<p>This paper deals with the problem of investment in new means of production, taking into account as well financial aspects as scheduling issues. Firstly, a presentation of the tackled hybrid flow shop scheduling problem and its translation into production capacities at different stages. Then, hypotheses and modeling choices made for investment decision are presented. An analytic mathematical model is proposed and tested with an example, to solve integrated investment/scheduling problem, in the particular case of hard demand context on a unique product fabricated on the considered production line. Since equations are clearly written, this model can be used as well as a simulation model, thus allowing taking successive decisions on several floors, as an optimization one, when coupled with an optimization method, then allowing a global vision of the production tool. In this case, an optimal set of decisions is offered to the model user, usually company’s decision-makers. At the end of this paper, we test our model in optimization conditions, to quantify the influence of both investment capacity and discount rate parameters, thus validating their taking into account in the model.</p>

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Decision Support Mathematical Model for a Production Line Design in a Context of High Demand

  • Christophe Sauvey,
  • Wajdi Trabelsi

摘要

This paper deals with the problem of investment in new means of production, taking into account as well financial aspects as scheduling issues. Firstly, a presentation of the tackled hybrid flow shop scheduling problem and its translation into production capacities at different stages. Then, hypotheses and modeling choices made for investment decision are presented. An analytic mathematical model is proposed and tested with an example, to solve integrated investment/scheduling problem, in the particular case of hard demand context on a unique product fabricated on the considered production line. Since equations are clearly written, this model can be used as well as a simulation model, thus allowing taking successive decisions on several floors, as an optimization one, when coupled with an optimization method, then allowing a global vision of the production tool. In this case, an optimal set of decisions is offered to the model user, usually company’s decision-makers. At the end of this paper, we test our model in optimization conditions, to quantify the influence of both investment capacity and discount rate parameters, thus validating their taking into account in the model.