A genetic type-2 fuzzy logic based approach for the optimal allocation of mobile field engineers to their working areas

In utility based service industries with a large mobile workforce, there is a need to optimize the process of allocating engineers to tasks (i.e. fixing faults, installing new services, such as internet connections, gas or electricity etc.). Part of the process of optimizing the resource allocation to tasks involves finding the optimum area for an engineer to operate within, which we term as work area optimization. Work area optimization in large businesses can have a noticeable impact on business costs, revenues and customer satisfaction. However when attempting to optimize the workforce in real world scenarios, mostly single objective optimization algorithms are used while employing crisp logic. Nevertheless, there are many objectives that need to be satisfied and hence multi-objective based optimization will be more suitable. Even where multi-objective optimization is employed, the involved systems fail to recognize that these real world problems are full of uncertainties. Type-2 fuzzy logic systems can handle the high level of uncertainties associated with the dynamic and changing environments, such as those presented with real world scheduling problems. This paper presents a novel multi-objective genetic type-2 Fuzzy Logic based System for the optimal allocation of mobile workforces to their working areas. The method has been applied in a real world service industry workforce environment. The results show strong improvements when the proposed multi-objective type-2 fuzzy genetic based optimization system was applied to the work area optimization problem as compared to the heuristic or type-1 single objective optimization of the work area. Such optimization improvements of the working areas will result in improving the utilization of the workforce

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. We build neural network models to predict using the current state of the capacity plan, investigating the accuracy when predicting both daily and weekly service level outcomes. It is shown that the models produce a high accuracy, particularly in the weekly view. This provides a solution that can be used to both improve the current planning process and also as an evaluator in an automated planning process

iPatch: A Many-Objective Type-2 Fuzzy Logic System for Field Workforce Optimisation

Employing effective optimisation strategies in organisations with large workforces can have a clear impact on costs, revenues, and customer satisfaction. This is particularly true for organisations that employ large field workforces, such as utility companies. Ensuring each member of the workforce is fully utilised is a challenging problem as there are many factors that can impact the organisation's overall performance. We have developed a system that optimises to make sure we have the right engineers, in the right place, at the right time, with the right skills. This system is currently deployed to help solve real-world optimisation problems, which means there are many objectives to consider when optimising, and there is much uncertainty in the environment. The latest version of the system uses a multi-objective genetic algorithm as its core optimisation logic, with modifications such as Fuzzy Dominance Rules (FDRs), to help overcome the issues associated with many-objective optimisation. The system also utilises genetically optimised type-2 fuzzy logic systems to better handle the uncertainty in the data and modelling. This paper shows the genetically optimised type-2 fuzzy logic systems producing better results than the crisp value implementations in our application. We also show that we can help address the weaknesses in the standard NSGA-II dominance calculations by using FDRs. The impact of this work can be measured in a number of ways; productivity benefit of £1million a year, the reduction of over 2,500 metric tonnes of CO2 and a possible prevention of over 100 serious injuries and fatalities on the UK's roads

Tactical plan optimisation for large multi-skilled workforces using a bi-level model.

The service chain planning process is a critical component in the operations of companies in the service industry, such as logistics, telecoms or utilities. This process involves looking ahead over various timescales to ensure that available capacity matches the required demand whilst maximizing revenues and minimizing costs. This problem is particularly complex for companies with large, multi-skilled workforces as matching these resources to the required demand can be done in a vast number of combinations. The vastness of the problem space combined with the criticality to the business is leading to an increasing move towards automation of the process in recent years. In this paper we focus on the tactical plan where planning is occurring daily for the coming weeks, matching the available capacity to demand, using capacity levers to flex capacity to keep backlogs within target levels whilst maintaining target levels for provision of new revenues. First we describe the tactical planning problem before defining a bi-level model to search for optimal solutions to it. We show, by comparing the model results to actual planners on real world examples, that the bi-level model produces good results that replicate the planners' process whilst keeping the backlogs closer to target levels, thus providing a strong case for its use in the automation of the tactical planning process

Predictive planning with neural networks.

Critical for successful operations of service industries, such as telecoms, utility companies and logistic companies, is the service chain planning process. This involves optimizing resources against expected demand to maximize the utilization and minimize the wastage, which in turn maximizes revenue whilst minimizing the cost. This is increasingly involving the automation of the planning process. However, due to unforeseen factors, the calculated optimal allocation of resources to complete tasks often does not match up with what is actually occurring on the day. This factor highlights a requirement for a method of predicting accurately the number of tasks that will be completed given a known amount of resources and demand in order to produce a more accurate plan

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 day's 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

A comparison of particle swarm optimization and genetic algorithms for a multi-objective Type-2 fuzzy logic based system for the optimal allocation of mobile field engineers

In real world applications it can often be difficult to determine which optimization algorithm to use. This is especially true if the problem has multiple objectives, which is a common occurrence in real world applications. Both Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) algorithms have been explored, often being compared to each other. As problems are scaled up to more objectives, the suitability of these algorithms can change and would need to be modified. The most common multi-objective algorithms in use are Multi-Objective Genetic Algorithms (MOGA) and Multi-Objective Particle Swarm Optimization (MOPSO), which we are choosing to evaluate, as they can be tested in both their single and multi-objective forms. Real world applications often come with many conditions and constraints. The one being examined in this paper is concerned with the optimal design of working areas, for a large scale mobile workforce in the telecommunications utilities domain. This paper presents the suitable underlying algorithm to use for this problem with the aim of maximizing the utilization of the workforce, whilst having balanced and manageable working areas. The results show that genetic algorithms, in both its single and multi-objective forms, may be the most suitable option for this problem, when compared to PSO and MOPSO algorithms. The results also show that organizing the problem geographically helps the particle swarm algorithms

A fuzzy-genetic tactical resource planner for workforce allocation

For the recent few years, resource planning has become an interesting research topic for many companies, especially within telecommunications domain. Resource planning is basically trying to provide a high quality of service while trying to keep the cost as low as possible. The main aim of resource planning is to utilize the available resources as much as possible so that they can match the expected demand for services. Tactical resource planning looks at medium-term planning periods, i.e. weeks to months, and aims to establish coarse-grain resource deployments. In our previous work we introduced an experimental fuzzy based resource planning approach modeled for a delivery unit in British Telecom (BT) [1]. We presented a hierarchical based fuzzy logic system, which calculates the compatibility between resources and the allocated tasks, and then matches the most compatible tasks and resources to each other. The proposed hierarchical fuzzy logic based system (in an experimental setting) was able to achieve very good results in comparison to the original system, where the proposed system was able to achieve 12.2% improvement in tasks done per resource. In this paper, we introduce a hierarchical fuzzy logic based system that uses evolutionary systems to tune the fuzzy membership functions, which result in an improvement in the overall output of the system. The new fuzzy-genetic based system was able achieve better improvement in tasks done per resource than the hierarchical fuzzy logic based system that was tuned by experts. © 2013 IEEE

A type2 Fuzzy Logic System for workforce management in the telecommunications domain

Workforce management is one of the most important factors in the success of any company that provides its customers with services. Hence, in order for the company to achieve objectives like customer satisfaction and maximum resource utilization, there is a need to have a reliable means of efficiently managing the company workforce and making sure that the produced plan always gives a good choice when it comes to assigning the available technicians to the given jobs. As the quantity of services and the workforce grow, the use of an automated workforce management system becomes inevitable. However the automated workforce management system should allow full transparency to allow the user to interact with the generated plans. In addition, the workforce management systems face high levels of uncertainties when dealing with real-world scenarios, which necessitates employing systems, which are able to handle the linguistic and numerical uncertainties available in the real-world scenarios. Fuzzy Logic Systems (FLSs) are credited with providing transparent methodologies that can deal with the imprecision and uncertainties. However the vast majority of the FLSs employ the type-1 FLSs, which cannot directly handle the high levels of uncertainties. Type-2 FLSs which employ type-2 fuzzy sets can handle such high levels of uncertainties to give very good performances. In this paper, we will present a type-2 FLS based workforce management system that is being developed for a delivery unit in British Telecom (BT). We will show how the presented system was able to handle the faced uncertainties to give very good performance that outperformed the automated non-intelligent system and the type-1 FLSs based system. © 2012 IEEE

A genetic interval type-2 fuzzy logic based approach for operational resource planning

Within service providing industries, one of the challenges facing resource planners is to match the demand for services by trying to utilize the available resources as best as possible. The problem faced by the operational resource planner is to build a refined plan of tasks to resources for each day in a manner that the plan can be directly dispatched to the distributed available engineering field force. In this paper, we will introduce a genetic hierarchical interval type-2 fuzzy logic based operational planner. We will present experiments which will show that the proposed system is able to produce more efficient plans when compared to the traditional crisp logic based algorithms which employ hill climbing heuristic based search techniques. We will show also that the proposed system outperforms the type-1 fuzzy logic based counterparts. © 2013 IEEE