Providing Fairness to Mobile Workforces in an Automated Task Allocation Process: A Semantic Multi-agent Approach

Abstract

Problem statement: Mobile Workforces (MW) unlike computational resources of an automated system are active but not passive entities. Therefore, an automated resource allocation system that deals with MWs should assign tasks to them fairly and in a comparatively equal manner. An unfair task allocation in a group will cause dissatisfaction, which in turn demotivates MWs who are supposed to work as a team. Approach: In an automated Mobile Workforce Brokering System (MWBS) tasks are automatically assigned to MWs at Run-Time phase of the system’s run. However, the environmental risks specifically risk of disconnection disrupts the task allocation process. Disconnection causes unfair task allocation when an MW must carry the next upcoming task according to a rotator work schedule, but he is disconnected. In this situation another MW has to perform the task in order to satisfy a pre-planned daily workload. Results: In this study we explore through the Run-Time phase of MWBS and explain how its underpinning ontology-driven coordination model tackles the risk of disconnection and improves the fairness in the task allocation process. Conclusion: Moreover, fairness rates in task allocation processes are compared between an existing system and MWBS and improvement in fairness rate is shown and analyzed for 4 consecutive periods (months) of the system’s run.