Turning hustlers into entrepreneurs, and social needs into market demands: Corporate-community encounters in Nairobi, Kenya

This article contributes an empirically rich account of a social enterprise project embedded in local urban economies of Nairobi, Kenya. The confluence of rapid, unplanned urbanisation and economic liberalisation has led to growing formations of informal settlements and a vibrant informal sector across post-colonial cities. These “slum“ neighbourhoods, housing the majority of the urban population on a fraction of the city's land, are often ignored and marginalized by the state and municipal authorities, particularly with regards to basic service provision. At the same time, slum economies provide entry-points for various enterprise-led development schemes seeking to commercially engage both entrepreneurial individuals and their existing customer base in order to scale access to unmet needs. The discussion is based on an ethnographic study in one of Nairobi's largest informal settlements, which focused on the everyday practices of a local micro-franchise called “Community Cleaning Services“. The article illustrates how waste workers and self-proclaimed “hustlers“ were turned into micro-franchisee entrepreneurs providing a sanitation service to residential customers, through their engagement with Community Cleaning Services. This ethnographic account raises two potentially contradictory but inter-related debates that are rarely considered alongside one another in the existing literature on corporate involvement in low-income markets. First, it reframes the critiques of enterprise-led initiatives to “poverty alleviation“ by focusing on the implications of commercialising “basic“ services and on the logistical and cultural challenges of turning social needs into market demands. Second, it emphasises the often-invisible role of grassroots informal economies in enabling access to vital services in the absence of an adequately resourced and responsive municipality. The article concludes with a broader reflection on the effects and limitations of corporate-led development schemes targeting the urban poor and points to the contrasting logics of grassroots entrepreneurial urbanism and corporate-albeit “socially responsible“-parameters of success

Automated Generation and Integration of AUTOSAR ECU Configurations

Automotive Open System Architecture (AUTOSAR) is a system-level standard that is formed by the worldwide partnership of the automotive manufacturers and suppliers who are working together to develop a standardized Electrical and Electronic(E/E) framework and architecture for automobiles. The AUTOSAR methodology has two main activities: system configuration and the Electronic Control Unit (ECU) configuration. The system configuration is the mapping of the software components to the ECUs based on the system requirements. The ECU configuration process is an important part of the ECU software integration and generation. ECU specific information is extracted from the system configuration description and all the necessary information for the implementation such as tasks, scheduling, assignments of the runnables to tasks and configuration of the Basic Software (BSW) modules, are performed. This activity allows the ECU to modify the configuration parameters based on the vendor-specific requirements. Due to the high complexity and redundancy of this process, it has to be supported by different tool-related editors that can automatically generate source files like *.c and *.h for the configuration. In this thesis, we propose a method to automate the ECU configuration process for AUTOSAR. We use configuration templates written in xtend programming language along with a BSW generator tool developed at APAG Elektronik. This tool can extract the configuration parameters and automatically generate the required ECU module configuration. The Watchdog module will be used as an example to generate and integrate the ECU configuration. This enables the seamless generation of the software configurations from the system level requirements to the software implementation and therefore ensures consistency, correctness, cost efficiency and reduces the work done by the developer to generate the configuration

Distributed Planning for Self-Organizing Production Systems

Für automatisierte Produktionsanlagen gibt es einen fundamentalen Tradeoff zwischen Effizienz und Flexibilität. In den meisten Fällen sind die Abläufe nicht nur durch den physischen Aufbau der Produktionsanlage, sondern auch durch die spezielle zugeschnittene Programmierung der Anlagensteuerung fest vorgegeben. Änderungen müssen aufwändig in einer Vielzahl von Systemen nachgezogen werden. Das macht die Herstellung kleiner Stückzahlen unrentabel. In dieser Dissertation wird ein Ansatz entwickelt, um eine automatische Anpassung des Verhaltens von Produktionsanlagen an wechselnde Aufträge und Rahmenbedingungen zu erreichen. Dabei kommt das Prinzip der Selbstorganisation durch verteilte Planung zum Einsatz. Die aufeinander aufbauenden Ergebnisse der Dissertation sind wie folgt: 1. Es wird ein Modell von Produktionsanlagen entwickelt, dass nahtlos von der detaillierten Betrachtung physikalischer Produktionsprozesse bis hin zu Lieferbeziehungen zwischen Unternehmen skaliert. Im Vergleich zu existierenden Modellen von Produktionsanlagen werden weniger limitierende Annahmen gestellt. In diesem Sinne ist der Modellierungsansatz ein Kandidat für eine häufig geforderte "Theorie der Produktion". 2. Für die so modellierten Szenarien wird ein Algorithmus zur Optimierung der nebenläufigen Abläufe entwickelt. Der Algorithmus verbindet Techniken für die kombinatorische und die kontinuierliche Optimierung: Je nach Detailgrad und Ausgestaltung des modellierten Szenarios kann der identische Algorithmus kombinatorische Fertigungsfeinplanung (Scheduling) vornehmen, weltweite Lieferbeziehungen unter Einbezug von Unsicherheiten und Risiko optimieren und physikalische Prozesse prädiktiv regeln. Dafür werden Techniken der Monte-Carlo Baumsuche (die auch bei Deepminds Alpha Go zum Einsatz kommen) weiterentwickelt. Durch Ausnutzung zusätzlicher Struktur in den Modellen skaliert der Ansatz auch auf große Szenarien. 3. Der Planungsalgorithmus wird auf die verteilte Optimierung durch unabhängige Agenten übertragen. Dafür wird die sogenannte "Nutzen-Propagation" als Koordinations-Mechanismus entwickelt. Diese ist von der Belief-Propagation zur Inferenz in Probabilistischen Graphischen Modellen inspiriert. Jeder teilnehmende Agent hat einen lokalen Handlungsraum, in dem er den Systemzustand beobachten und handelnd eingreifen kann. Die Agenten sind an der Maximierung der Gesamtwohlfahrt über alle Agenten hinweg interessiert. Die dafür notwendige Kooperation entsteht über den Austausch von Nachrichten zwischen benachbarten Agenten. Die Nachrichten beschreiben den erwarteten Nutzen für ein angenommenes Verhalten im Handlungsraum beider Agenten. 4. Es wird eine Beschreibung der wiederverwendbaren Fähigkeiten von Maschinen und Anlagen auf Basis formaler Beschreibungslogiken entwickelt. Ausgehend von den beschriebenen Fähigkeiten, sowie der vorliegenden Aufträge mit ihren notwendigen Produktionsschritten, werden ausführbare Aktionen abgeleitet. Die ausführbaren Aktionen, mit wohldefinierten Vorbedingungen und Effekten, kapseln benötigte Parametrierungen, programmierte Abläufe und die Synchronisation von Maschinen zur Laufzeit. Die Ergebnisse zusammenfassend werden Grundlagen für flexible automatisierte Produktionssysteme geschaffen -- in einer Werkshalle, aber auch über Standorte und Organisationen verteilt -- welche die ihnen innewohnenden Freiheitsgrade durch Planung zur Laufzeit und agentenbasierte Koordination gezielt einsetzen können. Der Bezug zur Praxis wird durch Anwendungsbeispiele hergestellt. Die Machbarkeit des Ansatzes wurde mit realen Maschinen im Rahmen des EU-Projekts SkillPro und in einer Simulationsumgebung mit weiteren Szenarien demonstriert

Quantum algorithms applied to satellite mission planning for Earth observation

Earth imaging satellites are a crucial part of our everyday lives that enable global tracking of industrial activities. Use cases span many applications, from weather forecasting to digital maps, carbon footprint tracking, and vegetation monitoring. However, there are also limitations; satellites are difficult to manufacture, expensive to maintain, and tricky to launch into orbit. Therefore, it is critical that satellites are employed efficiently. This poses a challenge known as the satellite mission planning problem, which could be computationally prohibitive to solve on large scales. However, close-to-optimal algorithms can often provide satisfactory resolutions, such as greedy reinforcement learning, and optimization algorithms. This paper introduces a set of quantum algorithms to solve the mission planning problem and demonstrate an advantage over the classical algorithms implemented thus far. The problem is formulated as maximizing the number of high-priority tasks completed on real datasets containing thousands of tasks and multiple satellites. This work demonstrates that through solution-chaining and clustering, optimization and machine learning algorithms offer the greatest potential for optimal solutions. Most notably, this paper illustrates that a hybridized quantum-enhanced reinforcement learning agent can achieve a completion percentage of 98.5% over high-priority tasks, which is a significant improvement over the baseline greedy methods with a completion rate of 63.6%. The results presented in this work pave the way to quantum-enabled solutions in the space industry and, more generally, future mission planning problems across industries.Comment: 13 pages, 10 figues, 3 tables. Submitted to IEEE JSTAR

Conflict management and effectivity in multicultural teams: Team processes and conflict management styles

Objectives The main objectives of this study were to explore multicultural teams, conflict occurring in them, the effects of conflict and effectivity throughout teamwork. Specifically, the research attempts to find the appropriate conflict management style considering cultural diversity within teams that minimizes the negative effects of conflict. The paper also examines conflict and other team processes in different stages of teamwork, provides insight for the differences between the stages, and attempts to find ways to utilize cultural differences. The main objective is to optimize effectivity of multicultural teams. Summary The research was conducted to provide more insight for multinational corporations to utilize their diverse resources. Conflict has been proven to be either constructive or destructive for teamwork processes, which especially multicultural teams may find difficult due to the additional differences in the teams. Conflict management can be very effective and teach members to embrace constructive conflict, learn from it and reinforce more coherent teamwork. The beginning stages of teamwork are especially vulnerable as common processes are not established yet. Thus, the research studies conflict and effectivity of multicultural teams throughout team building stages and different team processes. Conclusions Multicultural team were found to be more complicated compared to homogeneous teams. Additional awareness and cultural competence training is needed for the beginning stages of teamwork, as the cultural differences were only learned throughout working, which makes initial work less effective. As members know each other personally, they are motivated to work more coherently and consider others. Coherence and common team identity increased negotiations and constructive conflict, and decreased relationship conflict and ambiguous processes