Mohammad Kaviyani-Charati started his PhD program and research work at Kühne Logistics University in January 2022. In addition, Mohammad graduated in industrial engineering from Amirkabir University of Technology (AUT), Tehran, Iran. This university is one of the best and most prestigious universities in Iran. His main research interests are sustainable supply chain, optimization, circular economy, industry 4.0, operations research, supply chain analytics and network optimization. He has worked with different universities and research centers in Iran, namely AUT, Kharazmi University and Mazandaran University. In addition to that, Mohammad has worked with different reputable companies in Iran (i.e. Solico-Kalleh Group, Shimialayesh Shomal and Khazar Ario Cellulose) where he analyzed costs and process-related data and then provided them with solutions for improving their performance and reducing their costs. Furthermore, he has collaborated with some foreign universities in Canada, Australia, America and Mexico.
Mohammad has published different scientific papers in well-known journals and presented at conferences, namely the Journal of Manufacturing Systems, Scientometrics, and so forth. In addition, some of his scientific work would be under review in computer and industrial engineering, expert systems with applications etc.
PhD candidate in Logistics and Supply Chain Management, Kühne Logistics University, Hamburg, Germany
|2016||Degree in Industrial Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran|
2020 - 2022
Industrial Engineering Specialist, Kalleh Dairy Company (Solico-Group), Tehran, Iran
|2019 - 2022||Associate Researcher, Kharazmi University, Tehran, Iran|
|2016 - 2019||Research Assistant, Mazandaran University and Industry, Tehran, Iran|
|2015 - 2019||CEO & Founder, White Cheese Mill, Tehran, Iran|
Journal Articles (Peer-Reviewed)
(2021): Mapping the intellectual structure of the coronavirus field (2000-2020): a co-word analysis, Scientometrics, 126: 6625-6657.
Abstract: Over the two last decades, coronaviruses have affected human life in different ways, especially in terms of health and economy. Due to the profound effects of novel coronaviruses, growing tides of research are emerging in various research fields. This paper employs a co-word analysis approach to map the intellectual structure of the coronavirus literature for a better understanding of how coronavirus research and the disease itself have developed during the target timeframe. A strategic diagram has been drawn to depict the coronavirus domain’s structure and development. A detailed picture of coronavirus literature has been extracted from a huge number of papers to provide a quick overview of the coronavirus literature. The main themes of past coronavirus-related publications are (a) “Antibody-Virus Interactions,” (b) “Emerging Infectious Diseases,” (c) “Protein Structure-based Drug Design and Antiviral Drug Discovery,” (d) “Coronavirus Detection Methods,” (e) “Viral Pathogenesis and Immunity,” and (f) “Animal Coronaviruses.” The emerging infectious diseases are mostly related to fatal diseases (such as Middle East respiratory syndrome, severe acute respiratory syndrome, and COVID-19) and animal coronaviruses (including porcine, turkey, feline, canine, equine, and bovine coronaviruses and infectious bronchitis virus), which are capable of placing animal-dependent industries such as the swine and poultry industries under strong economic pressure. Although considerable research into coronavirus has been done, this unique field has not yet matured sufficiently. Therefore, “Antibody-virus Interactions,” “Emerging Infectious Diseases,” and “Coronavirus Detection Methods” hold interesting, promising research gaps to be both explored and filled in the future.
(2020): Designing a closed-loop supply chain network for citrus fruits crates considering environmental and economic issues, Journal of Manufacturing Systems, 55: 199-220.
Abstract: Most cities, notably major and agricultural ones, are faced with environmental and waste problems. Distribution and collection of agricultural crops can be challenging duties as world demand and production are substantially increased. Accordingly, resource depletion, environmental concern, and the importance of the circular economy have convinced this research group to focus on a Closed-Loop Supply Chain (CLSC) network design. In this study, a new mixed linear mathematical model for a CLSC was developed which minimizes the CLSC’s total costs and which tackles and controls air pollution. Contrary to previous works about supply chain network design, we firstly consider citrus fruits’ crates in our model. To solve the model, two leading algorithms, Genetic Algorithm and Simulated Annealing, are employed and a third recently successful method, Keshtel Algorithm, is utilized. Further, two hybridization algorithms stemmed from mentioned ones are applied. Finally, the results are assessed by different criteria and compared, and then the two best algorithms are chosen in this case. Consequently, in order to achieve the most effective result, a real case study of crates was conducted. The results obviously presented applicability and efficiency of the proposed model. Thus, the most suitable network for CLSC of citrus fruits’ crates was designed in which the costs and emissions were reduced.
(2018): A Robust Optimization Methodology for Multi-Objective Location-Transportation Problem in Disaster Response Phase under Uncertainty, International Journal of Engineering: .
Abstract: This paper presents a multi-objective model for location-transportation problem under uncertainty that has been developed to respond to crisis. In the proposed model, humanitarian aid distribution centers (HADC), the number and location of them, the amount of relief goods stored in distribution centers, the amount of relief goods sent to the disaster zone, the number of injured people transferred to medical centers and the delivery of relief regarding the limits of capacity for transport, distribution centers and also available time and budget limits are all considered. This work aims at minimizing unfulfilled needs; that is meaning the number of people have not been transferred to medical centers. In order to take the inevitable uncertainty in some parameters into account, the primal deterministic model has been reformulated by applying the robust optimization approach. Also the performance of the both deterministic and robust models are investigated by solving a numerical example. The results of the study show that the robust counterpart of deterministic model will remain feasible with a high probability in reality.
Journal Articles (Professional)
(2020): Impact of adopting quick response and agility on supply chain competition with strategic customer behavior, Scientia Iranica, 29 (1): .
Abstract: A growing trend towards computerization and competition in supply chains results in uncertainty and quick variability that make the decisions difficult for both levels of retailers and manufacturers. In this paper, two Bi-Level Stackelberg models are developed under non- and agile conditions in the presence of strategic customers. Our main novelty approach in this paper is to consider both levels competing with each other in a sequential game to determine the optimal production and order quantities and prices with and without agile abilities. In addition, both proposed models are simplified single-level using the Karush-Kuhn-Tucker (KKT) approach. Then, they are remodeled by the Robust Optimization technique due to existing uncertain parameters. To have a better assessment of the models’ efficiency and applicability, they have been implemented in a real case and finally, the results are compared and analyzed.
(2018): Agility in a Competitive Supply Chain with Considering Strategic Customers, Journal of Industrial Engineering Research in Production Systems (IERPS), 6 (12): 33-47.
Abstract: Growths of technology and innovation have made continual changes in fashion business and costumer tastes. In situations like that, retailers and manufacturers select agility and flexibility as their main supply chain strategies. In this study, a bi-level model including the retailer and manufacturer who traditionally compete on the product quantity and price is proposed; then another model is developed by adding some characteristics of agility to first model. In the proposed model, in addition to the competition between the supply chain members, influences of the customers’ behavior on the decisions of supply chain members are considered. This study is aimed at proposing efficient solutions for determining the price and quantity of ordering and production, considering the situations of competition, customers and market toward maximization of the manufacturers’ and retailers’ profit. The proposed bi-level model is converted to a single-level one using the Karush-Kuhn-Tucker (KKT) and the results of the model are investigated and discussed by employing in a numerical example. Results show that the retailer and manufacturer, by making proper and precise decisions, can increase their sale price. Further, by improving their decisions, they can reduce the product clearance sale at the end of the sales season, which ends in the growth of profit for both of the supply chain members.
: Demand Sensitivity to Time, in: Hemmati, Mahya and Mohsen S. Sajadieh (ed.): Influencing Customer Demand: An Operations Management Approach, Taylor & Francis: Boca Raton.
Abstract: Demand is the main factor of inventory models in supply chain management and is classified into two types, namely, dependent and independent. The demand for a product is heavily influenced by time after launching, wherein some deteriorating items have faced inevitable consequences. Therefore, various time-dependent demand functions, their advantages, and disadvantages are explored to help determine the application of each model. Also, the functions are categorized into four cases: (i) linear time-dependent demand, (ii) exponential time-dependent demand, (iii) quadratic time-dependent demand, and (iv) ramp-type time-dependent demand, which are also examined in terms of mathematical modeling in this chapter. The functions will help decision-makers meet consumer demand well and efficiently as the market demand has been affected with time. Furthermore, two real case studies along with their optimization models are provided to show how the demand rate changes over time. Finally, a comprehensive research trend and some directions for future research are outlined.