PLANIFICATION DES LIVRAISON JOINTES DE DIFFERENTS PRODUITS A DIFFERENTS SITES

Colloque avec actes et comité de lecture. internationale.International audienceLe problème de livraisons jointes de produits (JDP) consiste à planifier les livraisons de différents produits à différents sites de consommation ou de distribution en traitant les problèmes de groupement, de livraison et de stockage. Il s’agit de construire des tournées de livraison sur un horizon de planification, en satisfaisant les demandes et en minimisant le coût total de commande, de livraison et de stockage. Les coûts fixes de commande portent d’une part sur le lancement d’une tournée, d’autre part sur chaque couple (produit, site) présent ou non dans la tournée. Les taux de demandes étant supposées fixes et connus, le problème en horizon infini admet une solution périodique, le plan de livraison optimal sur une période-type pouvant se répéter indéfiniment. Dans notre approche, le problème est formulé en temps discret et nous choisissons comme période-type commune de cyclicité un multiple de la période élémentaire, et cette période-type sert d’horizon de planification. Ainsi, les livraisons restent périodiques à travers la répétition de l’horizon de planification, mais les livraisons pendant l’horizon de planification ne sont pas contraintes à être périodiques. Les résultats numériques montrent en particulier la supériorité de cette approche sur une solution cyclique pour chaque couple (produit, site)

Approximation algorithms and hardness results for the joint replenishment Problepm with constant demands

19th Annual European Symposium, Saarbrücken, Germany, September 5-9, 2011. ProceedingsIn the Joint Replenishment Problem (JRP), the goal is to coordinate the replenishments of a collection of goods over time so that continuous demands are satisfied with minimum overall ordering and holding costs. We consider the case when demand rates are constant. Our main contribution is the first hardness result for any variant of JRP with constant demands. When replenishments per commodity are required to be periodic and the time horizon is infinite (which corresponds to the so-called general integer model with correction factor), we show that finding an optimal replenishment policy is at least as hard as integer factorization. This result provides the first theoretical evidence that the JRP with constant demands may have no polynomial-time algorithm and that relaxations and heuristics are called for. We then show that a simple modification of an algorithm by Wildeman et al. (1997) for the JRP gives a fully polynomial-time approximation scheme for the general integer model (without correction factor). We also extend their algorithm to the finite horizon case, achieving an approximation guarantee asymptotically equal to √9/8

Decision Support System for Inventory Control of Raw Material

PT Suwarni Agro Mandiri Plant Pariaman is a company which produces fertilizer. This company has a problem related to raw material inventory. The inventory can be overstock or stock out. It is due to their working which is not guided by an information system. Therefore, this research proposes a decision support system for controlling the inventory of the raw material. The system uses Material Requirement Planning (MRP) approach and is designed in three sub-systems. They are OLTP database for managing the daily activities, MRP for determining the lot size and the raw material ordering time, and OLAP with data warehouse for analyzing the raw material data. Keywords-inventory; inventory control; online analytical processing; online transaction processin

an evolutionary approach for the offsetting inventory cycle problem

AbstractIn inventory management, a fundamental issue is the rational use of required space. Among the numerous techniques adopted, an important role is played by the determination of the replenishment cycle offsetting which minimizes the warehouse space within a considered time horizon. The NP-completeness of the Offsetting Inventory Cycle Problem (OICP) has led the researchers towards the development and the comparison of specific heuristics. We propose and implement a genetic algorithm for the OICP, whose effectiveness is validated by comparing its solutions with those found by a mixed integer programming model. The algorithm, tested on realistic instances, shows a high reduction of the maximum space and a more regular warehouse saturation with negligible increase of the total cost. This paper, unlike other papers currently available in literature, provides instances data and results necessary for reproducibility, aiming to become a benchmark for future comparisons with other OICP algorithms

Sustainability analysis in integrated inventory control and transportation systems

Due to the importance of costs as well as environmental effects of logistical activities throughout supply chains, such as inventory holding, freight transportation, and warehousing activities, this dissertation models and analyzes four integrated inventory control and transportation problems that account for economic and environmental aspects of a supply chain agents related decisions. The first model presents an integrated inventory control and transportation problem in a single item deterministic demand setting. A supply chain agents inventory control and transportation mode selection problem is solved under carbon cap, carbon cap and trade, carbon cap and offset, and carbon tax regulations. The second model focuses on an integrated inventory control and transportation problem in a single item stochastic demand setting integrating environmental objectives into a continuous review inventory control system with considerations of two different transportation modes. The third model studies an integrated inventory control and transportation problem in a multi-item deterministic demand setting, in which, a decision making method is developed considering the economic and environmental objectives. In the fourth model, a multi-item stochastic demand consolidation policy is analyzed with the consideration of heterogeneous freight trucks for transportation. It is shown that the consolidation policy suggested can result in substantial economic as well as environmental benefits for the supply chain agents --Abstract, page iii

Assessment of joint inventory replenishment: a cooperative games approach

This research deals with the design of a logistics strategy with a collaborative approach between non-competing companies, who through joint coordination of the replenishment of their inventories reduce their costs thanks to the exploitation of economies of scale. The collaboration scope includes sharing logistic resources with limited capacities; transport units, warehouses, and management processes. These elements conform a novel extension of the Joint Replenishment Problem (JRP) named the Schochastic Collaborative Joint replenishment Problem (S-CJRP). The introduction of this model helps to increase practical elements into the inventory replenishment problem and to assess to what extent collaboration in inventory replenishment and logistics resources sharing might reduce the inventory costs. Overall, results showed that the proposed model could be a viable alternative to reduce logistics costs and demonstrated how the model can be a financially preferred alternative than individual investments to leverage resources capacity expansions. Furthermore, for a practical instance, the work shows the potential of JRP models to help decision-makers to better understand the impacts of fleet renewal and inventory replenishment decisions over the cost and CO2 emissions.DoctoradoDoctor en Ingeniería Industria

Stochastic joint replenishment problems: periodic review policies

Operations Managers of manufacturing systems, distribution systems, and supply chains address lot sizing and scheduling problems as part of their duties. These problems are concerned with decisions related to the size of orders and their schedule. In general, products share or compete for common resources and thus require coordination of their replenishment decisions whether replenishment involves manufacturing operations or not. This research is concerned with joint replenishment problems (JRPs) which are part of multi-item lot sizing and scheduling problems in manufacturing and distribution systems in single echelon/stage systems. The principal purpose of this research is to develop three new periodic review policies for stochastic joint replenishment problem. It also highlights the lack of research on joint replenishment problems with different demand classes (DSJRP). Therefore, periodic review policy is developed for this problem where the inventory system faces different demand classes that are deterministic demand and stochastic demand. Heuristic Algorithms have been developed to obtain (near) optimal parameters for the three policies as well as a heuristic algorithm has been developed for DSJRP. Numerical tests against literature benchmarks have been presented

Diseño de un modelo de reabastecimiento multi-ítem basada en colaboración, como estrategia para reducir costos de importación de cargas de comercio exterior

La presente investigación, tuvo como propósito el diseño de un modelo de inventarios basado en la coordinación de reposiciones de múltiples ítems ofrecidos por diferentes empresas, como estrategia de colaboración donde se comparten los costos fijos asociados al proceso de reabastecimiento entre los ítems agregados, de forma que el costo logístico se vea reducido. Para llevar a cabo este objetivo se resolvieron dos problemas centrales: (1) Determinar la frecuencia de reposición de cada ítem de forma costo – efectiva, y (2) realizar la asignación de los beneficios obtenidos, garantizando estabilidad en las asociaciones de empresas generadas. El primer problema fue resuelto mediante la ampliación y adaptación de una heurística basada en algoritmos genéticos, mientras que en el segundo se emplearon técnicas de la teoría de juegos colaborativos. La validación de la estrategia logística se hizo de dos formas; se desarrolló un caso de estudio en 4 empresas, donde además de considerar el estado actual, se simularon posibles cambios tanto favorables como desfavorables del mercado, a través de la variación de los costos y la demanda. En todos los casos el modelo colaborativo demostró resultados positivos. En una segunda prueba se simularon 1.000 problemas con diferentes tamaños, considerando 10, 20, 30 y 50 familias de productos. Los resultados generales resultaron promisorios; los ahorros encontrados están en el orden del 28.7% en contraste con el modelo regular o de operación individual.MaestríaMagister en Ingeniería Industria