Kanban ile çalışan yazılım geliştirme ekiplerinin kaynak optimizasyonu

Kanban has been a commonly used agile and lean way of working for software development teams due to its evolutionary approach, effectiveness in project management, and wide range of applicability. This thesis takes a holistic approach to investigate the optimization of software development teams working with Kanban. Due to the complexity of the nature of work, which involves multiple workstations, resources with different skill sets, work types, blocking scenarios, and work-in-process limits, simulations are conducted to gather data. The study proposes a solution framework that approaches the problem at two levels and an additional exploratory multi-objective optimization section using modern techniques to delve deeper into potential directions. The first level of the solution framework starts with a novel, comprehensive simulation algorithm that introduces a blocking scenario applied to the work items, unlike the machine breakdown method used in the manufacturing processes. The simulation model takes input parameters derived from a real-life company, adds resources, and WIP limits as other input parameters and collects data including output, lead time, blocking time, efficiency of resources, and more. This simulation module is then plugged into the solution methods including a greedy heuristic, a two-step model, and a decision tree clearing function to compare the three methods and find the best-performing under different conditions. The second level of the solution framework extends the simulation model by adding resource capabilities and work type priorities ending in over tens of millions of potential combinations. Due to the intractability of generating all possible combinations, three machine learning algorithms -random forest, XGBoost, and neural networks- are used to find the best potential feature set. Finally, a holistic approach is taken to account for lead time, output, and cost via multi-objective optimization models. Numerical analysis provides insights into the impact of resources, and work-in-progress limits through three strong evolutionary algorithms, which are strength Pareto evolutionary algorithm-II, Pareto archived evolution strategy, and non-dominated sorting genetic algorithm-II. The application of business priorities and the effect of these choices are demonstrated in navigating the trade-offs between cost, lead time, and output. This dissertation contributes significantly to both academic knowledge and practical applications, presenting pioneering insights into optimizing the output and lead time of a Kanban software development team, impacts of resources, work-in-progress limits, and priorities and provides a set of state-of-the-art practical solutions for achieving this goal.