Abdullah Alsheddy

PhD 2007-2011
Constraint Satisfaction and Optimization Laboratory
School of Computer Science and Electronic Engineering
University of Essex
email @essex.ac.uk: aalshe
URL: www.alsheddy.com/

Abdullah joined the University of Essex as a Master student in October 2006. He won the R.A. Brooker Prize for being the best overall performance on the MSc Computer Science course.

Abdullah joined the Constraint Satisfaction and Optimization Laboratory as a PhD student in October 2007. He passed his viva on 11 August 2011. He is a member of the Flexible Workforce Management Project, which is sponsored by British Telecom . Abdullah was appointed as a part-time research officer on a BT-funded project (involving Etisalat) during his PhD studies.

Abdullah's Research:
Abdullah's research focuses on fieldwork scheduling, which involves scheduling staff to multiple jobs. A good example is BT's daily scheduling of its 10,000+ technicians to serve its customers. Abdullah worked on a management concept called "staff empowerment". The idea is to allow employees some say in scheduling decisions. The aim is to improve morale, which hopefully will be translated into enhanced efficiency -- win-win for both employer and employees.

Abdullah's Contributions:
Abdullah's research contributes to both management and computer science. Abdullah advanced the field of staff empowerment by providing a rigorous formulation of the problem. Before Abdullah's work, researchers gave examples to empowerment but no formal frameworks have been proposed to characterise empowerment. Abdullah provides a model that enables quantitative assessment of costs and effects. To tackle the scheduling problem under empowerment, Abdullah extended Guided Local Search to multi-objective optimization. The extension is nontrivial in its own right. Its performance is comparable to state-of-the-art methods on two of the most used benchmark problems.

More specifically:

Abstract of Abdullah's thesis (submitted for examination June 2011):

Field Workforce Scheduling (FWS) is a very important and practical problem in service industries. It concerns the scheduling of multi-skilled employees to geographically dispersed tasks. In FWS, employee efficiency is highly important, and thus they have to be managed in an effective way. Employee empowerment is a relatively new and flexible management concept. It promises to benefit both organizations and employees by enhancing employee morale, satisfaction and productivity. This motivates the incorporation of empowerment when designing FWS models, which has not been thoroughly investigated.

This thesis describes the development of a new efficient empowerment scheduling model, called EmS, for FWS. The key feature of EmS is that it is strongly linked to the management literature on empowerment from which the requirements are derived. EmS provides employees with a simple, yet flexible and fair means of involvement in the scheduling decision, through which they can suggest their own schedules. This is formulated using a multi-objective optimization (MOO) approach where the task is to find a balance between employee empowerment and the employer's interest. To evaluate EmS, a series of empirical experiments are conducted, presenting the first extensive and in-depth study of the feasibility of empowerment in the FWS context, as well as the efficiency of an empowerment scheduling model.

To tackle the empowerment scheduling problem, a new method based on Guided Local Search (GLS) is developed. GLS is a simple, yet effective single-objective metaheuristic with few parameters to tune. As a pioneering work, we propose an extension to GLS (called GPLS) as a general method for tackling MOO problems. In addition, a number of GPLS-based frameworks are proposed, which prove the potential of GPLS to be a central part of more advanced frameworks. GPLS and its frameworks are extensively tested on standard MOO benchmarks, and EmS. Computational results suggest that GPLS is comparable to state-of-the-art MOO metaheuristics.

This page is maintained by Edward Tsang; last updated 2011.08.12
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