QBWL Workshop

Aus der Forschung

Flexibility in engineer-to-order production planning

von QBWL Team am 18.06.2021, 08:04

Ein neuer Artikel von Robert Brachmann und Prof. Dr. Rainer Kolisch von der TU München ist im International Journal of Production Economics erschienen:

The impact of flexibility on engineer-to-order production planning

This paper addresses the tactical production planning problem of engineer-to-order companies. For this, we extend the resource-constrained project scheduling problem with flexible resource profiles (FRCPSP) to the multi-project case as well as flexible resource availability. We model the problem as a deterministic discrete-time mixed-integer program using step-variables. For each activity the start time, the duration, and the allocation of a given resource requirement over time has to be determined, taking into account precedence constraints between activities and availability of resources. The capacity of resources is not fixed but can vary subject to working time accounts. Using a lexicographic ordering approach, the primary objective is to minimize the total weighted lateness of projects, while the secondary objective is to balance working time accounts. In our computational study, we solve real-world instances from a mid-size company with up to 100 projects and 1725 activities. We show that optimal solutions can be derived in reasonable computation time with off-the-shelf commercial solvers. From a managerial point of view, our results demonstrate the value of flexibility.


Robert Brachmann, Rainer Kolisch,
The impact of flexibility on engineer-to-order production planning,
International Journal of Production Economics,
Volume 239, 2021, 108183, ISSN 0925-5273, DOI: 10.1016/j.ijpe.2021.108183


Veröffentlich im Druck: September 2021

Metaheuristics “In the Large”

von QBWL Team am 14.06.2021, 13:27

Unter Beteiligung von Prof. Dr. Stefan Voß (Universität Hamburg) erscheint im European Journal of Operational Research eine Darstellung des sog. Metaheuristics in the large Projektes, welches das Ziel verfolgt, die wissenschaftliche Vergleichbarkeit von Forschung im Bereich der Metaheuristiken zu verbessern.

Metaheuristics “In the Large”

Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need for stronger scientific and computational infrastructure to support the development, analysis and comparison of new approaches. To this end, we present the vision and progress of the Metaheuristics In the Large project. The conceptual underpinnings of the project are: truly extensible algorithm templates that support reuse without modification, white box problem descriptions that provide generic support for the injection of domain specific knowledge, and remotely accessible frameworks, components and problems that will enhance reproducibility and accelerate the field’s progress. We argue that, via such principled choice of infrastructure support, the field can pursue a higher level of scientific enquiry. We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics.


Jerry Swan, Steven Adrænsen, Colin G. Johnson, Ahmed Kheiri, Faustyna Krawiec, J.J. Merelo, Leandro L. Minku, Ender Özcan, Gisele L. Pappa, Pablo García-Sánchez, Kenneth Sörensen, Stefan Voß, Markus Wagner, David R. White,
Metaheuristics “In the Large”,
European Journal of Operational Research,
2021,ISSN 0377-2217, DOI: 10.1016/j.ejor.2021.05.042


Online verfügbar: Juni 2021

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.


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


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.


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


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.


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