Zum bundesweiten Digitaltag am 24. Juni 2022 richtete das Zentrum für Digitalen Wandel in Kooperation mit dem Hi-X-DigiHub und der COMPRA GmbH zum zweiten Mal ein digitales Symposium zum Thema der Low-Code-Softwareentwicklung aus. Mit der Low-Code-Technologie ist die Hoffnung verbunden, mehr Nicht-Informatiker_innen in die Software-Entwicklung einzubinden. Die Technologie ist für verschiedene Problemfelder von besonderer Bedeutung. Es besteht die Möglichkeit, Expert_innen aus Politik, BWL, Recht und anderen Bereichen direkt in die Software-Entwicklung einzubinden, so dass es zu weniger Reibungsverlusten bei der entwickelten Software kommt, da die Fachexpertise direkt in die Entwicklung eingebracht werden kann. Dem bestehenden Fachkräftemangel in der Softwareentwicklung kann insoweit begegnet werden, als dass die Mitwirkung von Expert_innen in der Entwick-lung dazu beitragen kann, dass Systeme entstehen können, bei denen weniger Code und damit weniger Programmier-Expertise erforderlich ist. Insgesamt verspricht dieses Verfahren Effizienz und Innovation in der Softwareent-wicklung.

Background: The Multi Depot Pickup and Delivery Problem with Time Windows and Heterogeneous Vehicle Fleets (MDPDPTWHV) is a strongly practically oriented routing problem with many real-world constraints. Due to its complexity, solution approaches with sufficiently good quality ideally contain several operators with certain probabilities.Thus, automatically selecting the best parameter configurations enhances the overall solution quality. Methods: To solve the MDPDPTWHV, we present a Grouping Genetic Algorithm (GGA) framework with several operators and population management variants. A Bayesian Optimization (BO) approach is introduced to optimize the GGA’s parameter configuration. The parameter tuning is evaluated on five data sets which differ in several structural characteristics and contain 1200 problem instances. The outcomes of the parameter-tuned GGA are compared to both the initial GGA parameter configuration and a state-of-the-art Adaptive Large Neighborhood Search (ALNS). Results: The presented GGA framework achieves a better solution quality than the ALNS, even for the initial parameter configuration used. The mean value of the relative error is less than 0.9% and its standard deviation is less than 1.31% for every problem class. For the ALNS, these values are up to three times higher and the GGA is up to 38% faster than the ALNS. Conclusions: It is shown that the BO, as a parameter tuning approach, is a good choice in improving the performance of the considered meta-heuristic over all instances in each data set. In addition, the best parameter configuration per problem class with the same characteristics is able to improve both the frequency of finding the best solution, as well as the relative error to this solution, significantly.

Mobility-on-demand services continue to grow in popularity and could provide a cheap and resource-saving alternative to private vehicles. However, to be truly attractive to the general public, these services must be thoroughly optimized. In this paper, we consider a ride-hailing problem where available vehicles have to be assigned to dynamically arising customer requests and, furthermore, vacant vehicles have to be repositioned to other parts of the service area to balance supply and demand. We propose a novel repositioning strategy based on dynamically created, overlapping zones that addresses identified weaknesses of previous repositioning approaches. While most other ride-hailing studies only consider one specific setting for which a suitable ridehailing strategy is developed, we further analyze which design decisions in the context of assignment and repositioning work best under different given problem characteristics. Our results show that the proposed repositioning approach outperforms the benchmark approaches in most of the relevant settings, independent of the underlying objective function. Additionally, we show that, especially for low-utilized fleets, the simple nearest-vehicle assignment strategy outperforms matching-based assignment approaches in many settings. The insights gained are analyzed and thoroughly discussed.

Solving a pickup and delivery problem with, e. g., multiple depots, time windows, and heterogeneous vehicles is a challenging routing task. Due to the complexity, a meta-heuristic approach (e. g., a genetic algorithm) with sufficiently good solution quality is recommended. Genetic algorithms contain multiple operators such as the crossover and mutation operators that are called with certain probabilities. However, selecting appropriate probability values (parameters) for these operators strongly depend on the data structure of the given instances. For each new instance, the best parameter configuration must be found to enhance the overall solution quality. In this paper, an a-priori parameter selection approach based on classifying new instances to clusters is presented. Beforehand, a bayesian optimization approach with gaussian processes is used to find the best parameters for each cluster. The a-priori parameter selection is evaluated on four well-known pickup and delivery problem data sets, each with 60 instances and different number of depots.

Taxonomies constitute one fundamental type of artefact in design science, describing and classifying existing or future objects of a domain. Taxonomies support researchers and practitioners with analysing and understanding a domain, which in turn is a prerequisite for theory building. Despite the increasing interest in taxonomies (and methodological guidance for building them), there is hardly any guidance for researchers on how to rigorously evaluate taxonomies. Based on a literature analysis, this study sheds light on the question of whether, when, and how researchers currently evaluate taxonomies. We critically synthesize and comprehensively review 306 articles that are concerned with taxonomies. Surprisingly, we find that taxonomies are rarely evaluated in IS research, nor is there any consistency in terms of methods used for evaluations. We describe the methods used by IS researchers to evaluate taxonomies after taxonomy building has been completed. Being the first to systematically analyse taxonomy evaluation, we propose a preliminary version of a framework for taxonomy evaluation which enables researchers to choose among the wide range of taxonomy evaluation methods available. Our study advances an informed and purposeful evaluation of taxonomies and contributes to bridging the gap between abstract design science evaluation strategies and concrete taxonomy evaluation methods.

Due to the increasing price pressure in the less-than-truckload (LTL) market, horizontal cooperation is an effective and efficient way for small- and medium-sized LTL carriers to enhance their profits by exchanging requests. For this purpose, a decentralized auction-based collaboration framework has proven to provide a good approach. In this paper, such a collaboration framework is adopted and extended by applying it to a practical-oriented routing problem, the Pickup and Delivery Problem with Time Windows and Heterogeneous Vehicle Fleets. Particularly, we analyse the bundle selection process made by the auctioneers, which is a stochastic problem and specify which requests are supposed to be offered together in a bundle to the cooperating carriers. For the purpose of solving the selection problem properly, we implement a new procedure required due to the characteristics of the considered routing problem: a scenario-based bundle selection approach. In order to make this approach applicable, two pre-selection techniques (cluster- and neural network-based) are developed. Our approach is evaluated on 240 collaboration network instances created from well-known pickup and delivery research data sets generated by Li and Lim (2001). The collaboration framework results are compared with respect to the profit to individual transportation planning of each carrier (lower threshold) as well as centralized transportation planning of all carriers (upper threshold). It can be shown that the auction-based collaboration approach is up to 43.49% better than the individual planning as well as exhausts at least 53.5% of the centralized transportation planning potential on average.

In face of the complexities of business models as well as their dynamic and uncertain environments, business model designers increasingly rely on a systemic view, for example by applying System Dynamics. Despite acceptance in research and practice, this approach comes however with several drawbacks such as high complexity of model construction and the models themselves. To overcome these challenges, we examine the potential of integrating reference modeling and System Dynamics. In this study, we describe the expected benefits and requirements of reference modeling for business models, give a preliminary overview of suitable reference model components, and outline promising directions of our ongoing and future research.

Due to the growing amount of cooperative business scenarios, collaborative Business Process Management (cBPM) has emerged. The increased number of stakeholders with minor expertise in process modeling leads to a high relevance of model understandability in cBPM contexts. Despite extensive works in the research fields of cBPM and model understandability in BPM, there is no analysis and comprehensive overview of methods supporting process model understandability in cBPM scenarios. To address this research gap, this paper presents the results of a literature review. The paper identifies concepts for supporting model understandability in BPM, provides an overview of methods implementing these concepts, and discusses the methods’ applicability in cBPM. The four concepts process model transformation, process model visualization, process model description, and modeling support are introduced. Subsequently, 69 methods are classified and discussed in the context of cBPM. Results contribute to revealing existing academic voids and can guide practitioners in cBPM scenarios.