Coupling of agenetic algorithm and a simulation model for short-term scheduling in a discontinuous fine chemistry plant

In this paper a discrete-event simulation model is coupled with a genetic a lgorithm to treat highly combinatorial scheduling problems encountered in a production campaign of a fine chemistry plant. The main constraints and fe atures of fine chemistry have been taken into account in the development of the model, thus allowing a realistic evaluation of the objective function used in the stochastic optimization procedure. After a presentation of prob lem combinatorics, the coupling strategy is then proposed and illustrated b y nn example of industrial size (24 equipment items, 140 products, 12 diffe rent production recipes and 40 products to be recycled during the campaign) . This example serves as an incentive to show how the approach can improve production performance. Three technical criteria have been studied: campaig n completion time, average product cycle time, respect of due-dates. Two ki nds of optimization variables have been considered: product input order and /or allocation of heuristics for conflit treatment. The results obtained ar e then analysed and some perspectives of this work are presented.