Formalizing self-organizing processes of task allocation

Cycle Time Optimization of Processes Using an Entropy-Based Learning for Task Allocation

Cycle time optimization could be one of the great challenges in business process management. Although there is much research on this subject, task similarities have been paid little attention. In this paper, a new approach is proposed to optimize cycle time by minimizing entropy of work lists in resource allocation while keeping workloads balanced. The idea of the entropy of work lists comes fr...

Towards an Artificial Hormone System for Self-organizing Real-Time Task Allocation

This article presents the concept of an artificial hormone system for a completely decentralized realization of self-organizing task allocation. We show that tight upper bounds for the real-time behavior of self-configuration can be given. We also show two simulation results using the artificial hormone system demonstrating the operation of the artificial hormone system under different workloads.

Landforms identification using neural network-self organizing map and SRTM data

During an 11 days mission in February 2000 the Shuttle Radar Topography Mission (SRTM) collected data over 80% of the Earth's land surface, for all areas between 60 degrees N and 56 degrees S latitude. Since SRTM data became available, many studies utilized them for application in topography and morphometric landscape analysis. Exploiting SRTM data for recognition and extraction of topographic ...

Formalizing Guidelines for Building Meaningful Self- Organizing Maps

Cycle Time Reduction and Runtime Rebalancing by Reallocating Dependent Tasks

Business Process Management Systems (BPMS) is a complex information system that provides designing, administrating, and improving the business processes. Task allocation to human resources is one of the most important issues which should be managed more efficiently in BPMS. Task allocation algorithms are defined in order to meet the various policies of organizations. The most important of these...