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FP5

CHEM Résumé de rapport

Project ID: G1RD-CT-2001-00466
Financé au titre de: FP5-GROWTH
Pays: Spain

Scheduling and planning procedure under multiobjective criteria

The aim of the CHEM project was to develop and implement advanced Decision Support Systems (DSS) for process monitoring, data and event analysis, and operation support in industrial processes. The systems are synergistic integration of innovative software tools, which improve the safety, product quality and operation reliability as well as reduce the economic losses due to faulty states, mainly in refining, chemical and petrochemical processes.

The CHEM applications consist of integrated sets of software toolboxes that provide robust detection and diagnosis of process problems in real-time. The systems assist operators in assessing process status and responding to abnormal events. The project provides a flexible architecture and a methodology in order to facilitate the development of such applications on many processes.

The aim of this toolbox ('Scheduling and planning procedure under multiobjective criteria') is to provide the basic optimisation algorithms, the management of the objective function and the coordination between the different optimisation modules. Three optimisation techniques will be provided: simulated annealing (SA), mixed stochastic enumerative search (MSES) and genetic algorithms (GA).

Simulated annealing is an evolutionary optimisation technique that allows the improvement of an initial schedule generated using simple heuristic rules (like EDD earliest due-date). The SA algorithm improves the initial point exploring a defined neighbourhood trying to improve a configurable objective function.

Mixed stochastic enumerative search is an improvement of the SA mixing concepts given by the tabu search. The neighbourhood is explored but the explored points are inserted in a tabu list. The idea is that in this way the local optima are detected so and automatic change to another point of the space of solutions could be generated.

The genetic algorithms optimisation technique starts with a population of initial schedules and evolve it using crossover and mutation operators. At The end of the evolving process only the best individuals (those schedules with the best objective function) will be available.

All the optimisation algorithms need the evaluation of performance index. Therefore this toolbox also provides and objective function manager and evaluator, which allow the configuration of, customised objective functions.

Reported by

Univ. Politecnica de Catalunya, Chem. Eng. Dept.
Av. Diagonal 647, G2
08028 Barcelona
Spain
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