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

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

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

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

Monitoring Guidance for Underwater Noise in European Seas- Part II: Monitoring Guidance Specifications

This document has been prepared by the Technical Subgroup on Underwater Noise and other forms of Energy (TSG Noise), established in 2010 by the Marine Directors, i.e. the representatives of directorates or units in European Union Member States, Acceding Countries, Candidate Countries and EFTA Member States dealing with or responsible for marine issues. In December 2011, the Marine Directors requested the TSG Noise to provide monitoring guidance that could be used by Member States in establishing monitoring schemes to meet the needs of the Marine Strategy Framework Directive indicators for underwater noise in their marine waters. This document presents the recommendations and information needed to commence the monitoring required for underwater noise.JRC.H.1-Water Resource

Monitoring Guidance for Underwater Noise in European Seas - Part I: Executive Summary

This document has been prepared by the Technical Subgroup on Underwater Noise and other forms of Energy (TSG Noise), established in 2010 by the Marine Directors, i.e. the representatives of directorates or units in European Union Member States, Acceding Countries, Candidate Countries and EFTA Member States dealing with or responsible for marine issues. In December 2011, the Marine Directors requested the TSG Noise to provide monitoring guidance that could be used by Member States in establishing monitoring schemes to meet the needs of the Marine Strategy Framework Directive indicators for underwater noise in their marine waters. This document presents the key conclusions and recommendations that support the implementation of the practical guidance to commence the monitoring required for underwater noise.JRC.H.1-Water Resource

Managing the Effects of Noise From Ship Traffic, Seismic Surveying and Construction on Marine Mammals in Antarctica

© 2019 Erbe, Dähne, Gordon, Herata, Houser, Koschinski, Leaper, McCauley, Miller, Müller, Murray, Oswald, Scholik-Schlomer, Schuster, Van Opzeeland and Janik. The Protocol on Environmental Protection of the Antarctic Treaty stipulates that the protection of the Antarctic environment and associated ecosystems be fundamentally considered in the planning and conducting of all activities in the Antarctic Treaty area. One of the key pollutants created by human activities in the Antarctic is noise, which is primarily caused by ship traffic (from tourism, fisheries, and research), but also by geophysical research (e.g., seismic surveys) and by research station support activities (including construction). Arguably, amongst the species most vulnerable to noise are marine mammals since they specialize in using sound for communication, navigation and foraging, and therefore have evolved the highest auditory sensitivity among marine organisms. Reported effects of noise on marine mammals in lower-latitude oceans include stress, behavioral changes such as avoidance, auditory masking, hearing threshold shifts, and—in extreme cases—death. Eight mysticete species, 10 odontocete species, and six pinniped species occur south of 60°S (i.e., in the Southern or Antarctic Ocean). For many of these, the Southern Ocean is a key area for foraging and reproduction. Yet, little is known about how these species are affected by noise. We review the current prevalence of anthropogenic noise and the distribution of marine mammals in the Southern Ocean, and the current research gaps that prevent us from accurately assessing noise impacts on Antarctic marine mammals. A questionnaire given to 29 international experts on marine mammals revealed a variety of research needs. Those that received the highest rankings were (1) improved data on abundance and distribution of Antarctic marine mammals, (2) hearing data for Antarctic marine mammals, in particular a mysticete audiogram, and (3) an assessment of the effectiveness of various noise mitigation options. The management need with the highest score was a refinement of noise exposure criteria. Environmental evaluations are a requirement before conducting activities in the Antarctic. Because of a lack of scientific data on impacts, requirements and noise thresholds often vary between countries that conduct these evaluations, leading to different standards across countries. Addressing the identified research needs will help to implement informed and reasonable thresholds for noise production in the Antarctic and help to protect the Antarctic environment

Inferring Market Structure from Customer Response to Competing and Complementary Products

We consider customer influences on market structure, arguing that market structure should explain the extent to which any given set of market offerings are substitutes or complements. We describe recent additions to the market structure analysis literature and identify promising directions for new research in market structure analysis. Impressive advances in data collection, statistical methodology and information technology provide unique opportunities for researchers to build market structure tools that can assist “real-time” marketing decision-making.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46981/1/11002_2004_Article_5088105.pd