QBWL Workshop

Registration for 31st QBWL Workshop

von QBWL Team am 02.11.2021, 15:57

The 31st QBWL workshop will take place in Kloster Drübeck from 28.3. to 31.3.2022. Registration for the workshop is now open. To register and/or submit an abstract please see the workshop section. The workshop is planned as an offline event under the current hygiene regulations. At the moment that is 2G, i.e. participants must be vaccinated or recovered. We will be the only guests in Kloster Drübeck.

 

Please be aware that the number of participants is limited to 106 (32 double bedrooms, 42 single bedrooms). For this reason, the total presentation time for each participating chair should not exceed one hour. As in previous years, we plan to have 10, 20, and 30-minute talks. We can already announce one program item today: Jochen Gönsch will give a tutorial on Approximate Dynamic Programming. The costs will be 300 Euro per person in a double room and 325 Euro per person in a single room.

 

 

 

 

 

30th QBWL Workshop took place online (2021)

von QBWL Team am 15.03.2021, 13:10

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.

Aus der Forschung

Crowdsourced Logistics

von QBWL Team am 10.06.2021, 13:26

Das Team vom Lehrstuhl für Supply Chain Management & Operations von Prof. Dr. Heinrich Kuhn hat einen Artikel in der Zeitschrift Networks platziert:

Crowdsourced logistics: The pickup and delivery problem with transshipments and occasional drivers

This article considers a setting in which a courier, express, and parcel service provider operates a fleet of vehicles with regular drivers (RDs) to ship parcels from pickup to delivery points. Additionally, the company uses a platform where occasional drivers (ODs) offer their willingness to take on requests that are on or near the route they had originally planned. There exist transshipment points (TPs) to better integrate these ODs. ODs or RDs may transfer load at these predetermined TPs. The problem is modeled as a mixed-integer programming model and called pickup and delivery problem with transshipments and occasional drivers (PDPTOD). We develop a solution approach based on an adaptive large neighborhood search. The article provides insights on how the number and location of TPs impact the cost advantages achieved by integrating ODs. It also shows that the cost savings are highly sensitive to the assumed flexibility and compensation scheme.

Zitation

Voigt, S, Kuhn, H.
Crowdsourced logistics: The pickup and delivery problem with transshipments and occasional drivers
Networks 2021; 1– 24. DOI: 10.1002/net.22045

https://doi.org/10.1002/net.22045

Online seit Mai 2021


Integer programming for container relocation

von QBWL Team am 10.06.2021, 13:16

Prof. Dr. Stefan Voß (Universität Hamburg) und Shunji Tanaka stellen in Ihrem neuen Artikel aus dem European Journal of Operational Research neue mathematische Planungsmodelle für das Container relocation problem vor:

An exact approach to the restricted block relocation problem based on a new integer programming formulation

This study addresses the block(s) relocation problem (BRP), also known as the container relocation problem. This problem considers individually retrieving blocks piled up in tiers according to a predetermined order. When the block to be retrieved next is not at the top, we have to relocate the blocks above it because we can access only the topmost blocks. The objective is to retrieve all the blocks with the smallest number of relocations. In this study, a novel exact algorithm is proposed for the restricted BRP, a class of the problem where relocatable blocks are restricted. The proposed algorithm computes lower and upper bounds iteratively by solving the corresponding integer programming problems until the optimality gap is reduced to zero. The novelty of the algorithm lies in the formulations based on complete and truncated relocation sequences of the individual blocks. We examine the effectiveness of the proposed algorithm through computational experiments for benchmark instances from the literature. In particular, we report that, for the first time, all the instances with up to 100 blocks are solved to proven optimality.

Zitation

Shunji Tanaka, Stefan Voß,
An exact approach to the restricted block relocation problem based on a new integer programming formulation,
European Journal of Operational Research,
2021, ISSN 0377-2217, DOI: 10.1016/j.ejor.2021.03.062

In Press / Online im Mai 2021

https://doi.org/10.1016/j.ejor.2021.03.062


Maximization of Open Hospital Capacity

von QBWL Team am 07.06.2021, 09:52

Prof. Dr. Rainer Kolisch (TU München) ist Co-Autor einer Publikation in der Zeitschrift Vaccines aus dem Mai 2021:

Maximization of Open Hospital Capacity under Shortage of SARS-CoV-2 Vaccines—An Open Access, Stochastic Simulation Tool

The Covid-19 pandemic has led to the novel situation that hospitals must prioritize staff for a vaccine rollout while there is acute shortage of the vaccine. In spite of the availability of guidelines from state agencies, there is partial confusion about what an optimal rollout plan is. This study investigates effects in a hospital model under different rollout schemes. Methods. A simulation model is implemented in VBA, and is studied for parameter variation in a predefined hospital setting. The implemented code is available as open access supplement.
A rollout scheme assigning vaccine doses to staff primarily by staff’s pathogen exposure maximizes the predicted open hospital capacity when compared to a rollout based on a purely hierarchical prioritization. The effect increases under resource scarcity and greater disease activity. Nursing staff benefits most from an exposure focused rollout.
The model employs SARS-CoV-2 parameters; nonetheless, effects observable in the model are transferable to other infectious diseases. Necessary future prioritization plans need to consider pathogen characteristics and social factors.

Zitation

Bosbach, Wolfram A.; Heinrich, Martin; Kolisch, Rainer; Heiss, Christian. 2021.
Maximization of Open Hospital Capacity under Shortage of SARS-CoV-2 Vaccines—An Open Access, Stochastic Simulation Tool,
Vaccines 9, no. 6: 546, DOI: 10.3390/vaccines9060546

https://doi.org/10.3390/vaccines9060546


Learning in Greedy Randomized Adaptive Search

von QBWL Team am 07.06.2021, 09:38

Im Journal Applied Soft Computing steht ein Artikel unter Mitwirkung von Prof. Dr. Stefan Voß (Universität Hamburg) unmittelbar vor der Publikation:

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.

Zitation

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

https://doi.org/10.1016/j.asoc.2021.107521


Trading decisions of wind power plants

von QBWL Team am 04.06.2021, 09:18

Benedikt Finnah und Prof. Dr. Jochen Gönsch (Universität Duisburg-Essen) entwickeln Künstliche Intelligenz (KI) für mehr erneuerbare Energie. Der neue Artikel erscheint im August 2021 im Journal of Production Economics.

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.

Zitation

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

https://doi.org/10.1016/j.ijpe.2021.108155