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

Model and Combinatorial Optimization Methods for Tactical 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

A Combinatorial Optimisation Approach For Closed-Loop Supply Chains Inventory Planning With Deterministic Demands

Supply chains in equipment-intensive service industries often involve repair operations. In this context, tactical inventory planning is concerned with optimally planning supplies and repairs based on demand forecasts and in the face of conflicting business objectives. This paper considers closed-loop supply chains and proposes a mixed-integer programming model 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 implement common planning strategies (e.g., just-in-time replenishment, minimal repair

iSee: intelligent sharing of explanation experiences.

The right to an explanation of the decision reached by a machine learning (ML) model is now an EU regulation. However, different system stakeholders may have different background knowledge, competencies and goals, thus requiring different kinds of explanations. There is a growing armoury of XAI methods, interpreting ML models and explaining their predictions, recommendations and diagnoses. We refer to these collectively as "explanation strategies". As these explanation strategies mature, practitioners gain experience in understanding which strategies to deploy in different circumstances. What is lacking, and what the iSee project will address, is the science and technology for capturing, sharing and re-using explanation strategies based on similar user experiences, along with a much-needed route to explainable AI (XAI) compliance. Our vision is to improve every user's experience of AI, by harnessing experiences of best practice in XAI by providing an interactive environment where personalised explanation experiences are accessible to everyone. Video Link: https://youtu.be/81O6-q_yx0

Fuzzy Logic Based Personalized Task Recommendation System for Field Services

Within service providing industries, field service resources often follow a schedule that is produced centrally by a scheduling system. The main objective of such systems is to fully utilize the resources by increasing the number of completed tasks while reducing operational costs. Existing off the shelf scheduling systems started to incorporate the resources’ preferences and experience which although being implicit knowledge, are recognized as important drivers for service delivery efficiency. One of the scheduling systems that currently operates at BT allocates tasks interactively with a subset of empowered engineers. These engineers can select the tasks they think relevant for them to address along the working period. In this paper, we propose a fuzzy logic based personalized recommendation system that recommends tasks to the engineers based on their history of completed tasks. By analyzing the past data, we observe that the engineers indeed have distinguishable preferences that can be identified and exploited using the proposed system. We introduce a new evaluation measure for evaluating the proposed recommendations. Experiments show that the recommended tasks have up to 100% similarity to the previous tasks chosen by the engineers. Personalized recommendation systems for field service engineers have the potential to help understand how the field engineers react as the workstack evolves and new tasks come in, and to ultimately improve the robustness of service delivery

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

The Asset Replacement Problem: State of the Art

In this book chapter we summarize how the asset replacement problem has evolved over time. We provide a broad view of different modeling approaches, including the economic life, the repair cost limit, comprehensive cost minimization models, and we analyze in detail the parallel replacement models. We suggest a new model for parallel replacement that addresses some of the issues not yet solved in this area. Finally, we discuss the limitations of the current models from a theoretical and applied perspective and identify some of the challenges still faced by academics and practitioners working on this topi

Fuzzy Logic Based Personalized Task Recommendation System for Field Services

Within service providing industries, field service resources often follow a schedule that is produced centrally by a scheduling system. The main objective of such systems is to fully utilize the resources by increasing the number of completed tasks while reducing operational costs. Existing off the shelf scheduling systems started to incorporate the resources’ preferences and experience which although being implicit knowledge, are recognized as important drivers for service delivery efficiency. One of the scheduling systems that currently operates at BT allocates tasks interactively with a subset of empowered engineers. These engineers can select the tasks they think relevant for them to address along the working period. In this paper, we propose a fuzzy logic based personalized recommendation system that recommends tasks to the engineers based on their history of completed tasks. By analyzing the past data, we observe that the engineers indeed have distinguishable preferences that can be identified and exploited using the proposed system. We introduce a new evaluation measure for evaluating the proposed recommendations. Experiments show that the recommended tasks have up to 100% similarity to the previous tasks chosen by the engineers. Personalized recommendation systems for field service engineers have the potential to help understand how the field engineers react as the workstack evolves and new tasks come in, and to ultimately improve the robustness of service delivery