The asset replacement problem state of the art.

This book chapter outlines the different modelling approaches for realising sustainable operations of asset replacement and studying the impact of the economic life, the repair-cost limit and comprehensive cost minimisation models. In particular it analyses in detail the parallel replacement models and suggests a new model that addresses some of the issues not yet solved in this area. Finally a discussion about the limitations of the current models from a theoretical and applied perspective is proposed and identifies some of the challenges still faced by academics and practitioners working on this topic

A risk management system for sustainable fleet replacement.

This article analyzes the fleet management problem faced by a firm when deciding which vehicles to add to its fleet. Such a decision depends not only on the expected mileage and tasks to be assigned to the vehicle but also on the evolution of fuel and CO2 emission prices and on fuel efficiency. This article contributes to the literature on fleet replacement and sustainable operations by proposing a general decision support system for the fleet replacement problem using stochastic programming and conditional value at risk (CVaR) to account for uncertainty in the decision process. The article analyzes how the CVaR associated with different types of vehicle is affected by the parameters in the model by reporting on the results of a real-world case study

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 However the smooth adoption of such a system is superseded by a challenge for exposing the human understandable proof of the machine reasoning.With that in mind, this paper introduces an explainable recommender system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

Recursive expected conditional value at risk in the fleet renewal problem with alternative fuel vehicles.

We study the fleet portfolio management problem faced by a firm deciding which alternative fuel vehicles (AFVs) to choose for its fleet to minimise the weighted average of cost and risk, in a stochastic multi-period setting. We consider different types of technology and vehicles with heterogeneous capabilities. We propose a new time consistent recursive risk measure, the Recursive Expected Conditional Value at Risk (RECVaR), which we prove to be coherent. We then solve the problem for a large UK based company, reporting how the optimal policies are affected by risk aversion and by the clustering for each type of vehicle

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

Model and Combinatorial Optimization Methods for Tactical Planning in Closed-Loop Supply Chains

International audienc

Strategies for handling uncertainty in tactical distribution planning - an empirical study

International audienc

A Metaheuristic for Tactical Inventory Planning in Closed-Loop Supply Chains

International audienc

Tactical Supply Chain Distribution Planning In The Telecommunications Service Industry

Supply chains are ubiquitous across industries and a considerable e ort has been invested in supply chain management techniques over the last two decades. In equipment-intensive service industries, it often involves repair operations. In this context, tactical inventory planning is concerned with optimally planning supplies and repairs based on demand forecasts and in face of con icting business objectives. It is based on a case study in the telecommunications sector where large quantities and varieties of spare parts are required for service maintenance and repair tasks at customer premises or company exchanges. Speci cally, we consider a multi-echelon spare parts supply chain and tackle the problem of determining an optimal stock distribution plan given a demand forecast. We propose a mixed integer programming and a metaheuristic approach to this problem. The model is open to a variety of network topologies, site functions and transfer policies. It also accommodates multiple objectives by the means of a weighted cost function. We report experiments on pseudo-random instances designed to evaluate plan quality and impact of cost weightings. In particular, we show how appropriate weightings allow to emulate common planning strategies (e.g., just-in-time replenishment, minimal repair). We also assess plan quality and system performance against di erent classes of pseudo-random instances featuring different volume and distribution of stock and demand

Service scheduling to minimise the risk of missing appointments

© 2017 IEEE. This paper introduces the risk minimisation objective in the Stochastic Vehicle Routing Problem (SVRP). In the studied variant of SVRP, technicians drive to customer sites to provide service. The service times and travel times are stochastic, and a time window is required for the start of the service for each customer. Most previous research uses a chance-constrained approach to the problem. Some consider the probability of journey duration exceeding the threshold of the driver's workload while others set restrictions on the probability of individual time window constraints being violated. Their objectives are related to traditional routing costs whilst a different approach was taken in this paper. The risk of missing a task is defined as the probability that the technician assigned to the task arrives at the customer site later than the time window. The problem studied in this paper is to generate a schedule that minimises the maximum risk and sum of risks of the tasks. The duration of each task may be considered as following a known normal distribution. However the distribution of the start time of the service at a customer site will not be normally distributed due to time window constraints. Therefore a multiple integral expression of the risk was derived, and this expression works whether task distribution is normal or not. Additionally a deterministic heuristic searching method was applied to solve the problem. Experiments are carried out to test the method. Results of this work have been applied to an industrial case of SVRP where field engineering individuals drive to customer sites to provide time-constrained services. This original approach allows organisations to pay more attention to increasing customer satisfaction and become more competitive in the market