The Hamburg Logistics Professorship (HHLOP) is starting 2023 with new leadership, drive and new ideas. Prof. Dr Hanno Friedrich from Kühne Logistics University and Prof. Dr Dominik Kress from Helmut Schmidt University have jointly taken over the baton of leadership of the Hamburg Logistics Professorship from Prof. Dr Thomas Strothotte.

Next meeting: Discussion on the topic of Energy Port with the HPA (Hamburg Port Authority) on Wednesday, the 26th of April 2023. To this end, the HPA will first explain to us the current activities and plans in the port during a harbour tour, which we will then discuss at the HPA.

We have moved within the Campus Nord building. You can now find us on floor 2!

Our new paper in the European Journal of Operational Research is now online.

Abstract: Constraint programming solvers are known to perform remarkably well for most scheduling problems. However, when comparing the performance of different available solvers, there is usually no clear winner over all relevant problem instances. This gives rise to the question of how to select a promising solver when knowing the concrete instance to be solved. In this article, we aim to provide first insights into this question for the flexible job shop scheduling problem. We investigate relative performance differences among five constraint programming solvers on problem instances taken from the literature as well as randomly generated problem instances. These solvers include commercial and non-commercial software and represent the state-of-the-art as identified in the relevant literature. We find that two solvers, the IBM ILOG CPLEX CP Optimizer and Google’s OR-Tools, outperform alternative solvers. These two solvers show complementary strengths regarding their ability to determine provably optimal solutions within practically reasonable time limits and their ability to quickly determine high quality feasible solutions across different test instances. Hence, we leverage the resulting performance complementarity by proposing algorithm selection approaches that predict the best solver for a given problem instance based on instance features or parameters. The approaches are based on two machine learning techniques, decision trees and deep neural networks, in various variants. In a computational study, we analyze the performance of the resulting algorithm selection models and show that our approaches outperform the use of a single solver and should thus be considered as a relevant tool by decision makers in practice.

Available: Müller, D., M. G. Müller, D. Kress and E. Pesch (2022). An Algorithm Selection Approach for the Flexible Job Shop Scheduling Problem: Choosing Constraint Programming Solvers through Machine Learning. European Journal of Operational Research (accepted for publication). DOI: https://doi.org/10.1016/j.ejor.2022.01.034.

Our new paper in Computers & Operations Research is now online.

Abstract: We consider a semiconductor final-test scheduling problem that aims at minimizing the total weighted tardiness. In contrast to previous studies on this problem, we explicitly take account of the need to assign human operators to setup operations. We present decomposition-based heuristic solution approaches and a mixed integer program. In a computational study based on real-world problem instances that mimic settings at our industry partner, we show that our heuristics clearly outperform a standard solver when computational time is limited. Based on this result, we provide decision support for managers by analyzing the capability and effect of rescheduling jobs in the presence of a highly dynamic environment with frequently changing customer requests and common test machine failures.

Available: Kress, D. and D. Müller (2022). Semiconductor Final-Test Scheduling under Setup Operator Constraints. Computers & Operations Research. DOI: https://doi.org/10.1016/j.cor.2021.105619.

Our new paper in the International Journal of Production Research is now online.

Abstract: This paper addresses a flexible job shop scheduling problem that takes account of workforce constraints and aims to minimise the makespan. The former constraints ensure that eligible workers that operate the machines and may be heterogeneously qualified, are assigned to the machines during the processing of operations. We develop different variants of filter-and-fan (F&F) based heuristic solution approaches that combine a local search procedure with a tree search procedure. The former procedure is used to obtain local optima, while the latter procedure generates compound transitions in order to explore larger neighbourhoods. In order to be able to adapt neighbourhood structures that have formerly shown to perform well when workforce restrictions are not considered, we decompose the problem into two components for decisions on machine allocation and sequencing and decisions on worker assignment, respectively. Based on this idea, we develop multiple definitions of neighbourhoods that are successively locked and unlocked during runtime of the F&F heuristics. In a computational study, we show that our solution approaches are competitive when compared with the use of a standard constraint programming solver and that they outperform state-of-the-art heuristic approaches on average.

Available: Müller, D. and D. Kress (2021). Filter-and-Fan Approaches for Scheduling Flexible Job Shops under Workforce Constraints. International Journal of Production Research. DOI: https://doi.org/10.1080/00207543.2021.1937745.

Our new paper in the European Journal of Operational Research is now online.

Abstract: This paper treats the Piggyback Transportation Problem: A large vehicle moves successive batches of small vehicles from a depot to a single launching point. Here, the small vehicles depart toward assigned customers, supply shipments, and return to the depot. Once the large vehicle has returned and another batch of small vehicles has been loaded at the depot, the process repeats until all customers are serviced. With autonomous driving on the verge of practical application, this general setting occurs whenever small autonomous delivery vehicles with limited operating range, e.g., unmanned aerial vehicles (drones) or delivery robots, need to be brought in the proximity of the customers by a larger vehicle, e.g., a truck. We aim at the most elementary decision problem in this context, which is inspired by Amazon’s novel last-mile concept, the flying warehouse. According to this concept, drones are launched from a flying warehouse and – after their return to an earthbound depot – are resupplied to the flying warehouse by an air shuttle. We formulate the Piggyback Transportation Problem, investigate its computational complexity, and derive suited solution procedures. From a theoretical perspective, we prove different important structural problem properties. From a practical point of view, we explore the impact of the two main cost drivers, the capacity of the large vehicle and the fleet size of small vehicles, on service quality.

Available: Wang, K., E. Pesch, D. Kress, I. Fridman and N. Boysen (2021). The Piggyback Transportation Problem: Transporting Drones Launched from a Flying Warehouse. European Journal of Operational Research. DOI: https://doi.org/10.1016/j.ejor.2021.03.064.

Download (via Share Link, expires July 17, 2021): https://authors.elsevier.com/a/1d8wG1LnJ6hfHY

After the pandemic is over, members of the university and guest that do not own a chip card for the “Campus Nord” will be able to access our offices via the main entrance of the “Rentenversicherung Nord”. Once you have entered the building, turn to your left and enter the university area. Use the lift to find our offices.

You have unrestricted access in the following time slots:

Mon., Tue.: 07:30-16:00
Wed.: 07:30-14:00
Thu.: 07:30-18:00
Fri.: 07:30-13:00

At all other times as well as while the pandemic is prevailing, we are happy to pick you up at the side entrance upon prior appointment.

Prof. Dr. Dominik Kress is the new chairholder of the chair for Business Administration, especially Procurement and Production.