The models,algorithms and implementation results of a computerized scheduling system were introduced for the steelmaking-continuous casting process (SCCP) of a steel plant in China.The scheduling of SCCP in this plant required that each cast plan should be processed on time,the charges in the same cast should be processed continuously on the same caster,and the waiting time of the charges which are in front of each caster cannot exceed the given threshold.At the same time,the processing time of charges cannot be conflicted mutually in the same converters or refining furnaces.Based on the research background,a hybrid optimal scheduling approach and its application were discussed.Aiming at the main equipment scheduling,an optimal scheduling method was proposed which consisted of equipment assignment algorithm based on dynamic program (DP) technique and conflict elimination algorithm based on linear program (LP) technique.The approach guarantees that the charges are continuously processed on the same caster.Meanwhile,the requirement for high temperature ladle can also be satisfied due to the ladle matching function.Numerical results demonstrate solution quality,computational efficiency,and values of the models and algorithm.
In the steelmaking and continuous casting (SMCC) production process, operation time delay may lead to casting break or processing conflict so that the initial scheduling plan becomes unrealizable. Existing research methods are difficult to guarantee the accuracy of the model and successful application to actual applications. The rescheduling problem in response to operation time delay is firstly analyzed. This is then followed by the establishment of a novel multi-objective nonlinear programming model (MONPM). In specifications, a three-stage rescheduling method is proposed including the batches splitting (BS), forward scheduling method (FSM) and backward scheduling method (BSM). As a result, the real-time application shows that the proposed rescheduling method efficiently ensures the continuous casting and dramatically shortens the redundant waiting time for molten steel in very short rescheduling time.
Unlike a traditional flowshop problem where a job is assumed to be indivisible, in the lot-streaming flowshop problem, a job is allowed to overlap its operations between successive machines by splitting it into a number of smaller sub-lots and moving the completed portion of the sub-lots to downstream machine. In this way, the production is accelerated. This paper presents a discrete artificial bee colony (DABC) algorithm for a lot-streaming flowshop scheduling problem with total flowtime criterion. Unlike the basic ABC algorithm, the proposed DABC algorithm represents a solution as a discrete job permutation. An efficient initialization scheme based on the extended Nawaz-Enscore-Ham heuristic is utilized to produce an initial population with a certain level of quality and diversity. Employed and onlooker bees generate new solutions in their neighborhood, whereas scout bees generate new solutions by performing insert operator and swap operator to the best solution found so far. Moreover, a simple but effective local search is embedded in the algorithm to enhance local exploitation capability. A comparative experiment is carried out with the existing discrete particle swarm optimization, hybrid genetic algorithm, threshold accepting, simulated annealing and ant colony optimization algorithms based on a total of 160 randomly generated instances. The experimental results show that the proposed DABC algorithm is quite effective for the lot-streaming flowshop with total flowtime criterion in terms of searching quality, robustness and effectiveness. This research provides the references to the optimization research on lot-streaming flowshop.
