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UID:0-294@lisn.upsaclay.fr
DTSTART;TZID=Europe/Paris:20250912T143000
DTEND;TZID=Europe/Paris:20250912T143000
DTSTAMP:20250903T165841Z
URL:https://www.lisn.upsaclay.fr/evenements/mathematical-programming-appro
 aches-for-lot-sizing-under-intermittent-renewable-energy/
SUMMARY:Mathematical programming approaches for lot-sizing under intermitte
 nt renewable energy
DESCRIPTION:Abstract\nThe industrial sector is currently the second largest
  global carbon emitter\, it should urgently and drastically reduce its car
 bon emissions to meet the 2050 carbon neutrality target set by the COP-21 
 Paris Agreement. This global context translates into a huge pressure on in
 dustrial companies to lower the carbon footprint of their activities. At t
 he same time\, energy cost has become a primary concern for them due to th
 e sharp increase in the price of gas and grid electricity. At a company le
 vel\, a possible way of simultaneously addressing these two challenges con
 sists in building a decentralized energy system based on renewable sources
  (e.g. wind\, sun) directly on the production site and to use the renewabl
 e electricity generated on-site to power\, at least partially\, the indust
 rial processes. This PhD thesis thus considers an industrial production si
 te partially powered by a decentralized energy system based on intermitten
 t renewable energy sources\, and seeks to tackle the challenges posed by t
 he management and planning of such a site at the operational short-term le
 vel. More precisely\, we focus on how to jointly plan the industrial produ
 ction and the energy supply of this site\, over an horizon spanning the ne
 xt few days\, so as to minimize the total production and energy cost. We f
 irst consider a deterministic setting and investigate how to model this co
 mbinatorial optimization problem as a mixed-integer linear program. We pro
 pose a novel modeling approach which relies on the extension of a multi-pr
 oduct single-resource small-bucket lot-sizing model called the proportiona
 l lot-sizing and scheduling problem. This extension is particularly delica
 te when the problem involves sequence-dependent changeovers whose duration
  may overlap multiple planning periods. We show that the proposed modeling
  approach leads to a drastic reduction of the size of the mathematical for
 mulation as compared to state-of-the-art modeling approaches and enables u
 s to significantly improve the numerical efficiency of a mathematical prog
 ramming solver at providing near-optimal solutions of the problem. We then
  consider an important aspect of the renewable energy generation: it is hi
 ghly uncertain and can hardly be accurately predicted. To handle this unce
 rtainty\, we propose a two-stage scenario-based stochastic programming app
 roach. This modeling approach results in the formulation of a large-size m
 ixed-integer linear program displaying a block-decomposable structure. To 
 solve it\, we develop an enhanced branch-and-Benders-cut algorithm which c
 ombines several strategies to increase the convergence speed of the algori
 thm towards an optimal solution. This allows us to solve to near-optimalit
 y instances involving a large number of scenarios. We finally extend our p
 revious work in two directions. We thus include uncertainties on both the 
 renewable energy generation and the demands\, and refine our modeling of t
 he decision process by considering multiple decision stages. This allows u
 s to account for the fact the uncertain parameters are revealed progressiv
 ely over time and that some planning decisions can be adjusted accordingly
 . We introduce a multi-stage stochastic programming model for this problem
  and develop an hybrid algorithm combining a branch-and-bound search and a
  stochastic dual dynamic programming approach to solve it.\nRapporteurs\nM
 . Raf JANS: Department of Logistics and Operations Management\, HEC Montr
 éal\, CANADA\nM. Vincent LECLERE\, Professeur associé\, École nationale
  des ponts et chaussées\, FRANCE\nExaminateurs\nMme Dominique QUADRI\, Pr
 ofesseure des universités\, Université Paris Saclay\, FRANCE\nM. Nabil A
 BSI\, Professeur\, Mines Saint-Etienne\, FRANCE\nMme Ayse AKBALIK\, Maîtr
 esse de conférences (HDR)\, Université de Lorraine\, FRANCE\nLien publiq
 ue live streaming  : ici
CATEGORIES:Science des Données,Thèses et HDR
LOCATION:LISN Site Plaine – Digitéo\, 1 rue René THOM 91190 Gif-sur
 -Yvette\, France
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DTSTART:20250330T030000
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