Abdullah Alsheddy

BSc, MSc, PhD in Computer Science

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My PhD Thesis

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On Thursday 11th of August 2011, I have passed my PhD viva. The title of the thesis is "Empowerment Scheduling: A Multi-objective Optimization Approach Using Guided Local Search". (download a copy from here!)

My two External Examiners, Professor Xin Yao of Birmingham and Dr Ian Miguel of St Andrews, conducted a robust examination, overseen by Independent Chair, Professor John Gillies.

My research focuses on field workforce scheduling, which involves scheduling staff to multiple jobs in different geographical locations. A good example is British Telecommunication’s daily scheduling of its 10,000+ technicians to serve its customers. I have worked on a management concept called “employee 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.

I have advanced the field of employee empowerment by providing a rigorous formulation of the problem. Before this work, researchers gave examples to empowerment but no formal frameworks have been proposed to characterise empowerment. I have provided a model that enables quantitative assessment of costs and effects.

To tackle the scheduling problem under empowerment, I have extended the well-known single-objective Guided Local Search to multi-objective optimization. The extension is nontrivial in its own right. Its performance is comparable to state-of-the-art multi-objective methods on two of the most used multiobjective benchmark problems: “travelling salesman” and “knapsack” problems.

 

 

 

 

 

 The abstract of the thesis is given below.

 

Abstract of my PhD Thesis entitled: "Empowerment Scheduling: A Multi-objective Optimization Approach Using Guided Local Search"

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.

 

Last Updated on Tuesday, 04 June 2013 20:04