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Title: Optimisation methods for staff scheduling and rostering : an employee-friendly approach
Author: Knight, Roger Alan
ISNI:       0000 0001 2418 528X
Awarding Body: City University London
Current Institution: City, University of London
Date of Award: 2008
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The growth in the global call centre industry over the last twenty years has been huge. The main motivating factor for businesses to introduce call centres as their main vehicle for handling customer contacts has been that call centres are inherently efficient. Since the mid-1980's, UK businesses have sought to establish competitive advantage by using call centres to reduce the cost of managing their customer contacts. Over the last decade or so, however, an alternative strategy has emerged based not on cost-reduction and efficiency, but on revenue generation and service quality. This new strategy places high value on customer and staff retention. This thesis is concerned with the operations management task of employee rostering. We argue that traditional models for producing rosters for call centre employees are designed to support the older efficiency-based culture, and are inappropriate for call centres adopting the more recent quality-based culture. We show how the use of methods and models drawn from conflicting management philosophies contributes to the high level of employee turnover, and inhibits the drive for service quality. Our primary contributions are to identify a set of rostering goals which reflect the interests of the employees, and to quantitatively represent these goals in a system of mathematical rostering models designed to support the revenue generation strategy. Our models are implemented using the robust Mixed Integer Programming methodology. In addition, we adapt our model to address the related problem of nurse rostering, and solve two benchmark problems to optimality. We demonstrate that our model generates rosters of a higher quality than the alternatives, at no additional cost.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: HD28 Management. Industrial Management