Mushroom picking heuristics framework for knapsack-like problems of resource allocation

Resource allocation is a complex challenge that extends across diverse disciplines, each presenting its distinct considerations and demands. This intricate task involves the distribution of resources in a manner that meets the needs and objectives of various sectors. In this study, we propose an innovative mushroom picker heuristics to knapsack-like resource allocation problems, mainly with product categorization, wherein each potential solution is metaphorically likened to a mushroom. The heuristic process comprises several stages: first, the preparation of the forest ground, followed by the identification of distinct mushroom clearings, then the search for mushrooms within these clearings, and finally, the decision-making process regarding the selection and collection of mushrooms. Through this heuristic framework, we aim to elucidate effective strategies for solution discovery and decision-making in complex problem domains. Twelve tuning parameters are presented to reduce the solution space. We provide an explanation of the application of the proposed mushroom picking heuristics on the basis of two problems: (1) the shelf space allocation in retail and (2) the commercial to TV break placement in media planning. This algorithm can also be used to solve other problems that can be modelled as knapsack problems.