This year’s QBWL workshop was hosted on 8 March 2021 by the Chair of Supply and Value Chain Management at TUM Campus Straubing. Due to the COVID-19 pandemic, the workshop was hosted in a virtual format.
116 participants from 18 universities joined the workshop which consisted of a large number of presentations and discussions. In three streams on “Last Mile Delivery, Routing & Logistics”, “ Revenue Management & Supply Planning”, and “Sequencing & Scheduling”, 20 researchers presented their current or future research projects. The presentations were complemented by vivid discussions.The agenda can be found below.
QBWL is a gathering of German research groups working on quantitative topics in business and economics. It was established about 3 decades ago and has continuously grown to a group of more than 25 chairs. The workshop is hosted once a year by one of the participating chairs, usually as a 3-day in-person workshop.
Next year’s workshop is planned for March 28-31, 2022 in Kloster Drübeck, hosted by Prof. Sven Müller from the University of Magdeburg.
30th QBWL Workshop online (2021)
von QBWL Team am 29.10.2020, 14:35
The 30. QBWL Workshop takes place March 8. - 9. 2021. The format will be online.
For further information, see the "Workshop" section.
Fixed set search application for minimizing the makespan on unrelated parallel machines with sequence-dependent setup times
This paper addresses the problem of minimizing the makespan on unrelated parallel machines with sequence-dependent setup times. The term unrelated machines is used in the sense that there is no correlation between the processing times for jobs between different machines. Due to the NP-hardness of this problem, a wide range of metaheuristics has been developed to find near-optimal solutions. Out of such methods, the ones based on constructive greedy algorithms like the Greedy Randomized Adaptive Search Procedure (GRASP), Ant Colony Optimization (ACO) and Worm Optimization (WO) proved to be most efficient. The Fixed Set Search (FSS) is a novel population-based metaheuristic of this type that adds a learning mechanism to the GRASP. The basic concept of FSS is to avoid focusing on specific high quality solutions but on parts or elements that such solutions have. This is done through fixing a set of elements that exist in such solutions and dedicating computational effort to finding near-optimal solutions for the underlying subproblem. In this work, the FSS is applied to the problem of interest. Computational experiments show that the FSS manages to significantly outperform the GRASP, ACO and WO on the standard benchmark instances when the quality of found solutions is considered without an increase in computational cost. This application of the FSS is significant as it shows that it can also be applied to scheduling type problems, in addition to covering and routing ones.
Raka Jovanovic, Stefan Voß, Fixed set search application for minimizing the makespan on unrelated parallel machines with sequence-dependent setup times, Applied Soft Computing,2021,107521,ISSN 1568-4946, DOI: 10.1016/j.asoc.2021.107521
Optimizing trading decisions of wind power plants with hybrid energy storage systems using backwards approximate dynamic programming
On most modern energy markets, electricity is traded in advance and a power producer has to commit to deliver a certain amount of electricity some time before the actual delivery. This is especially difficult for power producers with renewable energy sources that are stochastic (like wind and solar). Thus, short-term electricity storages like batteries are used to increase flexibility. By contrast, long-term storages allow to exploit price fluctuations over time, but have a comparably bad efficiency over short periods of time. In this paper, we consider the decision problem of a power producer who sells electricity from wind turbines on the continuous intraday market and possesses two storage devices: a battery and a hydrogen based storage system. The problem is solved with a backwards approximate dynamic programming algorithm with optimal computing budget allocation. Numerical results show the algorithm's high solution quality. Furthermore, tests on real-world data demonstrate the value of using both storage types and investigate the effect of the storage parameters on profit.
Benedikt Finnah, Jochen Gönsch, Optimizing trading decisions of wind power plants with hybrid energy storage systems using backwards approximate dynamic programming, International Journal of Production Economics, Volume 238,2021,108155,ISSN 0925-5273, DOI: 10.1016/j.ijpe.2021.108155
A robust multiobjective model for the integrated berth and quay crane scheduling problem at seaside container terminals
The ever increasing demand for container transportation has led to the congestion of maritime container terminals in the world. In this work, the two interrelated problems of berth and quay crane scheduling are considered in an integrated multiobjective mathematical model. A special character of this model is that the arrival times of vessels and the failure (working) times of quay cranes are not deterministic and can vary based on some scenarios. Hence, a robust model is devised for the problem having three objectives of minimising the deviations from target berthing locations and times as well as departure delays of all vessels. This robust optimisation seeks to minimise the value of the objectives regarding all the scenarios. An exact solution approach based on the 𝜖-constraint method by the Gurobi software is applied. Moreover, regarding the complexity of the problem, two Simulated Annealing (SA) based metaheuristics, namely a Multi-Objective Simulated Annealing (MOSA) and a Pareto Simulated Annealing (PSA) approach are adapted with a novel solution encoding scheme. The three methods are compared based on some multiobjective metrics and a statistical test. The advantage of the integration of berth and quay crane scheduling is examined as well.
Nourmohammadzadeh, A., Voß, S. A robust multiobjective model for the integrated berth and quay crane scheduling problem at seaside container terminals. Ann Math Artif Intell (2021). DOI: 10.1007/s10472-021-09743-5
The Piggyback Transportation Problem: Transporting drones launched from a flying warehouse
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.
Kai Wang, Erwin Pesch, Dominik Kress, Ilia Fridman, Nils Boysen, The Piggyback Transportation Problem: Transporting drones launched from a flying warehouse, European Journal of Operational Research, 2021,ISSN 0377-2217, DOI: 10.1016/j.ejor.2021.03.064
Concurrent design of product and supply chain architectures for modularity and flexibility: process, methods, and application
Product design and supply chain design are two key determinants of company competitiveness. However, they follow different design objectives and thus require a systematic trade-off. Although methodologies for product design and supply chain design are well established within each domain in research and industry, an integrated methodology that bridges both design domains is still lacking. Based on a recently introduced concurrent product and supply chain design process, we contribute to this underdeveloped research area with a generic approach towards exploring design tradespace. We introduce a detailed operational process for the concurrent design of product and supply chain architectures. To apply this generic process to the specific trade-off between the product-related objective of modularity and the supply-chain-related objective of sourcing flexibility, we also develop new methods for key steps of the process. We demonstrate the application of the process and the developed methods using an industrial case study of a new product (electric-vehicle battery module). The case shows that our methodology was able to structure the concurrent design process. It hereby ensured an efficient trade-off and led to high-quality designs.
Thiam-Soon Gan, Moritz Steffan, Martin Grunow & Renzo Akkerman (2021) Concurrent design of product and supply chain architectures for modularity and flexibility: process, methods, and application, International Journal of Production Research, DOI: 10.1080/00207543.2021.1886370