Reference: PPI-06-05-5703
Period underway: 31/12/2005 – 31/12/2007
Type: Research projects of emerging groups
Amount: 11,600 €
Financed by: UPV. VICE-RECTOR’S OFFICE FOR INNOVATION AND DEVELOPMENT
Principal Investigator: Mula Bru, Josefa
Participant: Ferriols Martínez, Francisco José; Mula Bru, Josefa; Peidro Paya, David; Poler Escoto, Raul; Tomás Miquel, José V.

Description:

The overall project objective is to develop models based on fuzzy mathematical programming to solve production planning problems under uncertainty conditions. These models will allow to adequately formalise any uncertainty present in real problems that rise when planning the production of industrial enterprises; e.g. uncertainty in market demand, uncertain delays due to failures in production processes, uncertainty in quantities supplied by providers, to mention but a few.

For modelling and solution purposes, the fuzzy mathematical programming approaches proposed in the doctoral thesis of this project’s principal investigator shall be considered. This doctoral thesis formed part of research project CICYT DPI 2002-01755, which is now ending. With the aforementioned CICYT Project, a new research line was formed which is to be continue through the undertaking of this project.

In order to prove the utility and efficacy of the models to be developed, different planning problems in an industry enterprise from the automobile sector will be solved by joining up “all” (this is one of main project objectives) the parameters, decision variables and restrictions that will be required by the production planning process. This will prove the models’ operation and their ability to handle a large number of variables in a real case.

It will be particularly interesting to check the characteristics of the solutions generated by the proposed models compared to the solutions generated by existing tools in the company that is the object of such application. Hence the idea is to integrate the proposed models into the tools that currently interact with the decision maker. Studying the characteristics of the solutions would ultimately determine which models work the best for planning problems under uncertainty.