Steelmaking Dispatching Under Chance-Constrained Process Uncertainty
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Solution Overview
Problem
Current steelmaking-and-continuous-casting dispatching methods are inefficient in handling non-critical events, leading to increased production costs and downtime due to the inability to effectively manage small fluctuations and uncertainties in the production process, with existing robustness optimization methods being too conservative and stochastic programming requiring accurate distribution data that is often difficult to obtain.
Innovation Solution
A distributed robust chance-constraint model is established to determine processing starting durations and furnace batch sequences, using a dual-approximation or linear-programming-approximation method, combined with a tabu-search algorithm to optimize steelmaking-and-continuous-casting dispatching, considering processing durations as random variables within a distribution set that includes support sets and moment information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robustness optimization methods are used to handle uncertainties in processing durations, then the dispatching plan becomes more stable against fluctuations, but the solution becomes too conservative and loses adaptability to practical production needs
Solution Approach 1:
The patent transforms the robust optimization problem by changing the parameter representation from fixed robust constraints to chance-constrained parameters with probabilistic thresholds. The chance constraints allow parameters to vary within acceptable probability bounds, balancing robustness against fluctuations with adaptability to practical production conditions. This is achieved by formulating constraints that satisfy probability thresholds rather than strict deterministic bounds.
2Reliability
If stochastic programming is used to model uncertainties in processing durations, then the dispatching plan can handle random variations, but accurate distribution data is required which is difficult to obtain in practice
Solution Approach 1:
The patent replaces the need for accurate, complex distribution data with simpler, more readily obtainable data requirements. Instead of requiring precise stochastic distribution parameters that are difficult to obtain, the method uses chance constraints that can be formulated with simpler statistical information, making the approach more practical and less data-intensive while still handling random variations effectively.
3Productivity
If conventional dispatching methods are used, then the computing process is simpler, but the ability to handle non-critical events and small fluctuations is insufficient, leading to increased downtime
Solution Approach 1:
The patent segments the dispatching problem by distinguishing between critical and non-critical events, and by separating the optimization into distinct phases: first determining furnace-batch sequences, then optimizing distribution themes and processing starting durations. This segmentation allows the model to handle non-critical events and small fluctuations through chance constraints without overwhelming computational complexity, as each segment can be optimized independently with appropriate detail.
4Reliability
If the dispatching plan is reformulated for every critical event, then the plan remains optimal, but the frequent re-dispatching increases computational burden and reduces response efficiency
Solution Approach 1:
The patent applies preliminary action by pre-formulating the dispatching plan with chance constraints that anticipate and accommodate uncertainties before they occur. The chance-constrained model is solved in advance to generate a robust dispatching schedule that can handle expected variations without requiring immediate re-formulation when events occur. This preliminary optimization with built-in flexibility reduces the need for frequent re-dispatching while maintaining plan optimality.
Data Source
AI summary
A steelmaking-and-continuous-casting dispatching method and apparatus based on a distributed robust chance-constraint model. The method includes: according to parameters, an objective function and a constraint condition in steelmaking-and-continuous-casting dispatching, establishing the distributed robust chance-constraint model; by using a dual-approximation method or a linear-programming-approximation method, solving the distributed robust chance-constraint model, to obtain processing starting durations of cast batches in conticasters and processing starting durations of furnace batches in machines other than the conticasters; and by using a solved result of the distributed robust chance-constraint model as an evaluation criterion, by using a tabu-search algorithm, determining a furnace-batch sequence and a distribution theme in the steelmaking-and-continuous-casting dispatching. The method deems the processing duration in the steelmaking-and-continuous-casting process as a random variable, and makes the description by using the polyhedral support set and the accurate moment information, and the method meets the actual production conditions more than the conventional research models.