FieldPlan: tactical field force planning in BT

In a highly competitive market, BT1 faces tough challenges as a service provider for telecommunication solutions. A proactive approach to the management of its resources is absolutely mandatory for its success. In this paper, an AI-based planning system for the management of parts of BT’s field force is presented. FieldPlan provides resource managers with full visibility of supply and demand, offers extensive what-if analysis capabilities and thus supports an effective decision making process.IFIP International Conference on Artificial Intelligence in Theory and Practice - Industrial Applications of AIRed de Universidades con Carreras en Informática (RedUNCI

Developing a catalogue of explainability methods to support expert and non-expert users.

Organisations face growing legal requirements and ethical responsibilities to ensure that decisions made by their intelligent systems are explainable. However, provisioning of an explanation is often application dependent, causing an extended design phase and delayed deployment. In this paper we present an explainability framework formed of a catalogue of explanation methods, allowing integration to a range of projects within a telecommunications organisation. These methods are split into low-level explanations, high-level explanations and co-created explanations. We motivate and evaluate this framework using the specific case-study of explaining the conclusions of field engineering experts to non-technical planning staff. Feedback from an iterative co-creation process and a qualitative evaluation is indicative that this is a valuable development tool for use in future company projects

Explainability through transparency and user control: a case-based recommender for engineering workers.

Within the service providing industries, field engineers can struggle to access tasks which are suited to their individual skills and experience. There is potential for a recommender system to improve access to information while being on site. However the smooth adoption of such a system is superseded by a challenge for exposing the human understandable proof of the machine reasoning.With that in mind, this paper introduces an explainable recommender system to facilitate transparent retrieval of task information for field engineers in the context of service delivery. The presented software adheres to the five goals of an explainable intelligent system and incorporates elements of both Case-Based Reasoning and heuristic techniques to develop a recommendation ranking of tasks. In addition we evaluate methods of building justifiable representations for similarity-based return on a classification task developed from engineers' notes. Our conclusion highlights the trade-off between performance and explainability

Facility location problem and permutation flow shop scheduling problem: a linked optimisation problem.

There is a growing literature spanning several research communities that studies multiple optimisation problems whose solutions interact, thereby leading researchers to consider suitable approaches to joint solution. Real-world problems, like supply chain, are systems characterised by such interactions. A decision made at one point of the supply chain could have significant consequence that ripples through linked production and transportation systems. Such interactions would require complex algorithmic designs. This paper, therefore, investigates the linkages between a facility location and permutation flow shop scheduling problems of a distributed manufacturing system with identical factory (FLPPFSP). We formulate a novel mathematical model from a linked optimisation perspective with objectives of minimising facility cost and makespan. We present three algorithmic approaches in tackling FLPPFSP; Non-dominated Sorting Genetic Algorithm for Linked Problem (NSGALP), Multi-Criteria Ranking Genetic Algorithm for Linked Problem (MCRGALP), and Sequential approach. To understand FLPPFSP linkages, we conduct a pre-assessment by randomly generating 10000 solution pairs on all combined problem instances and compute their respective correlation coefficients. Finally, we conduct experiments to compare results obtained by the selected algorithmic methods on 620 combined problem instances. Empirical results demonstrate that NSGALP outperforms the other two methods based on relative hypervolume, hypervolume and epsilon metrics

A Hybrid Metaheuristic Approach to a Real World Employee Scheduling Problem

Employee scheduling problems are of critical importance to large businesses. These problems are hard to solve due to large numbers of conflicting constraints. While many approaches address a subset of these constraints, there is no single approach for simultaneously addressing all of them. We hybridise 'Evolutionary Ruin & Stochastic Recreate' and 'Variable Neighbourhood Search' metaheuristics to solve a real world instance of the employee scheduling problem to near optimality. We compare this with Simulated Annealing, exploring the algorithm configuration space using the irace software package to ensure fair comparison. The hybrid algorithm generates schedules that reduce unmet demand by over 28% compared to the baseline. All data used, where possible, is either directly from the real world engineer scheduling operation of around 25,000 employees , or synthesised from a related distribution where data is unavailable

Enabling Field Force Operational Sustainability: A Big Bang-Big Crunch Type-2 Fuzzy Logic System for Goal-Driven Simulation

Business operational sustainability must allow creating economic value, building healthy ecosystems and developing strong communities. Hence, there is a need to develop solutions which can safeguard companies' business sustainability. Various solutions could have different costs and deliver different benefits. Therefore, there is a need to evaluate these solutions before being implemented. In reality, companies require achieving certain targets according to their plans and strategies. Goal-Driven Simulation (GDS) is an approach that allows evaluating solutions before implementing them in real-life while focusing on achieving desired targets. This paper presents a GDS based on interval type-2 Fuzzy Logic System (IT2FLS) optimized by the big bang-big crunch (BU-BC) algorithm with application to field force allocation within the telecommunications sector. The obtained results show the suitability of the proposed approach to model unexpected factors to protect the business sustainability in the telecommunications industry field force allocation domain

The Sarmatian/Pannonian boundary at the western margin of the Vienna Basin (City of Vienna, Austria)

Abstract Sarmatian and Pannonian cores, drilled at the western margin of the Vienna Basin in the City of Vienna, reveal a complex succession of marine and lacustrine depositional environments during the middle to late Miocene transition. Two Sarmatian and two Pannonian transgressive-regressive sequences were studied in detail. Identical successions of benthic faunal assemblages and similar patterns in magnetic susceptibility logs characterise these sequences. This allows a correlation of the boreholes over a distance of ~3.5 km across one of the major marginal faults of the Vienna Basin. Biostratigraphic data, combined with rough estimates of sedimentation rates, reveal large gaps between these sequences, suggesting that only major transgressions reached this marginal area. In particular, during the Sarmatian-Pannonian transition, the basin margin completely emerged and turned into a terrestrial setting for at least 600 ka

Decision support system for green real-life field scheduling problems

© Springer International Publishing AG 2017. A decision support system is designed in this paper for supporting the adoption of green logistics within scheduling problems, and applied to real-life services cases. In comparison to other green logistics models, this system deploys time-varying travel speeds instead of a constant speed, which is important for calculating the CO 2 emission accurately. This system adopts widely used instantaneous emission models in literature which can predict second-by-second emissions. The factors influencing emissions in these models are vehicle types, vehicle load and traffic conditions. As vehicle types play an important role in computing the amount of emissions, engineers’ vehicles’ number plates are mapped to specified emission formulas. This feature currently is not offered by any commercial software. To visualise the emissions of a planned route, a Heat Map view is proposed. Furthermore, the differences between minimising CO 2 emission compared to minimising travel time are discussed under different scenarios. The field scheduling problem is formulated as a vehicle routing and scheduling problem, which considers CO 2 emissions in the objective function, heterogeneous fleet, time window constraints and skill matching constraints, different from the traditional time-dependent VSRP formulation. In the scheduler, this problem is solved by metaheuristic methods. Three different metaheuristics are compared. They are Tabu search algorithms with random neighbourhood generators and two variants of Variable Neighbourhood search algorithms: variable neighbourhood descent (VND) and reduced variable neighbourhood search (RVNS). Results suggest that RVNS is a good trade-off between solution qualities and computational time for industrial application

FieldPlan: tactical field force planning in BT

In a highly competitive market, BT1 faces tough challenges as a service provider for telecommunication solutions. A proactive approach to the management of its resources is absolutely mandatory for its success. In this paper, an AI-based planning system for the management of parts of BT’s field force is presented. FieldPlan provides resource managers with full visibility of supply and demand, offers extensive what-if analysis capabilities and thus supports an effective decision making process.IFIP International Conference on Artificial Intelligence in Theory and Practice - Industrial Applications of AIRed de Universidades con Carreras en Informática (RedUNCI

Adaptable Closed-Domain Question Answering Using Contextualized CNN-Attention Models and Question Expansion

In closed-domain Question Answering (QA), the goal is to retrieve answers to questions within a specific domain. The main challenge of closed-domain QA is to develop a model that only requires small datasets for training since large-scale corpora may not be available. One approach is a flexible QA model that can adapt to different closed domains and train on their corpora. In this paper, we present a novel versatile reading comprehension style approach for closed-domain QA (called CA-AcdQA). The approach is based on pre-trained contextualized language models, Convolutional Neural Network (CNN), and a self-attention mechanism. The model captures the relevance between the question and context sentences at different levels of granularity by exploring the dependencies between the features extracted by the CNN. Moreover, we include candidate answer identification and question expansion techniques for context reduction and rewriting ambiguous questions. The model can be tuned to different domains with a small training dataset for sentence-level QA. The approach is tested on four publicly-available closed-domain QA datasets: Tesla (person), California (region), EU-law (system), and COVID-QA (biomedical) against nine other QA approaches. Results show that the ALBERT model variant outperforms all approaches on all datasets with a significant increase in Exact Match and F1 score. Furthermore, for the Covid-19 QA in which the text is complicated and specialized, the model is improved considerably with additional biomedical training resources (an F1 increase of 15.9 over the next highest baseline)