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

Bi-level optimisation and machine learning in the management of large service-oriented field workforces.

The tactical planning problem for members of the service industry with large multi-skilled workforces is an important process that is often underlooked. It sits between the operational plan - which involves the actual allocation of members of the workforce to tasks - and the strategic plan where long term visions are set. An accurate tactical plan can have great benefits to service organisations and this is something we demonstrate in this work. Sitting where it does, it is made up of a mix of forecast and actual data, which can make effectively solving the problem difficult. In members of the service industry with large multi-skilled workforces it can often become a very large problem very quickly, as the number of decisions scale quickly with the number of elements within the plan. In this study, we first update and define the tactical planning problem to fit the process currently undertaken manually in practice. We then identify properties within the problem that identify it as a new candidate for the application of bi-level optimisation techniques. The tactical plan is defined in the context of a pair of leader-follower linked sub-models, which we show to be solvable to produce automated solutions to the tactical plan. We further identify the need for the use of machine learning techniques to effectively find solutions in practical applications, where limited detail is available in the data due to its forecast nature. We develop neural network models to solve this issue and show that they provide more accurate results than the current planners. Finally, we utilise them as a surrogate for the follower in the bi-level framework to provide real world applicable solutions to the tactical planning problem. The models developed in this work have already begun to be deployed in practice and are providing significant impact. This is along with identifying a new application area for bi-level modelling techniques

Cross-trained workforce planning models

Cross-training has emerged as an effective method for increasing workforce flexibility in the face of uncertain demand. Despite recently receiving substantial attention in workforce planning literature, a number of challenges towards making the best use of cross-training remain. Most notably, approaches to automating the allocation of workers to their skills are typically not scalable to industrial sized problems. Secondly, insights into the nature of valuable cross-training actions are restricted to a small set of predefined structures. This thesis develops a multi-period cross-trained workforce planning model with temporal demand flexibility. Temporal demand flexibility enables the flow of incomplete work (or carryover ) across the planning horizon to be modelled, as well as an the option to utilise spare capacity by completing some work early. Set in a proposed Aggregate Planning stage, the model permits the planning of large and complex workforces over a horizon of many months and provides a bridge between the traditional Tactical and Operational stages of workforce planning. The performance of the different levels of planning flexibility the model offers is evaluated in an industry motivated case study. An extensive numerical study, under various supply and demand characteristics, leads to an evaluation of the value of cross-training as a supply strategy in this domain. The problem of effectively staffing a pre-fixed training structure (such as the modified chain or block) is an aspect of cross-training which has been extensively studied in the literature. In this thesis, we attempt to address the more frequently faced problem of ‘how should we train our existing workforce to improve demand coverage?’. We propose a two-stage stochastic programming model which extends existing literature by allowing the structure of cross-training to vary freely. The benefit of the resulting targeted training solutions are shown in application using a case study provided by BT. A wider numerical study highlights ‘rules-of-thumb’ for effective training solutions under a variety of characteristics for uncertain demand

Many-Objective Genetic Type-2 Fuzzy Logic Based Workforce Optimisation Strategies for Large Scale Organisational Design

Workforce optimisation aims to maximise the productivity of a workforce and is a crucial practice for large organisations. The more effective these workforce optimisation strategies are, the better placed the organisation is to meet their objectives. Usually, the focus of workforce optimisation is scheduling, routing and planning. These strategies are particularly relevant to organisations with large mobile workforces, such as utility companies. There has been much research focused on these areas. One aspect of workforce optimisation that gets overlooked is organisational design. Organisational design aims to maximise the potential utilisation of all resources while minimising costs. If done correctly, other systems (scheduling, routing and planning) will be more effective. This thesis looks at organisational design, from geographical structures and team structures to skilling and resource management. A many-objective optimisation system to tackle large-scale optimisation problems will be presented. The system will employ interval type-2 fuzzy logic to handle the uncertainties with the real-world data, such as travel times and task completion times. The proposed system was developed with data from British Telecom (BT) and was deployed within the organisation. The techniques presented at the end of this thesis led to a very significant improvement over the standard NSGA-II algorithm by 31.07% with a P-Value of 1.86-10. The system has delivered an increase in productivity in BT of 0.5%, saving an estimated £1million a year, cut fuel consumption by 2.9%, resulting in an additional saving of over £200k a year. Due to less fuel consumption Carbon Dioxide (CO2) emissions have been reduced by 2,500 metric tonnes. Furthermore, a report by the United Kingdom’s (UK’s) Department of Transport found that for every billion vehicle miles travelled, there were 15,409 serious injuries or deaths. The system saved an estimated 7.7 million miles, equating to preventing more than 115 serious casualties and fatalities

The safety and sustainability of mining at diverse scales: Placing health and safety at the core of responsibility

Mining plays a major role in meeting global resource demands with Europe hosting extensive mineral potential. However, few of these prospects are feasible for conventional exploitation due to their small size & ore grade, proximity to dense populations and tenement constraints. Hence, a significant paradigm shift towards switch-on, switch off small-scale mining (SOSO SSM) is needed in order to increase the viability of small, complex, high-grade deposits. The IMP@CT project developed mobile, modularised solutions to facilitate rapid deployment and in-situ extraction & processing, which necessitates the translation and extension of best practice safety and sustainability from established national regulations and industry standards. Despite decades of accumulated safety regulation, knowledge and experience, workplace errors and violations still lead to fatal accidents, particularly if immature safety attitudes and behaviours pervade an organisation. The presence of a mature safety culture is vital for mitigating the occurrence of injuries and fatalities, through a collective commitment to safety improvement. This study has aimed to consolidate safety and sustainability best practice that is tailored to SSM by identifying the critical safety considerations and applying safety culture maturity principles to the specific challenges associated with a semi-automated SOSO SSM system. Criteria-driven maturity modelling, informed by existing responsible mining initiatives and safety and socio-environmental culture perspectives from site personnel at all hierarchical levels, is developed to assess the environmental and social factors associated with small- to medium-scale regulated mining. The role of agile management for rapid adaptation and continuous improvement of safety and sustainability performance in SOSO SSM is discussed. This research has demonstrated that for SOSO SSM to effectively integrate a mature safety and socio-environmental culture within a flexible, containerised mining paradigm, managerial and technical agility, and human initiative must be encouraged to continuously drive progress in occupational health and safety and generate wider societal benefit

Distributionally Robust Resource Planning Under Binomial Demand Intakes

In this paper, we consider a distributionally robust resource planning model inspired by a real-world service industry problem. In this problem, there is a mixture of known demand and uncertain future demand. Prior to having full knowledge of the demand, we must decide upon how many jobs we will complete on each day of the plan. Any jobs that are not completed by the end of their due date incur a cost and become due the following day. We present two distributionally robust optimisation (DRO) models for this problem. The first is a non-parametric model with a phi-divergence based ambiguity set. The second is a parametric model, where we treat the number of uncertain jobs due on each day as a binomial random variable with an unknown success probability. We reformulate the parametric model as a mixed integer program and find that it scales poorly with the ambiguity and uncertainty sets. Hence, we make use of theoretical properties of the binomial distribution to derive fast heuristics based on dimension reduction. One is based on cutting surface algorithms commonly seen in the DRO literature. The other operates on a small subset of the uncertainty set for the future demand. We perform extensive computational experiments to establish the performance of our algorithms. Decisions from the parametric and non parametric models are compared, to assess the benefit of including the binomial information

A conceptual framework for strategic long term planning of platinum mining operations in the South African context

The challenge facing a South African mining company, with multiple mining rights to platinum mineral resources, is to create sustainable value whilst operating within mandated strategic bounds, identified constraints, and variable market and economic conditions. This can be achieved by allowing the fixed physical nature of the mineral asset to drive definition of the optimal (lowest capital and operating cost) technical solution to mining and processing activities, and developing and resourcing a strategically aligned portfolio of production entities that creates flexibility to near and longer term business environment shifts, i.e. a production mix that allows variation of output (metals, operating cost, capital intensity) to respond to short term market variation, within a long term context. The practical achievement of this outcome is enabled by the application of the strategic long term planning framework. The framework logic, methodology and components are described and the application demonstrated through a case study (Anglo Platinum Limited). Prior to definition and description of the strategic long term planning conceptual framework, the context of the South African platinum industry is described through consideration of the characteristics of the mineral resource, the platinum value chain, the PGM market, and the global and local business environment. The core elements of the framework, and the relationship between them, are expanded: scenario planning, business value optimisation, long term planning parameters, long term planning procedures, capital investment prioritisation, project value tracking, the relationship of the long term plan to the business plan, contingency planning and execution plans for supporting capability (projects, metallurgical, infrastructure and people). The implementation of the framework at Anglo Platinum Limited is considered over the period 2004 to 2007 with a description of the business response, facilitated by the framework and established capability, following the 2008 global financial crisis. It is concluded that the strategic long term planning framework is a logic construct that enables delivery of an optimised, strategically aligned, business plan from the mineral asset portfolio using a set of tools and techniques with a common language, standards, systems and processes that align decisions and actions on a cyclical basis

Investigating enterprise resource planning adoption and implementation in service sector organisations

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThis thesis investigates Enterprise Resource Planning (ERP) adoption and implementation in Service Sector Organisations (SSOs). ERP is a business management system that has emerged to support organisations to use a system of integrated applications to enhance their Information Technology (IT) infrastructures, enhance business processes and deliver high quality of services. Regardless of the fact that several other sector organisations have adopted and implemented ERP systems, its application in SSOs is rather inadequate. Among other reasons, two core rationales can be attributed to the latter fact – firstly, SSOs lack the sufficient knowledge, expertise and training to implement such sophisticated integrated systems and secondly, the top management lacks the ability to take appropriate decisions for ERP adoption and implementation. However, merely focusing on a number of factors influencing ERP adoption and implementation may not be suffice, as there is a need for a systematic decision-making process for adopting and implementing ERP systems in SSOs. The limited number of ERP systems’ applications in SSOs has resulted in inadequate research in this area with many issues, like its adoption and implementation requiring further exploration. Despite, the implications of ERP systems have yet to be assessed in SSOs, leaving ample scope for relevance and producing a unique piece of research work. Thus, the author demonstrates that it is of high importance to investigate this area within SSOs and contribute towards successful ERP adoption and implementation. This thesis makes a step forward and contributes to the body of knowledge as it: investigates factors influencing the decision-making process for ERP adoption and implementation in SSOs, prioritises the importance of factors influencing ERP adoption and implementation, evaluates ERP lifecycle phases and stages, maps the ERP factors on different phases and stages of the ERP lifecycle, and in doing so, to propose a model for ERP adoption and implementation in SSOs. The author claims that such an ERP adoption and implementation process in SSOs is significant and novel as: it extends established norms for ERP adoption and implementation, by including Analytical Hierarchy Process (AHP) technique for prioritising the importance of factors, thus, facilitating SSOs to produce more robust proposals for ERP adoption and implementation. The author further assess the proposed ERP adoption and implementation model by using a qualitative, interpretive, multiple case study research strategy. Findings from two case studies demonstrate that such a systematic approach contributes towards more robust decisions for ERP adoption and implementation and indicates that it is acceptable by the case study organisations. The thesis proposes, assesses and presents a novel model for ERP adoption and implementation in SSOs and contributes to the body of knowledge by extending the literature.King Abdulaziz University and Saudi Arabia Cultural Burea

Strategic Energy Technology Plan Study on Energy Education and Training in Europe

This document contains the collection of Assessment Reports from the Expert Working Groups of the Strategic Energy Technology Plan European Energy Education and Training Task Force. It provides background information supporting the findings and recommendations put forward in the Strategic Energy Technology (SET) Plan Roadmap on Education and Training, which addresses the human resource challenge for the energy research and innovation sector and constitutes an integral part of the SET Plan agenda. The findings put forward in the assessment reports are those of the experts involved in each working group, following a process of consultation on the current situation, ongoing activities in the education and training domain, needs and gaps and recommendations for specific actions regarding their respective technology field.JRC.F.6-Energy Technology Policy Outloo

A type-2 fuzzy logic based goal-driven simulation for optimising field service delivery

This thesis develops an intelligent system capable of incorporating the conditions that drive operational activity while implementing the means to handle unexpected factors to protect business sustainability. This solution aims to optimise field service operations in the utility-based industry, especially within one of the world's leading communications services companies, namely BT (British Telecom), which operates in highly regulated and competitive markets. Notably, the telecommunication sector is an essential driver of economic activity. Consequently, intelligent solutions must incorporate the ability to explain their underlying algorithms that power their final decisions to humans. In this regard, this thesis studies the following research gaps: the lack of integrated solutions that go beyond isolated monolithic architectures, the lack of agile end-to-end frameworks for handling uncertainty while business targets are defined, current solutions that address target-oriented problems do not incorporate explainable methodologies; as a result, limited explainability features result in inapplicability for highly regulated industries, and most tools do not support scalability for real-world scenarios. Hence, the need for an integrated, intelligent solution to address these target-oriented simulation problems. This thesis aims to reduce the gaps mentioned above by exploiting fuzzy logic capabilities such as mimicking human thinking and handling uncertainty. Moreover, this thesis also finds support in the Explainable AI field, particularly in the strategies and characteristics to deploy more transparent intelligent solutions that humans can understand. Hence, these foundations support the thesis to unlock explainability, transparency and interpretability. This thesis develops a series of techniques with the following features: the formalisation of an end-to-end framework that dynamically learns form data, the implementation of a novel fuzzy membership correlation analysis approach to enhance performance, the development of a novel fuzzy logic-based method to evaluate the relevancy of inputs, the modelling of a robust optimisation method for operational sustainability in the telecommunications sector, the design of an agile modelling approach for scalability and consistency, the formalisation of a novel fuzzy-logic system for goal-driven simulation for achieving specific business targets before being implemented in real-life conditions, and a novel simulation environment that incorporates visual tools to enhance interpretability while moving from conventional simulation to a target-oriented model. The proposed tool was developed based on data from BT, reflecting their real-world operational conditions. The data was protected and anonymised in compliance with BT’s sharing of information regulations. The techniques presented in the development of this thesis yield significant improvements aligned to institutional targets. Precisely, as detailed in Section 9.5, the proposed system can model a reduction between 3.78% and 5.36% of footprint carbon emission due to travel times for jobs completion on customer premises for specific geographical areas. The proposed framework allows generating simulation scenarios 13 times faster than conventional approaches. As described in Section 9.6, these improvements contribute to increased productivity and customer satisfaction metrics regarding keeping appointment times, completing orders in the promised timeframe or fixing faults when agreed by an estimated 2.6%. The proposed tool allows to evaluate decisions before acting; as detailed in Section 9.7, this contributes to the ‘promoters’ minus ‘detractors’ across business units measure by an estimated 1%