Des stratégies d'approvisionnement pérennes sont nécessaires pour les gestionnaires de la chaîne d'approvisionnement afin de faire face aux incertitudes d’approvisionnement et de demande. La diminution des niveaux de service et l'augmentation simultanée des coûts de stockage sont les impacts les plus importants de ces incertitudes. Les perturbations peuvent être causées par des discontinuités de l’approvisionnement, de l'instabilité politique, des catastrophes naturelles et des grèves des employés. Elles pourraient avoir un effet important sur la performance de la chaîne d'approvisionnement. Pour faire face à de telles perturbations, les modèles d'optimisation des stocks doivent être adaptés pour couvrir une structure de réseau multi-échelons et envisager des stratégies d'approvisionnement alternatives telles que le transport latéral (lateral transshipment) et plusieurs sources d’approvisionnement. Dans ce travail, une approche de modélisation basée sur des scénarios est proposée pour résoudre un problème d'optimisation multi-échelons des stocks. En prenant en compte la demande stochastique et les incertitudes sur les capacités de production, le modèle minimise le coût opérationnel total (coûts de stockage, de transport et de retard) tout en optimisant la gestion des stocks et les flux des marchandises. Afin de faire face aux incertitudes, plusieurs échantillons de scénarios sont générés par Monte Carlo et les exemples correspondants d'approximation (SAA) des programmes sont résolus pour obtenir une politique de réponse adéquate au système d'inventaire en cas de perturbations. De nombreuses expériences numériques sont menées et les résultats permettent d'acquérir des connaissances sur l'impact des perturbations sur le coût total du réseau et le niveau de service. / Supply Chain Management (SCM) is an important part of most companies and applying the appropriate strategy is essential for managers in competitive industries and markets. In this context, Inventory Management plays a crucial role. Different inventory systems are widely used in practice. However, it is fundamentally difficult to optimize, especially in multi-echelon networks. A key challenge in managing inventory is dealing with uncertainties in supply and demand. The simultaneous decrease of customer service and increase of inventory-related costs are the most significant effects of such uncertainties. To deal with this pattern, supply chain managers need to establish more effective and more flexible sourcing and distribution strategies. In this thesis, a “framework to optimize inventory decisions in multi-echelon distribution networks under supply and demand uncertainty” is proposed. In the first part of the research work, multi-echelon distribution systems, subject to demand uncertainty, are studied. Such distribution systems are one of the most challenging inventory network topologies to analyze. The optimal inventory and sourcing policies for these systems are not yet unknown. We consider a basic type of distribution network with a single family product through a periodic review setting. Based on this property, a two-stage mixed integer programming approach is proposed to find the optimal inventory-related decisions considering the non-stationary demand pattern. The model, which is based on a Distribution Requirements Planning (DRP) approach, minimizes the expected total cost composed of the fixed allocation, inventory holding, procurement, transportation, and back-ordering costs. Alternative inventory optimization models, including the lateral transshipment strategy and multiple sourcing, are thus built, and the corresponding stochastic programs are solved using the sample average approximation method. Several problem instances are generated to validate the applicability of the model and to evaluate the benefit of lateral transshipments and multiple sourcing in reducing the expected total costs of the distribution network. An empirical investigation is also conducted to validate the numerical findings by using the case of a major French retailer’s distribution network. The second part of the research work is focused on the structure of the optimal inventory policy which is investigated under supply disruptions. A two-stage stochastic model is proposed to solve a capacitated multi-echelon inventory optimization problem considering a stochastic demand as well as uncertain throughput capacity and possible inventory losses, due to disruptions. The model minimizes the total cost, composed of fixed allocation cost, inventory holding, transportation and backordering costs by optimizing inventory policy and flow decisions. The inventory is controlled according to a reorder point order-up-to-level (s, S) policy. In order to deal with the uncertainties, several scenario samples are generated by Monte Carlo method. Corresponding sample average approximations programs are solved to obtain the adequate response policy to the inventory system under disruptions. In addition, extensive numerical experiments are conducted. The results enable insights to be gained into the impact of disruptions on the network total cost and service level. In both parts of the research, insights are offered which could be valuable for practitioners. Further research possibilities are also provided.
| Identifer | oai:union.ndltd.org:theses.fr/2018BORD0289 |
| Date | 03 December 2018 |
| Creators | Firoozi, Mehdi |
| Contributors | Bordeaux, Ducq, Yves, Klibi, Walid, Babai, Mohamed Zied |
| Source Sets | Dépôt national des thèses électroniques françaises |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation, Text |
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