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 entitie s. Therefore, an automated resource allocation syst em that deals with MWs should assign tasks to them fai rly and in a comparatively equal manner. An unfair task allocation in a group will cause dissatisfacti on, 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 t he task allocation process. Disconnection causes un fair 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 ha s to perform the task in order to satisfy a pre-pla nned daily workload. Results: In this study we explore through the Run-Time phas e 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.