A multiple traveling salesman problem model for hot rolling scheduling in Shanghai Baoshan Iron & Steel Complex

The hot strip mill production scheduling problem: A tabu search approach

A computer is used for generating a part inspection plan for a coordinate measuring machine (CMM), in a feature-based rapid design system (RDS), having a Feature-Based Design Environment (FBDE), an Episodal Associative Memory (EAM), Fabrication Planning (FAB), and an Inspection Plan (INSP), with features which include form features (D1) which define the form or shape of the part, manufacturing features (D2), inspection features (D3), and geometric and design (GD&T) features (D4). The Inspection Plan (INSP) includes interaction means wherein the inspector interacts with the system to guide it to a desired result, and the inspector can define setups, measurement points, sequence for the points, and the via points. For the inspector's sequence input, a learning process is included, so that the inspector's desired sequence is sent to discovery means for organizing patterns and defining rules, which by recalling relevant past experiences, and learning from the inspector's input, creates a "self-improving" expert system.

Automated process planning for quality control inspection

A steel hot rolling mill subjects steel slabs to high temperatures and pressures in order to form steel coils. We describe the scheduling problem for a steel hot rolling mill. We detail the operation of a commercial decision support system which provides semi-automatic schedules, comparing its operation with existing, manual planning procedures. This commercial system is currently in use in several steel mills worldwide. The system features a very detailed multiobjective model of the steel hot rolling process. This model is solved using a variety of bespoke local and Tabu search heuristics. We describe both this model and the heuristics used to solve it. The production environment is highly unstable with frequent, unforeseen events interrupting planned production. We describe how the scheduling system's models, algorithms and interfaces have been developed to handle this instability. We consider particularly the impact on existing planning and production systems and the qualitative improvements which result from the system's implementation.

A flexible decision support system for steel hot rolling mill scheduling

Iron and steel production is a multistage process. A slab yard serves as a storage buffer between the continuous casting stage and the steel rolling mill. Slabs from continuous casting are stacked in the yard to await rolling. When implementing the rolling production schedule, slabs need to be picked up from the slab yard one by one according to the scheduled rolling sequence, heated in the heating furnace and then rolled. There are usually a number of suitable candidate slabs in the yard satisfying the requirement for each rolling item. In general, these candidate slabs are not at the most convenient positions in the stacks. To pick up a required slab, the slabs above it need to be shuffled. To save slab-handling cost and prevent the handling cranes from becoming a bottleneck, slabs for a rolling schedule must be selected from among the candidate slabs so that the total number of slabs shuffled is minimized. The paper studies the slab stack-shuffling problem using the hot strip rolling mill in Shanghai B...

Modelling and a genetic algorithm solution for the slab stack shuffling problem when implementing steel rolling schedules

Discrete event sequencing as a Traveling Salesman Problem

Optimal inter-process steel production scheduling

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Discrete event sequencing as a Traveling Salesman Problem

Optimal inter-process steel production scheduling