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Italy

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Operations Research & Supply Chain Optimization Expert | Machine Learning | Mathematical Modeling

Profile Summary

I am an Operations Research and Industrial Engineering researcher with over 15 years of academic and industrial experience in mathematical optimization, supply chain analytics, and decision support systems. I currently conduct research at the University of Milano-Bicocca, Italy, where I develop advanced optimization methods that integrate machine learning and optimal transport for solving complex real-world decision-making problems. My expertise spans mathematical optimization, Bayesian optimization, combinatorial optimization, robust and stochastic optimization, supply chain network design, logistics, production planning, inventory management, scheduling, and network optimization. I have extensive experience in designing exact algorithms, metaheuristics, and data-driven optimization models for industrial and research applications. My recent research focuses on Bayesian optimization, Wasserstein and Gromov-Wasserstein optimal transport, distributionally robust optimization, uncertainty quantification, and machine learning-enhanced optimization. I have published in leading international journals, including Expert Systems with Applications, Mathematics, Machine Learning and Knowledge Extraction, Environment, Development and Sustainability, Big Data and Cognitive Computing, and Public Transport. Beyond academia, I have worked as an optimization specialist in manufacturing and supply chain industries, developing optimization models for production planning, inventory control, logistics, and resource allocation. This combination of research excellence and practical industrial experience enables me to deliver solutions that are both scientifically rigorous and directly applicable to business challenges. I can assist clients with: Mathematical modeling and optimization Operations research and decision analytics Supply chain and logistics optimization Facility location and network design Scheduling and resource allocation Bayesian optimization and surrogate modeling Robust and stochastic optimization Metaheuristic algorithm development Python, Gurobi, MATLAB, and optimization software implementation Machine learning for optimization and decision support Whether you need support with research, algorithm development, optimization modeling, scientific consulting, or industrial decision-making projects, I am committed to delivering high-quality, reliable, and innovative solutions tailored to your objectives.

Subject Matter Expertise

• ☆ Industrial Engineering
• ☆ Industrial Operations Research
• ☆ Manufacturing
• ☆ Manufacturing Optimization
• ☆ Data Analysis
• ☆ Computer Simulation/Computational Modeling
• ☆ Machine Learning
• ☆ Applied Mathematics
• ☆ Mathematical Optimization
• ☆ Supply Chain & Logistics

Services

• Writing

Writing Technical Writing

Work Experience

Assegnisti

Universita' di Milano Bicocca

June 2024 - May 2025

Assistant Professor

Payame Noor University

September 2010 - April 2024

Education

PhD (industrial engineering)

Payame Noor University

September 2015 - January 2020

Master of science (industrial engineering)

Mazandaran University of Science and Technology

September 2006 - November 2008

bachelor degree (industrial engineering)

Iran University of Science and Technology

September 2002 - September 2006

Certifications

• Certification details not provided.

Publications

JOURNAL ARTICLE

Iman Seyedi, Antonio Candelieri, Francesco Archetti (2026). Geometric Optimal Transport for Sustainable Closed-Loop Supply Chain: A Fused Gromov–Wasserstein Framework for Structural and Attribute Inefficiency Diagnosis . Sustainability.

Iman Seyedi, Antonio Candelieri, Francesco Archetti (2026). When to Explore and When to Exploit: Adaptive Decisions in Bayesian Optimization . Machine Learning and Knowledge Extraction.

Iman Seyedi, Antonio Candelieri, Francesco Archetti (2026). When to Explore and When to Exploit: Adaptive Decisions in Bayesian Optimization . Machine Learning and Knowledge Extraction.

Iman Seyedi, Antonio Candelieri, Francesco Archetti, Andrea Ponti (2026). Resource Allocation via Bayesian Optimization in Wasserstein Spaces vs. Semi-Bandit Feedback . Big Data and Cognitive Computing.

Iman Seyedi, Antonio Candelieri, Enza Messina, Francesco Archetti (2026). Gromov–Wasserstein Meets Combinatorial Optimization: A Scalable Solver for the Capacitated Quadratic Assignment Problem . Mathematics.

Iman Seyedi, Antonio Candelieri, Enza Messina, Francesco Archetti (2026). Gromov–Wasserstein Meets Combinatorial Optimization: A Scalable Solver for the Capacitated Quadratic Assignment Problem . Mathematics.

Iman Seyedi, Antonio Candelieri, Francesco Archetti, Andrea Ponti (2026). Resource Allocation via Bayesian Optimization in Wasserstein Spaces vs. Semi-Bandit Feedback . Big Data and Cognitive Computing.

Iman Seyedi, Antonio Candelieri, Francesco Archetti (2026). Fused Unbalanced Gromov–Wasserstein-Based Network Distributional Resilience Analysis for Critical Infrastructure Assessment . Mathematics.

Candelieri, A and Archetti, F and Seyedi, I(2026). When to Explore and When to Exploit: Adaptive Decisions in Bayesian Optimization . MACHINE LEARNING AND KNOWLEDGE EXTRACTION. 8. (7). MDPI

Candelieri, A and Archetti, F and Seyedi, I and Ponti, A(2026). Resource Allocation via Bayesian Optimization in Wasserstein Spaces vs. Semi-Bandit Feedback . BIG DATA AND COGNITIVE COMPUTING. 10. (7). MDPI

Gromov–Wasserstein Meets Combinatorial Optimization: A Scalable Solver for the Capacitated Quadratic Assignment Problem @article{ 10281_615161, author = {Seyedi, I and Candelieri, A and Messina, E and Archetti, F}, title = {Gromov–Wasserstein Meets Combinatorial Optimization: A Scalable Solver for the Capacitated Quadratic Assignment Problem}, year = {2026}, publisher = {MDPI}, journal = {MATHEMATICS}, volume = {14}, doi = {10.3390/math14111972}, number = {11} } .

Fused Unbalanced Gromov–Wasserstein-Based Network Distributional Resilience Analysis for Critical Infrastructure Assessment @article{ 10281_589725, author = {Seyedi, I and Candelieri, A and Archetti, F}, title = {Fused Unbalanced Gromov–Wasserstein-Based Network Distributional Resilience Analysis for Critical Infrastructure Assessment}, year = {2026}, publisher = {MDPI}, journal = {MATHEMATICS}, volume = {14}, doi = {10.3390/math14030417}, number = {3} } .

Iman Seyedi, Antonio Candelieri, Francesco Archetti (2026). Fused Unbalanced Gromov–Wasserstein-Based Network Distributional Resilience Analysis for Critical Infrastructure Assessment . Mathematics.

Iman Seyedi, Zahra Akbari-Aghghaleh, Ashkan Mozdgir, Enza Messina (2025). Designing a perishable closed-loop poultry supply chain: metaheuristic approaches and model evaluation . Environment, Development and Sustainability.

Iman Seyedi, Antonio Candelieri, Francesco Archetti (2025). Distributionally Robust Bayesian Optimization via Sinkhorn-Based Wasserstein Barycenter . Machine Learning and Knowledge Extraction.

Iman Seyedi, Antonio Candelieri, Francesco Archetti (2025). Distributionally Robust Bayesian Optimization via Sinkhorn-Based Wasserstein Barycenter . Machine Learning and Knowledge Extraction.

Iman Seyedi, Antonio Candelieri, Enza Messina, Francesco Archetti (2025). Wasserstein Distributionally Robust Optimization for Chance Constrained Facility Location Under Uncertain Demand . Mathematics.

Iman Seyedi, Antonio Candelieri, Enza Messina, Francesco Archetti (2025). Wasserstein Distributionally Robust Optimization for Chance Constrained Facility Location Under Uncertain Demand . Mathematics.

Seyedi, I and Candelieri, A and Messina, E and Archetti, F(2025). Wasserstein Distributionally Robust Optimization for Chance Constrained Facility Location Under Uncertain Demand . MATHEMATICS. 13. (13). MDPI

Rajabi-Kafshgar, A and Seyedi, I and Tirkolaee, E(2025). Circular closed-loop supply chain network design considering 3D printing and PET bottle waste . ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY. 27. (8). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 20345--20381. Springer Science and Business Media B.V.

Seyedi, I and Candelieri, A and Archetti, F(2025). Distributionally Robust Bayesian Optimization via Sinkhorn-Based Wasserstein Barycenter . MACHINE LEARNING AND KNOWLEDGE EXTRACTION. 7. (3). MDPI

Akbari-Aghghaleh, Z and Mozdgir, A and Seyedi, I and Messina, E(2025). Designing a perishable closed-loop poultry supply chain: metaheuristic approaches and model evaluation . ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY. Springer Dordrecht

Rajabi-Kafshgar, A., Seyedi, I., Tirkolaee, E.B.(2025). Circular closed-loop supply chain network design considering 3D printing and PET bottle waste . Environment Development and Sustainability. 27. (8). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 20345-20381.

Iman Seyedi, Atefeh Rajabi-Kafshgar, Erfan Babaee Tirkolaee (2024). Circular closed-loop supply chain network design considering 3D printing and PET bottle waste . Environment, Development and Sustainability.

Iman Seyedi, Atefeh Rajabi-Kafshgar, Fatemeh Gholian-Jouybari, Mostafa Hajiaghaei-Keshteli (2023). Utilizing hybrid metaheuristic approach to design an agricultural closed-loop supply chain network . Expert Systems with Applications.

Iman Seyedi, Seyed Amir Hosseini Baboli, Ahmad Arabkoohsar(2023). Numerical modeling and optimization of pressure drop and heat transfer rate in a polymer fuel cell parallel cooling channel . Journal of the Brazilian Society of Mechanical Sciences and Engineering. 45. (4). Springer Science and Business Media {LLC}

Hosseini Baboli, S and Arabkoohsar, A and Seyedi, I(2023). Numerical modeling and optimization of pressure drop and heat transfer rate in a polymer fuel cell parallel cooling channel . JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING. 45. (4). Springer Science and Business Media Deutschland GmbH

Rajabi-Kafshgar, A and Gholian-Jouybari, F and Seyedi, S and Hajiaghaei-Keshteli, M(2023). Utilizing hybrid metaheuristic approach to design an agricultural closed-loop supply chain network . EXPERT SYSTEMS WITH APPLICATIONS. 217. Elsevier Ltd

Hosseini Baboli, S.A., Arabkoohsar, A., Seyedi, I.(2023). Numerical modeling and optimization of pressure drop and heat transfer rate in a polymer fuel cell parallel cooling channel . Journal of the Brazilian Society of Mechanical Sciences and Engineering. 45. (4).

Rajabi-Kafshgar, A., Gholian-Jouybari, F., Seyedi, I., Hajiaghaei-Keshteli, M.(2023). Utilizing hybrid metaheuristic approach to design an agricultural closed-loop supply chain network . Expert Systems with Applications. 217.

Seyedi, I., Hamedi, M., Tavakkoli-Moghaddam, R.(2022). Enhancing the Search Capability of the Imperialist Competitive Algorithm for Truck Scheduling Problem in the Cross-Docking System . Journal of Operational Research and Its Applications. 19. (4).

Optimization for a truck scheduling problem in multi-door cross dokcing with learning effect and deteriorating jobs @article{Seyedi, 2022,author={Seyedi, Iman and Hamedi, Maryam and Tavakkoli-Moghaddam, Reza},title={Optimization for a truck scheduling problem in multi-door cross dokcing with learning effect and deteriorating jobs},journal={Journal of Transportation Research},volume={19},number={2},pages={183-206},year={2022},publisher={},issn={1735-3459},eissn={2008-3351},doi={10.22034/tri.2022.119317},abstract={In general, each supply chain consists of three main stages of procurement, production and distribution. The use of the cross-docking system is a new strategy at the distribution stage to improve customer response time by moving products directly from pickup trucks to delivery trucks. Generally, for an activity to be done both machine and human resources are needed. Many researchers have already developed numerous planning methods for cross-docking systems, but human resource constraints have largely ignored. In this paper, for the first time, we examine the problem of truck scheduling in multi-door cross-dock considering the learning effects and the deterioration of tasks to fill the gap between theoretical planning models and what is happening in the real world. We have proposed a mixed integer programming model for this problem. According to the research literature, with increasing the size of the problem, the complexity of integer programming model is expanding rapidly so that the exact methods can hardly achieve the optimal solution. To solve large-scale problems, five meta-heuristic algorithms are used including Genetic Algorithms (GA), Imperial Competitive Algorithm (ICA), Keshtel Algorithm (KA), and Social Engineering Optimization (SEO). Finally, the numerical results obtained from all meta-heuristic algorithms are analyzed. We compare the meta- heuristic algorithms based on the best, average, Rpd and time criteria. As a result, the SEO and KA algorithm performed better than the other algorithms in terms of solution quality.},keywords={cross-docking,scheduling,Learning Effect,Deterioration,Meta Heuristic},title_fa={بهینه سازی مسئله زمانبندی کامیون ها در انبار متقاطع چنددربی با در نظر گرفتن اثر یادگیری و زوال پذیری کارها},abstract_fa={به طور کلی هر زنجیره‌ی تامین شامل سه مرحله‌ی اصلی تهیه، تولید و توزیع است. استفاده از سیستم انبار متقاطع یک استراتژی جدید در مرحله توزیع برای بهبود زمان پاسخگویی به مشتریان با انتقال محصولات به طور مستقیم از کامیون های دریافت به کامیون های ارسالی است. به طور کلی برای پردازش یک فعالیت، هر دو منبع ماشین و منابع انسانی مورد نیاز است. بسیاری از محققان تا‌کنون روشهای برنامه ریزی متعددی برای سیستم های انبار متقاطع توسعه داده اند، اما اکثراً محدودیت های مهم منابع انسانی را نادیده گرفته اند. در این مقاله برای اولین بار به بررسی مسئله زمانبندی کامیون‌ها در انبار متقاطع چند دربی با در نظر گرفتن اثرات عوامل انسانی و زوال پذیری کارها برای پر کردن شکاف بین مدل های برنامه‌ریزی نظری و آنچه در دنیای واقعی انجام می گیرد پرداخته‌ایم و برای این منظور یک مدل برنامه‌ریزی عدد صحیح مختلط برای مسئله یاد شده ارائه شده است. با توجه به ادبیات تحقیق زمان حل مدل ارائه شده توسط روش های دقیق با افزایش اندازه مساله به سرعت افزایش می یابد تا حدی که روش‌های دقیق به سختی می‌تواند به جواب بهینه دست پیدا کنند. برای حل مسائل در مقیاس بزرگ از چهار الگوریتم فراابتکاری شامل الگوریتم‌های ژنتیک (GA)، رقابت استعماری (ICA)، کشتل (KA) و بهینه سازی مهندسی اجتماعی (SEO) استفاده شده است. در نهایت نتایج عددی بدست آمده از تمامی الگوریتم‌های فرا‌ابتکاری مورد بررسی و تحلیل حساسیت قرار گرفته‌اند. الگوریتم‌های فراابتکاری را بر اساس معیار های بهترین، میانگین‌ جواب‌ها، Rpd و زمان مورد مقایسه قرار داده‌ایم. در نتیجه الگوریتم‌های SEO و الگوریتم کشتل از نظر کیفیت جواب بهتر از سایر الگوریتم‌ها عمل نمودند.},keywords_fa={cross-docking,scheduling,Learning Effect,Deterioration,Meta Heuristic},url={http://www.trijournal.ir/article_119317.html},eprint={http://www.trijournal.ir/article_119317_37b2c172eb9cc6c040a312e7196b3ee4.pdf}} . Journal of Transportation Research.

Iman Seyedi, Maryam Hamedi, Reza TavakkoliMoghadaam (2021). Developing a mathematical model for a multi-door cross-dock scheduling problem with human factors: A modified imperialist competitive algorithm . Journal of Industrial Engineering and Management Studies.

Seyedi, I and Hamedi, M and Tavakkoli-Moghaddam, R(2019). Truck scheduling in a cross-docking terminal by using novel robust heuristics . INTERNATIONAL JOURNAL OF ENGINEERING. TRANSACTIONS B: APPLICATIONS. 32. (2). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 284--291. Materials and Energy Research Center (M E R C)

Seyedi, I., Hamedi, M., Tavakkoli-Moghaddam, R.(2019). Truck scheduling in a cross-docking terminal by using novel robust heuristics . International Journal of Engineering, Transactions B: Applications. 32. (2). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 284-291.

(2019). Truck Scheduling in a Cross-Docking Terminal by Using Novel Robust Heuristics . International Journal of Engineering.

Seyedi, I and Mirzazadeh, S and Malekidaronkolaei, A and Mukhtar, M and Sahran, S(2016). An inventory model with reworking and setup time to consider effect of inflation and time value of money . JOURNAL OF ENGINEERING SCIENCE & TECHNOLOGY. 11. (3). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 416--430. Taylor's University Sdn Bhd

Seyedi, I., Mirzazadeh, S., Malekidaronkolaei, A., Mukhtar, M., Sahran, S.(2016). An inventory model with reworking and setup time to consider effect of inflation and time value of money . Journal of Engineering Science and Technology. 11. (3). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 416-430.

(2014). Evaluation of Managers' Attitude Toward Effects of Targeted Subsidy: A Case Study (Vulnerable Food Industry in Mazandaran Province in Iran) . Journal of Applied Sciences.

Maleki-Daronkolaei, A and Seyedi, S(2013). Taguchi method for three-stage assembly flow shop scheduling problem with blocking and sequence-dependent set up times . JOURNAL OF ENGINEERING SCIENCE & TECHNOLOGY. 8. (5). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 603--622. Taylor's University Sdn Bhd

(2013). Using the Taguchi method to optimize the differential evolution algorithm parameters for minimizing the workload smoothness index in simple assembly line balancing . Mathematical and Computer Modelling.

Mozdgir, A., Mahdavi, I., Badeleh, I.S., Solimanpur, M.(2013). Using the Taguchi method to optimize the differential evolution algorithm parameters for minimizing the workload smoothness index in simple assembly line balancing . Mathematical and Computer Modelling. 57. (1-2). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 137-151.

(2013). Solving a two-stage assembly flowshop scheduling problem to minimize the mean tardiness and earliness penalties by three meta-heuristics. Caspian Journal of Applied Sciences Research.

Maleki-Daronkolaei, A., Seyedi, I.(2013). Taguchi method for three-stage assembly flow shop scheduling problem with blocking and sequence-dependent set up times . Journal of Engineering Science and Technology. 8. (5). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 603-622.

Presentation of Integrated Model of Consumer Behavior in Electronic Shopping @article{Shafizadeh, 2013, author= {Shafizadeh, H. and Seyedi, S. I. and Ghasemi Dalarsetaghi, I.}, title= {Presentation of Integrated Model of Consumer Behavior in Electronic Shopping}, journal= {Jounal of Marketing Management}, volume= {8}, number= {شماره 19}, pages= {13-28}, year= {2013}, publisher= {Science and Research Branch, Islamic Azad University,}, issn= {1735-949X}, eissn= {2008-255X}, doi= {}, abstract= {Consumers' behavior at the time of shopping on the internet like the traditional model of shopping is the result of some cultural, social, individual, psychological, etc factors. In the recent years researchers have considered shopping behavior in the internet environment and also have designed different models for electronic consumer behavior meanwhile identifying influencing factors. Compilation and review of different perspectives seemingly disparate and an integrated model of consumer behavior presentation is the primary purpose of this article. The article is dialectical and based on analysis, combination and integration of consumer behavior literature. Findings show despite the wide and repeated researches and studies about electronic consumer behavior areas like trust, electronic contracts, prospect, motivators, motives, etc. count to survey the electronic consumer behavior still. The paper using electronic consumer behavior literature and previous empirical researches and by presentation some new influencing factors on the consumer behavior has presented an integrated model of electronic consumer behavior while helping retailers in understanding the electronic consumer behavior. }, keywords= {Electronic consumer,Electronic consumer behavior,Electronic retailer,Electronic shopping}, title_fa= {ارائه مدل مفهومی رفتار مصرف کننده در خرید الکترونیکی}, abstract_fa= {رفتار مشتریان در هنگام خرید از اینترنت همانند مدل رفتار خرید سنتی، ناشی از یک سری عوامل فرهنگی، اجتماعی، فردی، روان شناختی و . . . می­باشد. محققان در طی چند سال گذشته رفتار خرید در محیط اینترنت را بررسی و ضمن شناسایی عوامل تاثیرگذار بر آن، مدل­های مختلفی را  برای رفتار مصرف کننده­ی الکترونیکی طراحی نموده­اند. هدف اولیه از این مقاله گردآوری و بررسی دیدگاه­های مختلف به ظاهر نابرابر و ارائه­ی یک مدل مفهومی یکپارچه از رفتار مصرف کننده الکترونیکی می­باشد. این مقاله استدلالی بوده و بر پایه تجزیه و تحلیل و ترکیب و تلفیق ادبیات رفتار مصرف کننده استوار می­باشد. یافته­های این تحقیق نشان می­دهد، با وجود طیف وسیع و تکراری تحقیقات و مطالعاتی که به بررسی رفتار مصرف کننده الکترونیکی پرداخته و می­پردازند، هنوز هم حوزه­هایی همچون، اعتماد، تعاملات الکترونیکی، تصویر ذهنی، عوامل برانگیزاننده، محرک­ها و . . . می­توانند حوزه­هایی ارزشمند برای بررسی رفتار مصرف کننده­ی الکترونیکی باشند. این تحقیق با بهره گیری از ادبیات رفتار مصرف کننده­ی الکترونیکی و تحقیقات تجربی گذشته و با ارائه­ی چندین عامل جدید موثر بر رفتار این نوع مصرف کننده، ضمن کمک به خرده فروشان در فهم رفتار مصرف کننده­ی الکترونیکی، یک مدل یکپارچه از رفتار مصرف کننده­ی الکترونیکی را نیز ارائه نموده است.}, keywords_fa= {مصرف کننده الکترونیکی,رفتار مصرف کننده الکترونیکی,خرده فروش الکترونیکی,خرید الکترونیکی}, url= {http://jomm.srbiau.ac.ir/article_1002.html}, eprint= {http://jomm.srbiau.ac.ir/article_1002_5455f319f043e40252e3b90b9d2fe6c7.pdf}} . Jounal of Marketing Management.

(2012). A three-stage assembly flow shop scheduling problem with blocking and sequence-dependent set up times . Journal of Industrial Engineering International.

Maleki-Darounkolaei, A., Modiri, M., Tavakkoli-Moghaddam, R., Seyyedi, I.(2012). A three-stage assembly flow shop scheduling problem with blocking and sequence-dependent set up times . Journal of Industrial Engineering International. 8. (1).

Seyedi, Iman and Maleki-Daronkolaei, A and Kalashi, Fariborz(2012). Tabu search and simulated annealing for new three-stage assembly flow shop scheduling with blocking.

N. Javadian and I. Seyyedi and J. Rezaeian(2009). A Genetic Algorithm approach for A Dynamic Cell Formation Problem . IFAC Proceedings Volumes. 42. (4). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 1055-1060.

آسیب شناسی هنر ایران در عرصه فعالیت های کارآفرینانه @article{noormags:1234009, title= {آسیب شناسی هنر ایران در عرصه فعالیت های کارآفرینانه }, journal= {پژوهش هنر }, number= {6 }, year= {1393 }, author= {توکلی,‌مرداویج and سیدی,‌سید‌ایمان and }, pages= {73 -- 82 }, url= {https://www.noormags.ir/view/en/articlepage/1234009 }} . پژوهش هنر.

CONFERENCE PAPER

Mozdgir, A., Mahdavi, I., Seyyedi, I., Shiraqei, M.E.(2011). Using Taguchi method to optimize differential evolution algorithm parameters to minimize workload smoothness index in SALBP . Aip Conference Proceedings. 1337. Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 258-264.

Javadian, N., Seyyedi, I., Rezaeian, J.(2009). A genetic algorithm approach for a dynamic cell formation problem . IFAC Proceedings Volumes IFAC Papersonline. 42. (4 PART 1). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 1055-1060.

BOOK

Mostafavi, Seyed Mojtaba and SA, Iman Seyedi and Hoseini, MohammadExcel for Engineers. 2009. Toranj Group Publication, Ltd.