Practical inventory routing: A problem definition and an optimization method

The global objective of this work is to provide practical optimization methods to companies involved in inventory routing problems, taking into account this new type of data. Also, companies are sometimes not able to deal with changing plans every period and would like to adopt regular structures for serving customers

On the use of reference points for the biobjective Inventory Routing Problem

The article presents a study on the biobjective inventory routing problem. Contrary to most previous research, the problem is treated as a true multi-objective optimization problem, with the goal of identifying Pareto-optimal solutions. Due to the hardness of the problem at hand, a reference point based optimization approach is presented and implemented into an optimization and decision support system, which allows for the computation of a true subset of the optimal outcomes. Experimental investigation involving local search metaheuristics are conducted on benchmark data, and numerical results are reported and analyzed

Tabu search for high level synthesis

Tabu Search Vignette on Fred GLover's web pag

Inventory routing and on-line inventory routing file format

This document presents a simple extension of the TSPLIB file format to serve our needs in the Inventory Routing Problem types. Instead of creating a new file format or putting ASCII files online with a simple description, we have chosen to extend the TSPLIB file format

Electronic design: a new field of investigation for large scale optimization

International audienceMobile phones, music players, personal computers, set-top boxes and countless other digital electronics items are part of our daily life. These nomad devices offer more and more functionnality. For example, recent mobile phones allow to communicate, to take pictures, to play video, to listen to the music, to watch TV, to access to the Internet... Thus, the integrated circuit (IC) achieving all these functions become more and more complex. Designers aim at developping these products taking into account two main axes: - shorten the delay to reach the market - reduce the size of these devices For a long time, designers are using a lot of optimization techniques such as Integer Linear Programming, heuristics and metaheuristics. A two year observation of those behaviors has led to the following conclusions, even if the electronic community know these techniques, there is a great need for more formal techniques, more appropriate models and efficient soving approaches especially designed for their specific problems. The talk, after a global introduction will introduce several problems where the help from the metaheuristic is needed and for which collaborations are necessary. This talk will be the starting point of the creation of a network for long term collaborations

Métaheuristiques pour l'allocation de mémoire dans les systèmes embarqués

National audienceLa gestion de la mémoire cache a un impact significatif sur les performances et sur la consommation énergétique des systèmes embarqués. Cet article traite de l'allocation de mémoire des structures de données d'une application à la mémoire cache de manière à optimiser les performances du système. Les concepteurs de circuits souhaitent trouver un compromis entre le coût de l'architecture (le nombre de bancs mémoire à embarquer) et la consommation électrique. Le problème abordé consiste à allouer un banc mémoire à toute structure de données de manière à minimiser les conflits d'accès aux données. Le modèle proposé pour ce problème est le k-weighted graph coloring problem. Une formulation par PLNE et deux métaheuristiques basées respectivement sur une recherche taboue et sur un algorithme hybride à base de population sont comparées sur un ensemble d'instances. Les résultats obtenus sont encourageants et suggèrent que l'utilisation de méthodes issues de la coloration de graphes est une piste prometteuse pour l'allocation de mémoire dans les systèmes embarqués

A bi-objective stochastic approach for stochastic CARP

The Capacitated Arc Routing Problem (CARP) occurs in applications like urban waste collection or winter gritting. It is usually defined in literature on an undirected graph G = (V, E) , with a set V of n nodes and a set E of m edges. A fleet of identical vehicles of capacity Q is based at a depot node. Each edge i has a cost (length) ci and a demand qi (e.g. an amount of waste), and it may be traversed any number of times. The edges with non-zero demands or tasks require service by a vehicle. The goal is to determine a set of vehicle trips (routes) of minimum total cost, such that each trip starts and ends at the depot, each task is serviced by one single trip, and the total demand handled by any vehicle does not exceed Q . To the best of our knowledge the best published method is a memetic algorithm first introduced in 2001. This article provides a new extension of the NSGA II (Non-dominated Sorting Genetic Algorithm) template to comply with the stochastic sight of the CARP. The main contribution is: - to introduce mathematical expression to evaluate both cost and duration of the longest trip and also standard deviation of these two criteria. - to use a NGA-II template to optimize simultaneously the cost and the duration of the longest trip including standard deviation. The numerical experiments managed on the thee well-known benchmark sets of DeArmon, Belenguer and Benavent and Eglese, prove it is possible to obtain robust solutions in four simultaneous criteria in rather short computation times

Non-linear great deluge with reinforcement learning for university course timetabling

This paper describes a non-linear great deluge hyper-heuristic incorporating a reinforcement learning mechanism for the selection of low-level heuristics and a non-linear great deluge acceptance criterion. The proposed hyper-heuristic deals with complete solutions, i.e. it is a solution improvement approach not a constructive one. Two types of reinforcement learning are investigated: learning with static memory length and learning with dynamic memory length. The performance of the proposed algorithm is assessed using eleven test instances of the university course timetabling problem. The experimental results show that the non-linear great deluge hyper-heuristic performs better when using static memory than when using dynamic memory. Furthermore, the algorithm with static memory produced new best results for ?ve of the test instances while the algorithm with dynamic memory produced four best results compared to the best known results from the literature