Simulation Based Resource Optimization Using a Decision Tree Clearing Function

Simulation Based Resource Optimization Using a Decision Tree Clearing Function

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This study presents a novel approach to resource allocation in software development teams working with Kanban.The simulation algorithm created in this study takes three types of resources, three types of work, resource capabilities, and a blocking mechanism different from the classic machine breakdown read more scenario.The data generated by the simulations are used to train a decision tree regression which is integrated into an optimization model as a clearing function.In numerical analysis, il barone wine the research compares the decision tree clearing function to a straightforward two-step model that only takes the best of the simulation data and finds a resource allocation and a greedy heuristic algorithm which starts from an initial feasible solution and improves it step-by-step.Findings show that the developed decision tree clearing function model outperforms the other two benchmark models in mid and high amounts of data.