ARMS — Application of AI and OR Methods to Resource Management

  • Authors:
  • G. Owusu;C. Voudouris;R. Dorne;C. Ladde;G. Anim-Ansah;K. Gasson;G. Connolly

  • Affiliations:
  • -;-;-;-;-;-;-

  • Venue:
  • BT Technology Journal
  • Year:
  • 2003

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Abstract

This paper documents work on automating resource management in BT Retail. BT Retail manages BT's access network and provides telecommunications services to its customers. BT Retail's field engineers are allocated jobs via an information system known as Work Manager. In order to proactively position the engineers (i.e. resources) so as to service jobs in an optimal manner resource managers are involved in analysing the profiles of engineers in the light of incoming jobs and ‘selecting’ those profiles that will yield best quality of service (QoS) and reduce operational costs. A profile is a set of attributes that define a resource's capabilities (i.e. skills), capacity (i.e. availability), and location (i.e. area). Resource planning involves identifying an ‘optimal’ set of resource profiles. Accurate workload forecasting is sine qua non for optimal resource planning. To this end we have developed ARMS, Automated Resource Management System, a suite of components for workload forecasting and resource planning.