A memetic algorithm with dynamic population management for an integrated production–distribution problem

International audienceThis paper studies an NP-hard multi-period production–distribution problem to minimize the sum of three costs: production setups, inventories and distribution. This problem is solved by a very recent form of metaheuristic called memetic algorithm with population management (MA∣PM). Contrary to classical two-phase methods (production planning, then distribution planning), the algorithm simultaneously tackles production and distribution decisions. Several versions with different population management strategies are evaluated and compared with a two-phase heuristic and a Greedy Randomized Adaptive Search Procedure (GRASP), on 90 randomly generated instances with 20 periods and 50, 100 or 200 customers. The significant savings obtained compared to the two other methods confirm both the interest of integrating production and distribution decisions and of using the MA∣PM template

An Effective Memetic Algorithm with Population Management for the Split Delivery Vehicle Routing Problem

International audienceThis paper studies the Split Delivery Vehicle Routing problem (SDVRP), a variant of the VRP in which multiple visits to customers are allowed. This NP-hard problem is solved by a recent metaheuristic called Memetic Algorithm with Population Management or MA|PM (Sörensen, 2003). It consists in a genetic algorithm, combined with a local search procedure for intensification and a distance measure to control population diversity. Special moves dedicated to split deliveries are introduced in the local search. This solution approach is evaluated and compared with the tabu search algorithm of Archetti et al. (2006) and with lower bounds designed by Belenguer et al. (2000). Our method outperforms the tabu search both in solution quality and running time. On a set of 49 instances, it improves the best-known solution 32 times. The savings obtained confirm the interest and the power of the MA|PM

A GRASP for a combined production-distribution problem

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Optimisation conjointe de la production et de la distribution

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A memetic algorithm with population management for a production-distribution problem

International audienceThis paper studies an NP-hard multi-period production-distribution problem to minimize the sum of three costs: production setups, inventories and distribution. This problem is solved by a very recent form of metaheuristic called Memetic Algorithm with Population Management (MA|PM). Contrary to classical ***two-phase methods (production planning followed by vehicle routing in each period), the algorithm simultaneously tackles production and distribution decisions. It is compared with a two-phase heuristic and a Greedy Randomized Adaptive Search Procedure (GRASP) on 90 randomly generated instances with 50, 100 or 200 customers and 20 periods. The significant savings obtained compared to the two other methods confirm both the interest of integrating production and distribution decisions and of using the MA|PM template

Fast heuristics for a combined production planning and vehicle routing problem

International audienceThis paper studies a problem of production and distribution of one product on a multi-period horizon. The value of co-ordinating production and distribution planning is investigated. The particular scenario addressed here concerns a plant that manufactures one product that can be stored at the plant or shipped to customers who can also store it. The product is distributed by a limited fleet of vehicles to the customers whose the demands are known for each period of the planning horizon. The objective is to determine, for each period, the amount produced and the delivery trips, in order to minimise the total cost of production and distribution over the whole horizon. Two greedy heuristics followed by two local search procedures are proposed for this difficult problem. The first heuristic or uncoupled approach computes in a classical way a production plan and then a distribution plan. The second one or coupled approach determines the two plans simultaneously. The one product hypothesis is not restricted since our heuristics can be extended to cases of several products if these products can be mixed in the same vehicles without creating resource conflicts in production. These heuristics are tested on randomly generated instances with up to 200 customers and 20 periods: they are all very fast and significant savings are obtained by the coupled approach

Combined optimization of production and distribution

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Coordination de la planification de la production et de la distribution

Cette thèse étudie une problématique d'optimisation dans les chaînes logistiques. Généralement, la planification de la production se traite indépendamment de la distribution. De même, les problèmes de tournées de véhicules supposent connues les quantités à livrer et opèrent sur des plans de production déjà calculés. Ce travail propose une approche d'optimisation coordonnée, dans laquelle le plan de production et les tournées de distribution sont construits simultanément sur plusieurs périodes. Cette approche est surtout intéressante dans les activités ou les coûts de production et de distribution sont du même ordre tel que la production des aliments pour bétail ou des engrais. Deux grandes parties composent ce mémoire, la première traite d'une configuration avec un seul produit. Nous avons élaboré un modèle mathématique représentant le problème. Des heuristiques avec des recherches locales ont été proposées. Des méthodes plus puissantes basées sur la métaheuristique GRASP avec path-relinking et sur les algorithmes mémétiques ont été adaptées par la suite. La seconde configuration du problème comporte plusieurs produits avec des conflits de ressources. Une approche itérative hybride a été élaborée. Elle combine la résolution d'un programme linéaire pour la production et un tabou suivi de procédures de post-optimisation pour la distribution. Toutes les méthodes proposées ont été validées par des tests intensifs. Des économies significatives ont été obtenues par rapport aux méthodes découplées, qui traitent séparément la production et la distributionThis thesis studies an optimisation problematic in supply chain. Usually, production planning is treated without considering the distribution level and handles rarely transportation costs. In the same way, vehicle routing problems suppose that the amounts to be delivered are known and work on a production plan already built. This work presents a coordinated optimisation method in which a production plan and delivery trips are simultaneously built, over several periods. This approach is mostly profitable in sectors where production costs are of the same order as tranportations costs, like livestock feed or fertilizers industry. Two main parts compose this thesis. The first one considers the case with one product. One mathematical model is presented and several heuristics reinforced by local search are developped. More powerful methods based on the GRASP metaheuristic with path-relinking and on memetic algorithms are also elaborated. The second part is devoted to the case with several products and ressources conflicts. A iterative and hybrid approach is developed for this configuration. It is based on the solution of one linear program representing the production planning and on a tabu search metaheuristic with post-optimisation procedures for the distribution level. All the methods presented in this theses for the two cases are validated by an intensive testing. Significant savings are obtained compared to classical decoupled methods, that consider production and distribution separatelyTROYES-SCD-UTT (103872102) / SudocSudocFranceF

Disruption Management for Commercial Airlines: Methods and results for the ROADEF 2009 Challenge

International audienceA disruption management problem for commercial airlines, has been presented by Amadeus for the ROADEF 2008/2009 Challenge, an international competition organized by the French Operational Research and Decision Support Society (ROADEF). This paper presents this industrial large scale optimization problem and underlines its difficulties compared to previously tackled problems in the area. We review the most prominent methods proposed by the candidates and provide the official results and participant ranking. Last, as lessons learned from this experience, we draw guidelines for further research