Laboratoire de Génie Informatique et d’Automatique de l’Artois

Ph.D. thesis of Khouloud DORGHAM

Storage and distribution schemes under uncertainties: application to a central store optimization within a Territory Hospital Group

Starting date: 1 November 2019
Funding: Dual funding shared by the Artois University and the french DGOS (Direction Generale de l'Offre de Soins)
Keywords: optimization, multi-level distribution chain, distribution tour, uncertainties

In recent years, several logistical pooling strategies and models have been developed in the literature to solve the supply chain network design problem and achieve economies of scale. Therefore, in order to develop the health care system in France, the hospitals have met, since 2016, to form a Territorial Hospital Groups (THG) that aim at increasing cooperation between public hospitals and pooling the different services between institutions. The objective of this study is to enable the establishments of a THG to rationalize and optimize the storage and distribution of products in the stores of this grouping. Our work follows a previous study carried out in 2017/18 on the development of a simulation tool to measure the economic impacts of pooling and distribution scenario of products sub-families. In this thesis work we deal with the problem at its strategic level to decide which sub-families of products to share with which institutions? on which storage sites? and tactics level to ensure their distribution (with what frequency? for how much?) within the functional units of each institution. The aim is to minimise the various economic costs (storage costs, upstream and downstream transport costs, etc.). Initially, an optimization model should be developed to allow the location of storage stores and the choice of sub-families to be shared within a THG. In a second study, we will be interested in the supply and distribution schemes to be set up between stores and various suppliers and then between stores and platforms of each institution. A validation of the developed models will be done on instances of different sizes. We will extend these models to take into account the uncertainties on the data (demand, cost, time ...) with a possibilistic approach.

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