Gilbert Owusu

A fully multivariate DEUM algorithm

Distribution Estimation Using Markov network (DEUM) algorithm is a class of estimation of distribution algorithms that uses Markov networks to model and sample the distribution. Several different versions of this algorithm have been proposed and are shown to work well in a number of different optimisation problems. One of the key similarities between all of the DEUMalgorithms proposed so far is that they all assume the interaction between variables in the problem to be pre given. In other words, they do not learn the structure of the problem and assume that it is known in advance. Therefore, they may not be classified as full estimation of distribution algorithms. This work presents a fully multivariate DEUM algorithm that can automatically learn the undirected structure of the problem, automatically find the cliques from the structure and automatically estimate a joint probability model of the Markov network. This model is then sampled using Monte Carlo samplers. The proposed DEUM algorithm can be applied to any general optimisation problem even when the structure is not known.

ARMS: An automated resource management system for British Telecommunications plc

Abstract Accurate demand forecasting combined with resource planning is critical to a company’s performance and profitability. This paper describes ARMS (automated resource management system), an integrated system developed for the customer service operations of British Telecommunications plc to help with the operational/tactical planning and deployment of the company’s 20,000-strong field engineer workforce. ARMS integrates a forecasting tool with a resource planning tool and a resource balancing tool providing an end-to-end automated resource management solution for the organisation. OR techniques are used throughout the system, including ARIMA for forecasting, constraint satisfaction for problem modelling, heuristic search for problem solving thus demonstrating the value and relevance of OR in solving today’s business problems.

A multi-objective genetic type-2 fuzzy logic based system for mobile field workforce area optimization

In industries which employ large numbers of mobile field engineers (resources), there is a need to optimize the task allocation process. This particularly applies to utility companies such as electricity, gas and water suppliers as well as telecommunications. The process of allocating tasks to engineers involves finding the optimum area for each engineer to operate within where the locations available to the engineers depends on the work area she/he is assigned to. This particular process is termed as work area optimization and it is a sub-domain of workforce optimization. The optimization of resource scheduling, specifically the work area in this instance, in large businesses can have a noticeable impact on business costs, revenues and customer satisfaction.In previous attempts to tackle workforce optimization in real world scenarios, single objective optimization algorithms employing crisp logic were employed. The problem is that there are usually many objectives that need to be satisfied and hence multi-objective based optimization methods will be more suitable. Type-2 fuzzy logic systems could also be employed as they are able to handle the high level of uncertainties associated with the dynamic and changing real world workforce optimization and scheduling problems.This paper presents a novel multi-objective genetic type-2 fuzzy logic based system for mobile field workforce area optimization, which was employed in real world scheduling problems. This system had to overcome challenges, like how working areas were constructed, how teams were generated for each new area and how to realistically evaluate the newly suggested working areas. These problems were overcome by a novel neighborhood based clustering algorithm, sorting team members by skill, location and effect, and by creating an evaluation simulation that could accurately assess working areas by simulating one days worth of work, for each engineer in the working area, while taking into account uncertainties.The results show strong improvements when the proposed system was applied to the work area optimization problem, compared to the heuristic or type-1 single objective optimization of the work area. Such optimization improvements of the working areas will result in better utilization of the mobile field workforce in utilities and telecommunications companies.

An application of EDA and GA to dynamic pricing

E-commerce has transformed the way firms develop their pricing strategies, producing shift away from fixed pricing to dynamic pricing. In this paper, we use two different Estimation of distribution algorithms (EDAs), a Genetic Algorithm (GA) and a Simulated Annealing (SA) algorithm for solving two different dynamic pricing models. Promising results were obtained for an EDA confirming its suitability for resource management in the proposed model. Our analysis gives interesting insights into the application of population based optimization techniques for dynamic pricing.

On Optimising Resource Planning in BT plc with FOS

The need to move from reactive to proactive resource planning has been highlighted by industry analysts, academia and enterprises. Proactive resource planning provides business users with a view of future jobs, which in turn will help them to plan their workforce utilisation appropriately in order to reduce costs and improve customer satisfaction. This paper presents the application of FOS, an integrated service management system, for managing the resources of BT. FOS incorporates applications for reliable workload forecasting, optimised workforce planning, as well as advance tools for visualising and communicating the outputs to end users

Analysing the Effect of Demand Uncertainty in Dynamic Pricing with EAs

Dynamic pricing is a pricing strategy where a firm adjust the price for their products and services as a function of its perceived demand at different times. In this paper, we show how Evolutionary algorithms (EA) can be used to analyse the effect of demand uncertainty in dynamic pricing. The experiments are conducted in a range of dynamic pricing problems considering a number of different stochastic scenarios with a number of different EAs. The results are analysed, which suggest that higher demand fluctuation may not have adverse effect to the profit in comparison to the lower demand fluctuation, and that the reliability of EA for finding accurate policy could be higher when there is higher fluctuation then when there is lower fluctuation.

Modular neural networks for recursive collaborative forecasting in the service chain

In order to honour customer demand and sustain quality of service in BTs service chain, accurate forecasting for customer demand is critical for optimal resource planning. In the more general context of service organisations, failure to allocate sufficient resources to meet anticipated customer demand will lead to delayed or disrupted service provision which in turn will result in degraded quality of service for customers and ill-balanced utilisation of available resources. In this paper, we present our ongoing research on a prototype collaborative forecasting application, whereas organisations involved in a supply and demand partnership aim to co-operate by sharing and jointly forming forecasts to aid in resource planning. We identify key theoretical and implementation specific issues related to the area of collaborative forecasting and discuss our initial modular artificial neural network approach to the problem.

FOS: An Advanced Planning and Scheduling Suite for Service Operations

Advanced planning and scheduling (APS) has been a well-known term in manufacturing being used to refer to supply chain management suites and, more specifically the forecasting, planning and scheduling applications within them. However, the applicability of that part of ERP/SCM systems has been so far limited to product-driven industries. Despite the increased importance of services, integrated APS systems for service operations are yet to emerge from the main enterprise software vendors. In this paper, we present Field Optimisation Suite or FOS for short. FOS is an integrated APS platform for service operations. The platform is underpinned by operations research techniques for tackling complex decision making problems

Neural network demand models and evolutionary optimisers for dynamic pricing

Dynamic pricing is a pricing strategy where price for the product changes according to the expected demand for it. Some work on using neural network for dynamic pricing have been previously reported, such as for forecasting the demand and modelling consumer choices. However, little work has been done in using them for optimising pricing policies. In this paper, we describe how neural networks and evolutionary algorithms can be combined together to optimise pricing policies. Particularly, we build a neural network based demand model and use evolutionary algorithms to optimise policy over build model. There are two key benefits of this approach. Use of neural network makes it flexible enough to model a range of different demand scenarios occurring within different products and services, and the use of evolutionary algorithm makes it versatile enough to solve very complex models. We also evaluate the pricing policies found by neural network based model to that found by other widely used demand models. Our results show that proposed model is more consistent, adapts well in a range of different scenarios, and in general, finds more accurate pricing policy than other three compared models.

AI and computer-based methods in performance evaluation of sporting feats: an overview

Performance evaluation is a complex process, usually involving the analyses of large amounts of possibly subjective information. The complexity increases when the performances of more than one athlete are being evaluated. For example a coach in charge of twenty divers should be able to remember the strengths and weaknesses of each athlete. Given these difficulties, it is therefore not surprising that a number of computer-based systems have been developed to speed-up and improve the quality of performance evaluation. Most of these systems are visually based such that individuals working on performance analysis first record the motion in question by electronic means and then input these images into a computer for quantification and subsequent analysis. There seems to be enormous potential for AI (i.e. Artificial Intelligence) technologies to make a significant contribution in the analysis phase. Indeed AI technologies have been applied to performance evaluation in recent years, though their applicability has been limited for a variety of reasons. The main factor has been a lack of characterisation of the domain of performance evaluation. This paper reviews selected research and applications of computational models and AI technologies in particular in performance evaluation of sporting feats for individual based events.