Optimal resource rescheduling in classification yards considering flexible skill patterns

• Main Authors: Pollehn, T. (Author), Preis, H. (Author), Ruf, M. (Author)
• Format: Article
• Language: English
• Published: Elsevier B.V. 2023
• Subjects: Classification yard; Classification yards; Engines; Heuristic methods; Integer programming; Job resources; Job sequencing; Locomotives; Mixed integer optimization; Network node; Personnel; Quality of service; Quality-of-service; Railway optimization; Railway optimizations; Resource allocation; Resource scheduling; Resources allocation; Resource-scheduling; Time availability; Transport systems
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 22109706 (ISSN)
• DOI: 10.1016/j.jrtpm.2023.100390

Classification yards represent network nodes in the single-wagonload transport system. The processes are complex due to a high number of involved resources and restrictive dependencies. Decisions on job sequencing and resource allocation have a major impact on outbound delays and thus on the quality of service in the network. Due to permanent updates of arrival times and resource availabilities, a constant revision of decisions is necessary. In many cases, considering multiple qualifications of the personnel is crucial for efficient operations. This paper presents an approach for the rescheduling of processes and the assignment of resources in classification yards, which allows to determine best working schedules based on current data such that the cumulative outbound delay of all trains is minimized. Therefore, the paper presents a mixed integer program that includes all essential components (tracks, locomotives and personnel with individual skill patterns). For the real-time capable solution of the optimization problem, four different heuristic approaches based on priority rules are presented. The performance of these approaches is evaluated by a gap analysis with respect to the solutions found by CPLEX. For this purpose, real example data of an operation day of a large classification yard in Germany are used. © 2023 The Author(s)