Ghazal Ziadlou begann im Oktober 2025 ihr Studium der PhD an der KLU unter der Betreuung von Prof. Dr. Arne Heinold. Ihr Forschungsschwerpunkt liegt auf Operations Research für die Logistik im Bereich der Kohlendioxidentfernung (CDR). Sie erwarb einen Master of Science in Wirtschaftsingenieurwesen mit dem Schwerpunkt Systemoptimierung, nachdem sie zuvor einen Bachelor of Science in Wirtschaftsingenieurwesen an der Babol Noshirvani University of Technology abgeschlossen hatte; dabei stach sie in ihrem Jahrgang hervor, indem sie unter den Studienanfängern ihres Jahrgangs den ersten Platz im Masterstudiengang belegte.
In ihrer Masterarbeit schlug sie ein leistungsfähiges Terminplanungsmodell für ein Netzwerk aus miteinander interagierenden Fabriken vor, das Transportkosten berücksichtigt; daraus entstanden sowohl ein Konferenzbeitrag als auch ein Fachartikel, der in der Zeitschrift „Computers & Industrial Engineering“ veröffentlicht wurde. Während ihres gesamten Studiums war sie als Lehrassistent:in für verschiedene Kurse tätig, darunter Simulation, Bestandssteuerung und -planung, datengestützte Modellierung sowie multivariate statistische Analyse. Nach ihrem Abschluss arbeitete sie als wissenschaftliche Mitarbeiterin an Machbarkeitsstudien für reale Fälle in einer der iranischen Provinzen, um dazu beizutragen, die Möglichkeiten für Start-ups und neue Unternehmen zu erweitern und zu verbessern. Ihre allgemeinen Forschungsinteressen umfassen Optimierung, Operations Research, Entscheidungsfindung, Simulation, Produktionsplanung und -terminierung, Datenwissenschaft und maschinelles Lernen.
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Networks
Academic Position
| Since 2025 | PhD candidate at Kühne Logistics University, Hamburg, Germany |
Professional Experience
| 2023 - 2024 | Research Assistant, Babol Noshirvani University of Technology, Babol, Iran |
| 2021 - 2023 | Teaching Assistant, Babol Noshirvani University of Technology, Babol, Iran |
| 2020 | Student Internship, Mazinoor Lighting Inc, Babol, Iran |
Education
| 2020 - 2022 | Master of Science in Industrial Engineering (Systems Optimization), Babol Noshirvani University of Technology, Babol, Iran |
| 2016 - 2020 | Bachelor of Science in Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran |
Publikationen
Nowadays, many massive factories are forced to distribute their products in several manufacturing units. This issue has caused the emergence of a novel category of problems called distributed production scheduling, which is vital in today's growing world. In this paper, the distributed production scheduling problem by considering network configuration with two echelons is addressed. The first and second echelon factories have different job configurations and have a hybrid flow shop and a flexible job shop environment, respectively. For this problem, A bi-objective mixed integer linear programming (MILP) model is presented to minimize the maximum completion time of jobs and transportation costs between the selected factories in two echelons, respectively. Consequently, the epsilon constraint method is used to deal with this bi-objective model. In addition, since distributed scheduling problems are classified as NP-Hard problems, it is very challenging to solve them for large-sized instances. For this reason, a constraint programming model (CP) is also proposed. To evaluate the performance of the proposed MILP model and CP model, a total of 180 numerical instances are randomly generated in small, medium, and large sizes. The obtained results demonstrate the significant ability of the constraint programming approach in solving complex distributed scheduling problems even for large-sized instances with 30 jobs, 10 stages/operations for each job, 6 machines for each stage/operation, and 4 factories at each echelon in a reasonable time and proof that the CP model can outperform the MILP model in this problem.
Today, with the increasing advancement of technology, in many large factories, the production of products has changed from a single-factory to a multi-factory environment. Rising customer demand and the complexity of their expectations, the ability to compete with other competitors in delivering products, as well as lower production costs in such manufacturing environments, are among the factors that have forced many factories to distribute their manufacturing in several production units in recent years. Therefore, production planning and scheduling in such conditions are very important. In fact, in distributed production environments, each factory can be considered as a separate entity that includes its characteristics, limitations, and performance, and this factor has made the distributed production scheduling problems more complex than the production scheduling problems for just one factory. Therefore, in this study, a mixed-integer linear programming model is presented for the distributedproduction scheduling problem considering network configuration in two echelons, in which the first and secondechelon factories have a hybrid flow shop and flexible job shop environments, respectively. The proposed biobjectivemathematical model aims to minimize the maximum completion time of jobs and transportation costsbetween echelons. The proposed model is solved for a small-size instance by GAMS software and the results arereported
