Sid Shakya

Fuzzy dominance rules for real-world many objective optimization

In real world optimization problems there are often multiple objectives to consider. However, with traditional multi-objective optimization algorithms, like the Non-Dominated Sorting Genetic Algorithm, NSGA-II, one solution is not produced at the end of the process but a set of non-dominated solutions. This set of solutions make up what is known as the Pareto front. The Pareto front relies on calculating the dominance of each solution the multi-objective algorithm produces. Traditional dominance calculations are reasonable for a small number of objectives. However, the more objectives there are in the problem, the more unsuitable these dominance calculations become. This leads to poor selection criteria and ultimately a weaker form of optimization when compared to a small number of objectives. In this paper, we present a fuzzy logic system for computing dominance between two solutions. We have evaluated this fuzzy logic system in optimizing a set of black box test problems. In addition, we have also applied it to a real world many-objective system that optimizes five conflicting objectives, in the telecommunications domain. The implementation of the fuzzy logic system has led to the NSGA-II algorithm with Fuzzy Dominance Rules (FDRs) being able to perform better in a number of black box tests and improving the results of our real-world many-objective optimization problem, with a statistically significant improvement to the hypervolume of 5.46%.

Spares parts optimization for legacy telecom networks using a permutation-based evolutionary algorithm

Large service organizations such as telecom or utility companies face numerous decision management problems, many of which are related to the management of various resources. The management of spare parts and inventory is one of the key resource management problems an organization faces on a daily basis. Especially their timely availability can have serious impacts on the service quality and the customer satisfaction. Lack of visibility and availability of spare parts in the right place and at the right time can lead to traveling longer distance to supply the parts or in worst case a disruption of the service. We propose to automate the management of spare parts for a legacy technology in a telecom network by leveraging an evolutionary algorithm for optimizing the distribution of spare parts. Our results show that a significant gain can be made in comparison to a assignment typically performed in a manual mechanism.

Predicting Service Levels Using Neural Networks

In this paper we present a method to predict service levels in utility companies, giving them advanced visibility of expected service outcomes and helping them to ensure adherence to service level agreements made to their clients. Service level adherence is one of the key targets during the service chain planning process in service industries, such as telecoms or utility companies. These specify a time limit for successful completion of a certain percentage of tasks on that service level agreement. With the increasing use of automation within the planning process, the requirement for a method to evaluate the current plan decisions effects on service level outcomes has surfaced.

The Role of Service Quality in Transforming Operations

The introduction of any tool requires changes in the users’ environment to use the tool. McAfee (Harv Bus Rev November: 141–149, 2006) highlights this point by stating that the challenges in IT projects are not just technical but managerial. Here, managerial refers to embedding the system within the organisation. Kotter says ‘in the final analysis, change sticks when “it becomes the way we do things here”, when it seeps into the bloodstream of the corporate body’ (Kotter, Harv Bus Rev January: 96–103, 2007). Clearly, the success of any IT transformation programme is in part a function of the quality of service being provided by the system. BT not only is a consumer of service and field automation technologies but also provides production management solutions to other industries. We have observed (both qualitatively and quantitatively) from our experiences of developing production management systems that the quality of the services being provided by production management systems is dependent on the perceptions of the users of the system. This correlates with the measures put in place for engagement between the development team and the end users during the life cycle of the development.