپی ارائه کند. رویکرد ارائه‌شده ابتدا با استفاده از روش‌های ابتکاری، نقطه‌ای شدنی با کیفیت خوب برای مسئله پیدا کرده سپس آن را به‌عنوان نقطة شروع در الگوریتم جست‌وجوی همسایگی برای یافتن بهینة محلی به‌کار گرفته است. با توجه به نتایج محاسباتی درمی‌یابیم که استفاده از این رویکرد ترکیبی به‌جای رویکردهای انفرادی در زمان کوتاه‌تر به جواب بهینة محلی با کیفیت خوب دست پیدا می‌کند. 113 124 مهدی نجفی Mehdi Najafi Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran Iran najafi.mehdi@sharif.edu مهسا فریدمهر Mahsa Faridmehr Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran Iran faridmehr_m@ie.sharif.edu Beam Angle Optimization Fluence Map Optimization Intensity Modulated Radiation Therapy Treatment Programming 1. 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Designing radiotherapy plans with elastic constraints and interior point methods, Health care management science, Vol. 6, No. 1, PP. 5-16.##6. Spirou, S.V. and C.-S. Chui. (1998). A gradient inverse planning algorithm with dose-volume constraints. Medical physics, Vol. 25, No. 3, PP. 321-333.##7. Bednarz, G. et al. (2004). Inverse treatment planning using volume-based objective functions, Physics in Medicine and Biology, Vol. 49, No. 12, P. 2503.##8. Langer, M. et al. (1990). Largescale optimization of beam weights under dose-volume restrictions, Vol. 18, No. 4, PP. 887-893.##9. Lee, E.K., T. Fox, and I. Crocker, (2003), Integer programming applied to intensity-modulated radiation therapy treatment planning. Annals of Operations Research, Vol. 119, No. 1-4, PP. 165-181.##10. Romeijn, H.E., Dempsey. J.F and J.G. Li. (2004). A unifying framework for multi-criteria fluence map optimization models, Physics in Medicine and Biology, Vol. 49, No. 10, P. 1991.##11. 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Kirkpatrick, S. (1984). Optimization by simulated annealing: Quantitative studies, Journal of statistical physics, Vol. 34, No. 5-6, PP. 975-986.##17. Bertsimas, D. et al. (2013). A hybrid approach to beam angle optimization in intensity-modulated radiation therapy, Computers and Operations Research, Vol. 40, No. 9, PP. 2187-2197.##18. Lin, S., Lim, G.J., and J.F. (2016). Bard, Benders decomposition and an IP-based heuristic for selecting IMRT treatment beam angles, European Journal of Operational Research, Vol. 251, No. 3, PP. 715-726.##19. Lim, G.J., Choi, J., and Mohan, (R. 2008). Iterative solution methods for beamangle and fluence map optimization in intensity modulated radiation therapy planning, OR Spectrum, Vol. 30, No. 2, PP. 289-309.##20. Lim, G.J., and Cao, W. (2012), A two-phase method for selecting IMRT treatment beam angles: Branch-and-Prune and local neighborhood search, European Journal of Operational Research, Vol. 217, No. 3, PP. 609-618.##21. Zhang, H.H. et al. (2009). Solving beam-angle selection and dose optimization simultaneously via high-throughput computing, INFORMS Journal on Computing, Vol. 21, No. 3, PP. 427-444.##22. Li, Y. et al. (2005). A particle swarm optimization algorithm for beam angle selection in intensity-modulated radiotherapy planning, Physics in medicine and biology, Vol. 50, No. 15, P. 3491.##23. Ahnesjö, A., Saxner, M., and Trepp, A. (1992). A pencil beam model for photondose calculation, Medical physics, Vol. 19, No. 2, PP. 263-273.##24. Deasy, J.O., Blanco, A.I., and Clark, V.H. (2003). CERR: A computational environment for radiotherapy research, Medical Physics, Vol. 30, No. 5, PP. 979-985.##25. Group, I.M.R.T.C.W., (2001), Intensity-modulated radiotherapy: current status and issues of interest, International Journal of Radiation Oncology* Biology* Physics, Vol. 51, No. 4, PP. 880-914.##

1 توسعۀ مدل تولید اقتصادی در زنجیره‌های تأمین سه‌سطحی یکپارچه و غیریکپارچه با درنظرگرفتن سیاست بهینۀ کنترل موجودی Developing an Economic Production Quantity Model in Integrated and Non-integrated Three-Layer Supply Chains with an Optimal Inventory Control Policy https://aie.ut.ac.ir/article_66297.html 10.22059/jieng.2018.201740.1082 1 This paper develops an economic production quantity model in a three-layer supply chain with two different structures. This chain composes of a supplier, a manufacturer, and multiple retailers. In this chain, the supplier transforms raw material to the semi-finished product, and sends them to the manufacturer. Then, the manufacturer transmutes them to the finished product, and delivers them to the retailers to satisfy market demand. The retailers replenish their inventory at the same time. The demand of each retailer is different due to essence of various demand customers, and according to the decisions of the chain’s members, two structures of non-integrated and integrated supply chains are surveyed. In this paper, we will employ the Stackelberg approach to solve the presented models. The ordering cycle of retailers is the decision variable of the model. The main aim of this study is to develop an inventory and production model in three-layer supply chains to minimize the total cost of chain by utilizing the optimal inventory control policy. At last, numerical examples are presented for each structure of supply chains. 0 در این تحقیق، به توسعة مدل تولید اقتصادی در زنجیرة تأمین سه‌سطحی با دو ساختار متفاوت می‌پردازیم. زنجیره‌ای متشکل از یک تأمین‌کننده، یک تولیدکنندهو چندین خرده‌فروش که تأمین‌کننده مواد اولیه را به کالای پیش‌ساختهتبدیل می‌کند و در اختیار تولیدکننده قرار می‌دهد، تولیدکننده نیز آن‌ها را به کالای نهایی تبدیل و برای خرده‌فروشان ارسال می‌کند تا بدین ترتیب خرده‌فروشان بتوانند پاسخگوی تقاضای بازار باشند. گفتنی است آن‌ها بازپرسازیموجودیشان را به‌طور هم‌زمان انجام می‌دهند و از آنجا که هریک مشتریان خاص خود را دارند، تقاضایشان با یکدیگر متفاوت است. با توجه به تصمیمات اعضای زنجیره، دو ساختار زنجیرة تأمین غیریکپارچه و یکپارچه بررسی شده است. در این پژوهش، ما از رویکرد استکلبرگ برای حل مدل‌های ارائه‌شده استفاده خواهیم کرد. سیکل سفارش‌دهی خرده‌فروشان متغیر تصمیم مدل است. هدف اصلی این پژوهش، توسعة مدل موجودی و تولید در زنجیره‌های تأمین سه‌سطحی در راستای کاهش هزینه‌های زنجیرة تأمین با استفاده از سیاست بهینة کنترل موجودی است. در پایان، نمونه مسائلی برای هریک از ساختار‌های زنجیرة تأمین ارائه شده است. 125 137 مهسا نوری داریان Mahsa Noori Daryan Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran Iran m.nooridaryan@ut.ac.ir عطاالله طالعی زاده Ataollah Taleizadeh Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran Iran taleizadeh@ut.ac.ir Game theory Inventory Control Production Planning Stackelberg Equilibrium Supply Chain Supply Chain Management Laudon, K.C., and Laudon, J.P. 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