Abstract: Various technologies related to an enterprise computing platform are presented. Documents in a framework can be edited via logical views as described. An enterprise computing platform having a variety of frameworks can be configured to operate in a variety of business domains. Features such as parallel computing, distributed computing, logical documents, document transformation, space visualization, data security, and others can be accomplished via configuration rather than coding.

Abstract: Techniques and tools are described for determining optimal product assortments and optimal planograms using a hybrid binary multi-dimensional knapsack representation. An optimal product assortment and an optimal planogram can be determined by receiving one or more objectives, receiving one or more constraints, receiving dimensions and hierarchies, transforming the dimensions and hierarchies into structural graphs of s-cells, generating a dynamic model using, at least in part, the one or more objectives, performing an optimization run using the dynamic model, and outputting results of the optimization run. An optimal product assortment and an optimal planogram can also be determined by receiving product attributes for a set of products, receiving avatar information, receiving one or more objectives and constraints, generating a dynamic model using, performing an optimization run using the dynamic model, and outputting results of the optimization run.

Abstract: Various technologies related to an enterprise computing platform are presented. A new level of software development can be achieved by avoiding the coding process. An enterprise computing platform having a variety of frameworks can be configured to operate in a variety of business domains. Features such as parallel computing, distributed computing, logical documents, document transformation, space visualization, data security, and others can be accomplished via configuration rather than coding. Considerable efficiency improvements in the software development process can be realized.

Abstract: In one example, a method of optimization of resources is provided. The method comprises receiving data related to one or more predetermined parameters and one or more rules for optimization of one or more resources. The method further comprises encoding the received data. In addition, the method comprises generating one or more objective functions, one or more constraints, and one or more output indicators using the received data and the encoded data. Further, the method comprises generating one or more models and sub-models corresponding to the one or more resources. Furthermore, the method comprises generating one or more optimization results associated with the one or more sub-models using a distributed computing framework. The method further comprises generating an aggregated optimized result which is used for optimization of the one or more resources.

Abstract: Methods and systems for space and resource optimization are disclosed, including a method comprising receiving a plurality of inputs, transforming the plurality of inputs into at least one or more of (but not limited to) an algorithmic graph and a structural graph based on a domain-specific area using a computer processor. The method further includes creating and applying heuristics for parallelization, performing an optimization run, and analyzing an optimal result produced by the optimization run.