BiGSEM Colloquium (Management)

Wednesday, June 26 at 2pm in U3-140

Speaker: Robin Weisner (supervised by Sabrina Backs)
Title: A Closer Look at Entrepreneurial Ecosystems: An Agent-Based Simulation Approach [45 minutes]

Abstract: Entrepreneurial ecosystems are a well-established phenomenon to explain a high concentration of entrepreneurial activity in a certain geographical area. They consists of six key elements that interact with and influence each other. Universities play a key role in this context, as they have numerous opportunities, especially in the course of the third mission, such as initiating cooperation and creating spin-offs, in order to stimulate the entrepreneurial ecosystem. We would like to use agent-based simulation to take a closer look at what these are in detail and how they can affect the entrepreneurial ecosystem by modeling such an entire ecosystem, representing the individual actors with their decisions and on both the micro and macro levels to investigate interaction. The aim of this research project is to develop recommendations for action for the actors, if necessary also the region and the state, which allow to establish "what-if" relationships that can be applied to different entrepreneurial ecosystems.

Speaker: Corinna Hesse (supervised by Peter Limbach)
Title: Capital Allocation -  Empirical Evidence and Literature Review [30 minutes]

Abstract: Companies frequently decide on the use of their capital – a process called capital allocation. This process covers two primary categories: investments, such as spending cash on capital expenditures and research and development, and financing, such as corporate payouts and debt repayments. While extant literature focuses on the individual components of the capital allocation process, this research agenda is concerned with the general strategic allocation of firms’ capital, incorporating all its elements simultaneously. Data on capital allocation strategies and objectives is not available in standardized format. However, U.S. corporations increasingly report about capital allocation in their earnings calls and, to a lower extent, in their annual reports (10-Ks). As a starting point, we conduct a manual analysis of earnings call transcripts and 10-Ks to identify statements capital allocation, translating them into statistically exploitable variables. To provide large-sample evidence, we employ a self-learning algorithm that annotates sentences within the aforementioned documents. The first paper of our research agenda aims to understand several questions that are at the heart of corporate finance and governance: (1) Which firms voluntarily disclose information about capital allocation and what information is reported? (2) How does capital allocation disclosure and strategy relate to corporate governance and CEO characteristics? (3) How does capital allocation vary in the cross-section of firms as well as over time? (4) Do firms with a capital allocation strategy exhibit superior accounting and stock-market performance?

Speaker: Frederik Tolkmitt (supervised by myself)
Title: Innovation diffusion in the presence of opposing information sources: An agent-based simulation approach [45 minutes]

Abstract:  The market introduction of radically new products and their respective innovation diffusion can considerably be affected by consumers’ increased uncertainty due to diverging information received from opposing sources. Such information can originate, for example, from strong general attitudes being prevalent in certain consumer groups (e.g., regarding the pros and cons of integrating AI capabilities in products) or it may even be spread by a competitor employing a negative-word-of-mouth campaign (e.g., when questioning the eco friendliness of electric vehicles). In such a setting, consumers receive ambiguous signals, which should increase their uncertainty and potentially delay product adoption. However, commonly used belief updating models (such as Bayesian learning), in a counterfactual manner, assume a decrease of uncertainty whenever new information is received even if this information substantially deviates from previous information. In our research, we demonstrate this effect by means of a computational simulation experiment based on a straightforward agent-based model of innovation diffusion and we propose a novel approach that more realistically captures consumer uncertainty with respect to insufficient or ambiguous information.

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Wednesday, May 22, 2-3pm, in U3-140

Speaker: David Winkelmann

Title: Subscription-Based Inventory Planning for E-Grocery Retailing

Abstract:
The growing e-grocery sector faces challenges in becoming profitable due to heightened customer expectations and logistical complexities. This project addresses the impact of uncertainty in customer demand on inventory planning for online grocery retailers. Given the perishable nature of grocery products and intense market competition, retailers must ensure product availability while minimizing overstocking costs. We propose introducing subscription offers as a solution to mitigate these inventory challenges. Unlike existing literature focusing on uniform subscription models that may harm profitability, our approach considers the synergy between implementing product subscriptions and cost savings from improved inventory planning. We present a three-step procedure enabling retailers to understand uncertainty costs, quantify the value of gathering additional planning information, and implement profitability-enhancing subscription offers. This holistic approach ensures the development of sustainable subscription models in the e-grocery domain.

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24th of April 2024

Speaker: Mohsen Nafar
Title: "A New Dynamic Variable Ordering for Compiling Decision Diagrams for the Maximum Independent Set Problem"

Spearker: Elias Schede
Title: "Anytime Algorithm Configuration"

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08.11.2023

Jakob Schulte

"Innovative Matheuristic for Large-Scale Optimization: A Case Study from ROADEF 2022"
Abstract:
The ROADEF challenge 2022 involves RENAULT's supply chains, which span over 40 plants in 17 countries and involve 1500 suppliers. Every week, 6000 trucks deliver parts from suppliers to plants. The filling rate of these trucks is critical since the inbound transportation annual budget is several hundred million euros. The objective is to pack a set of items from suppliers into stacks and load the stacks onto trucks for delivery to the plants, with the goal of minimizing (a) the number of trucks used and (b) the inventory in the plants due to early deliveries. Items have a delivery time window ranging from 1 to 5 days. It is possible to deliver the items early (e.g., at their earliest arrival time) to better fill the trucks, but early deliveries generate inventory costs for the plants. Inventory costs are highly sensitive at Renault, as they represent several hundred million euros. Therefore, the best solution is to deliver items to the plants at the latest possible time while minimizing the number of trucks used. In terms of data volume, a large instance can contain up to 260,000 items and 5,000 planned trucks, spanning a horizon of 7 weeks.

In this paper, we introduce an innovative matheuristic designed for optimizing large-scale assignment and container loading problems. Our algorithm effectively showcases its capabilities by yielding new globally optimal solutions, which have the potential to generate significant cost savings for RENAULT's supply chain operations, potentially amounting to millions of euros. By tackling the intricate optimization challenges posed by the ROADEF Challenge 2022, our approach offers a promising avenue for elevating supply chain management, improving cost efficiency, and enhancing overall operational performance within a vast industrial context.

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17.01.2024

Peter Limbach and Christian Stummer

"Two editors' experiences on the dos and don’ts in academic publishing"

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07.02.2024 - in U3-140 - it will starts at 3pm (instead of 2pm)

Henning Witteborg

"Using Stochastic Programming for Surgery Scheduling under Uncertainty”

Abstract:

The scheduling of elective surgeries highly depends on the available capacities of beds in the postoperative intensive care ward. While these capacities, with their high demands on equipment and staff, are limited, the development of the occupancy of the intensive care units is subject to various factors of uncertainty, such as the individual length-of-stay of a patient or emergency arrivals. Despite this fluctuating demand, we aim to optimize the utilization of capacities in the intensive care unit after a surgery.

For this purpose, we present a model that proposes a scheduling of elective patients for their stay in the ward while considering the mentioned factors of uncertainty. We model this problem as a stochastic programming approach with a planning horizon of one week, in which we approximate the uncertain and decision-dependent evolution of the ICU occupation in scenarios generated by methods from Machine Learning and simulation techniques. The model aims to schedule as many patients as possible one week in advance while respecting the risk of exceeding critical occupancy levels as chance constraints and therefore avoiding outcomes that would be considered unacceptable in practice.

In cooperation with a hospital in Bielefeld, the model's architecture is based on real-world implications, using historic data from patients who were admitted to the intensive care unit. To demonstrate the applicability of the model, we created instances that represent real-world planning problems and typical patient characteristics. Our results show the potential for significant improvements in surgery scheduling by optimizing the utilization of the necessary capacities in postoperative care. Additionally, the conceptualization as a stochastic programming model with chance constraints allows the implementation of risk-oriented policies for the scheduling and admission of patients.

 

 

24.05.2023 (2-4 pm): BiGSEM Colloquium, U3-140

Speaker: Elias Schede will talk about "Selector: An ensemble for automated algorithm configuration"

Abstract: Solvers contain parameters that influence their performance and these must be set by the user to ensure high-quality solutions are generated, or optimal solutions are found quickly. Manually setting these parameters is tedious and error-prone, since search spaces may be large or even infinite. Existing approaches to automate the task of algorithm configuration (AC) make use of a single machine learning model that is trained on previous runtime data and used to create promising new configurations. We combine a variety of successful models from different configurators to an ensemble that proposes new configurations. To this end, each model in the ensemble suggests configurations and a hyper-configurable selection algorithm down-selects them to match the number of configurations to try to the amount of computational resources available. Using multiple models leads to a more diverse search, since each model has a unique belief about good regions of the search space, from which it can propose configurations.

 

Speaker: Frederik Tolkmitt will talk about "The role of uncertainty in innovation diffusion of radically new products: An agent-based simulation study"

Abstract: When consumers are uncertain whether they have sufficient (unambiguous) information regarding a new product, they might delay the adoption decision to a later point in time. This uncertainty effect can have a major impact on the market diffusion of an innovation. Most prior models that account for consumers’ belief updating in such a setting capture this effect by resorting to some form of Bayesian learning and, usually, assume that the distribution of all possible information with respect to the attributes of the new product is normal and that the pieces of information received by individual consumers are independent draws from this distribution. Consequently, consumers’ uncertainty regarding their beliefs decreases with each additional piece of information (e.g., after talking with a peer or being exposed to advertisements). This strong assumption is convenient as it makes models of opinion dynamics analytically tractable, and it works in many instances. However, when two diverging opinions are prevalent among consumers in a certain market and, thus, receiving additional information potentially increases uncertainty of individual consumers, a different approach is required. Radically new products, for which consumers cannot lean on previous experiences, constitute a prime example for such markets on which the distribution of beliefs can be bimodal. We propose a suitable approach that can deal with the latter setting and we demonstrate the value added of this novel approach (in contrast to the traditional approach) through computational simulation experiments based on an agent-based market model of innovation diffusion.

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21.06.2023 (2-4 pm): BiGSEM Colloquium, U3-140

Presentation of the GOR Bachelor Thesis Award and a short overview of the winning work by Paulina Heine [Chair: Kevin Tierney]

Speaker: Corinna Hesse [Chair: Peter Limbach]
Title "Capital allocation" - Abstract

Speaker: Luisa Liedtke [Chair: Kai Bormann]
Title "Uncertainty in the context of corporate and daily purpose: A multilevel investigation" - Abstract

Speaker: Annika Schaefer [Chair: Kai Bormann]
Title "Crafting meaning out of contrasts: How illegitimate work tasks ignite job crafting and contribute to meaningful work" - Abstract

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12.07.2023 (2-4 pm): BiGSEM Colloquium, U3-140

Speaker: Tba
Title:Tba

19th of October 2022

Speaker: Henning Witteborg
Title: Co-Design-Study of a Mobile Application for Cochlear Implantation Rehabilitation
Abstract:
Patients who undergo a cochlear Implantation face the need of an intensive aftercare to support their speech and hearing therapy. In order to support this process of training and improving the use of the implant, our group of researchers and practitioners are about to test an algorithm-based application that is supposed to offer patient-specific exercise program on a mobile device. For a first test with a small group of patients, we would like to add co-design-aspects to the development process. To make this process successful, we would like to discuss questions like the following:
•       How close can patients can be included in the development process?
•       Are joint workshops with all test patients better than individual sessions?
•       How many iterations of feedbacks and adaptions are reasonable?
•       What are the requirements that patients can really feel like stakeholders in the process?

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30th of November 2022

Speaker: Tba
Title: Tba

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11th of Januray 2023

Speaker: Tba
Title: Tba

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15th of February 2023

Speaker: Tba
Title: Tba

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