A Hyper Heuristic Algorithm Based Genetic Programming for Steel Production Scheduling of Cyber-Physical System-ORIENTED

Research on Multi-Equipment Collaborative Scheduling Algorithm under Composite Constraints

Multi-equipment multi-process frequent scheduling under complex constraints is at the root of a large number of idle time fragments and transport waiting time in multi-equipment processes. To improve equipment utilization and reduce idle transportation time, a production process optimization scheduling algorithm with “minimum processing time and minimum transportation time” is proposed. Taking into account factors such as product priority, equipment priority, process priority, and overall task adjustment, the scheduling optimization is carried out through a hybrid algorithm combining a one-dimensional search algorithm and a dual NSGA-II algorithm. Compared with other algorithms, the scheduling algorithm proposed in this article not only shortens the minimum processing time but also strives to maximize the utilization rate of each piece of equipment, reducing the processing time of the enterprise by 8% or more, while also reducing the overall transportation time and indirectly reducing costs. The superiority of this algorithm is verified through practice, showing that that the complexity of the scheduling process is lower, and it is feasible in actual operation.

Multi-Stage Multi-Product Production and Inventory Planning for Cold Rolling under Random Yield

This paper studies a multi-stage multi-product production and inventory planning problem with random yield derived from the cold rolling process in the steel industry. The cold rolling process has multiple stages, and intermediate inventory buffers are kept between stages to ensure continuous operation. Switching products during the cold rolling process is typically very costly. Backorder costs are incurred for unsatisfied demand while inventory holding costs are incurred for excess inventory. The process also experiences random yield. The objective of the production and inventory planning problem is to minimize the total cost including the switching costs, inventory holding costs, and backorder costs. We propose a stochastic formulation with a nonlinear objective function. Two lower bounds are proposed, which are based on full information relaxation and Jensen’s inequality, respectively. Then, we develop two heuristics from the proposed lower bounds. In addition, we propose a two-stage procedure motivated by newsvendor logic. To verify the performance of the proposed bounds and heuristics, computational tests are conducted on synthetic instances. The results show the efficiency of the proposed bounds and heuristics.