Research And Application On Optimal Scheduling Problem In Cold Rolling Line

The cold-rolled sheet is a kind of deep processed product with high added value in iron and steel enterprise. Improvement of production planning and scheduling level is an important way to increase economic benefits, reduce production costs and enhance competitive power. Based on a key project of National High-Tech Research and Development Program, this dissertation studies the production planning and scheduling problem of cold rolling line in a cold-rolled sheet plant of Shanghai Baosteel Co. Ltd. Following research work is mainly carried on in this dissertation.As the "bottle-neck" process, bell-type batch annealing is obviously different from other processes in the cold rolling line. Its production has complicated resource, energy and process constraints. Its mathematic model is difficult to formulate and optimal scheduling is hard to implement. Scheduling in batch annealing shop includes combinatorial stacking of coils and order optimization. A mathematical model is set up to minimize the total heating treatment time of batch stacking. The factors of affecting heating treatment time are thoroughly included in this model through analysis of technical rules. An improved group adaptive genetic algorithm is proposed to optimize the model with the methods of improving initial solutions, heuristic crossover, mutation and local search under the guidance of process rules.The unique process rules of bell-type annealing lead to its absence from production supervision. Accurate simulation of its process is the base of production planning of the whole cold rolling line. Modeling problem of bell-type annealing shop scheduling is discussed based on combinatorial stacking. The traveling flow of lots in annealing shop is mapped into hardware design modeling analogously, based on the similarity of concurrency, sequence and hierarchy between large-scale integrated circuit and production scheduling of bell-type batch annealing. A simulation model of annealing shop scheduling is developed with hardware description method based on mature SystemC simulation platform in the field of electronics design. Accurate and efficient simulation of batch annealing workflow can be achieved by this model.The optimal order scheduling problem with many categories and small batches is researched based on above work. In order to improve order delivery satisfaction and address scheduling uncertainty, a fuzzy Job Shop scheduling model is established to represent the whole logistics of the cold rolling line, in which an order batch is regarded as a fundamental scheduling job. Due to the inventory in front of each process as the production buffer, manufacturing time progress rules on each process are defined based on different scenarios. A Parallel Discrete Particle Swarm Optimization (PDPSO) algorithm based on multi-sub colony is implemented to solve the fuzzy scheduling model, which improves the calculation speed.Description of order planning for multi-process cold rolling line using Job Shop is still not a suitable way according to its abstraction, so an optimization model for production planning and coil scheduling in the whole process of cold rolling is proposed in this paper. Objective of the model is to minimize order delay punishment and virtual switch cost in each process. It is difficult to optimize the model directly because of its complication, so a reasoning method based on time windows is constructed to deal with the coupling essence of the problem. Then optimization of multi-process production planning is converted to several constrained single-process scheduling problem with time windows. Heuristic improved Ant Colony Optimization (ACO) algorithms are adopted to solve the problem because of resemble between them.The research work mentioned above is embedded in the production management and decision system of the plant as core algorithms. The feasibility and validity of the research work is testified by the running result in the field. It shows that the achievement can provide reasonable guidance for production planning and scheduling in cold rolling line.