Achieving an optimal trade-off between revenue and energy peak within a smart grid environment

We consider an energy provider whose goal is to simultaneously set revenue-maximizing prices and meet a peak load constraint. In our bilevel setting, the provider acts as a leader (upper level) that takes into account a smart grid (lower level) that minimizes the sum of users' disutilities. The latter bases its decisions on the hourly prices set by the leader, as well as the schedule preferences set by the users for each task. Considering both the monopolistic and competitive situations, we illustrate numerically the validity of the approach, which achieves an 'optimal' trade-off between three objectives: revenue, user cost, and peak demand

A biobjective model for resource provisioning in multi-cloud environments with capacity constraints

Private and public clouds are good means for getting on-demand intensive computing resources. In such a context, selecting the most appropriate clouds and virtual machines (VMs) is a complex task. From the user’s point of view, the challenge consists in efficiently managing cloud resources while integrating prices and performance criteria. This paper focuses on the problem of selecting the appropriate clouds and VMs to run bags-of-tasks (BoT): big sets of identical and independent tasks. More precisely, we define new mathematical optimization models to deal with the time of use of each VMs and to jointly integrate the execution makespan and the cost into the objective function through a bi-objective problem. In order to provide trade-off solutions to the problem, we propose a lexicographic approach. In addition, we introduce, in two different ways, capacity constraints or bounds on the number of VMs available in the clouds. A global limit on the number of VMs or resource constraints at each time period can be defined. Computational experiments are performed on a synthetic dataset. Sensitivity analysis highlights the effect of the resource limits on the minimum makespan, the effect of the deadline in the total operation cost, the impact of considering instantaneous capacity constraints instead of a global limit and the trade-off between the cost and the execution makespan

Approche à Deux niveaux pour un Problème de Transport Longue Distance

National audienceLa mise en place d'un système de transport de marchandises longue distance revêt à ce jour une importance toute particulière dans un secteur économique en pleine restructuration. A l'heure actuelle les stratégies d'optimisation se sont concentrées sur la définition de plans de transport minimisant le nombre de véhicules utilisés ou minimisant une distance parcourue. Des approches multi-objectifs considérant simultanément des objectifs écologiques et économiques ont également été proposées . A notre connaissance, il n'existe à ce jour aucune étude portant sur la valorisation de la capacité inutilisée et sa tarification. C'est cette problématique que nous considérons dans ce travail. Pour ce faire nous étendons les approches de "Yield Management" ou "Gestion du Revenu" initiées dans le domaine du transport aérien au transport longue distance de marchandises. Plus précisément nous considérons des approches à deux niveaux permettant de tenir compte de l'interaction hiérarchique entre deux niveaux de décision

Complexity of near-optimal robust versions of multilevel optimization problems

Near-optimality robustness extends multilevel optimization with a limited deviation of a lower level from its optimal solution, anticipated by higher levels. We analyze the complexity of near-optimal robust multilevel problems, where near-optimal robustness is modelled through additional adversarial decision-makers. Near-optimal robust versions of multilevel problems are shown to remain in the same complexity class as the problem without near-optimality robustness under general conditions

A Managerial Analysis of Urban Parcel Delivery: A Lean Business Approach

The improper integration of traditional transportation modes with low emissions vehicles can generate a price war that reduces the service quality, undermining the efficiency and the profitability of parcel delivery operators. This paper aims to provide managerial insights to design a win-win strategy for the co-existence of traditional and green business models. In doing so, we adopt a multi-disciplinary approach that integrates a qualitative analysis through a Lean Business methodology, named GUEST, with a quantitative analysis based on simulation-optimisation techniques. This kind of holistic vision has received little attention in the literature. The first analysis investigates the parcel delivery industry with an emphasis on the main business models involved, their costs and revenues structures, while the quantitative part aims to simulate the system and extract sustainable policies. In particular, results highlight that in deploying mixed-fleet policies, the decision-makers have to focus both on the environmental sustainability that benefits from the adoption of low-emission vehicles, and on the operational feasibility and economic sustainability of the two services. In this direction, the paper suggests some managerial insights concerning the split of the customer demand between traditional and green operators, according to the classes of parcels and geographical areas of the city

Sensitivity analysis of stochastic user equilibrium and its application to delivery services pricing

International audienceIn e-commerce the delivery of products is a crucial part for the success of a e-shop. An efficient delivery system should offer various services and predict customersbehavior. The latter are influenced by the price of a delivery service, but also byits quality (congestion effect induced by customers’ choices) in a higly competitveenvironment. In this study, we introduce a bilevel model to optimize a deliverysystem. At the upper level, the provider control services’ tariffs. At the lower level,users react by choosing their delivery service according to a utility function whichincorporates the provider tariff and the congestion effect. We model the customers’reaction using Stochastic User Equilibrium (SUE). We also present a sensitivityanalysis for the SUE that gives explicit expression of the derivatives of customersdistribution with respect to services’ tariffs. Based on a local search that exploit thederivatives information, a new heuristic algorithm for the bilevel delivery servicespricing problem is developed and compared to others existing approachs

A Bilevel Model for Large-scale Time-and-Level-of-Use Pricing

National audienc