Grass Cutter Heuristics for Knapsack-Like Problems of Resource Allocation

Resource allocation emerges as a multifaceted challenge spanning various disciplines, each with its own unique considerations and requirements. This study presents innovative grass-cutter heuristics devised to address knapsack-like resource allocation problems incorporating product categorization. These heuristics are designed to streamline the solution process, enhancing efficiency and profitability. We implemented a set of twelve parameters strategically aimed at narrowing down the solution space. By doing so, we aim to craft solutions that are relatively profitable while avoiding random element generation and avoiding exhaustive exploration of the entire solution space. We give examples of the application of the proposed approach to two problems: (1) the shelf space allocation in retail and (2) the commercial to TV break placement in media planning.