Population-based extremal optimization algorithm for hot rolling scheduling problem

作者：Sun, Kai^*; Yang, Genke; Pan, Changchun

来源：Journal of Computational Information Systems, 2010, 6(7): 2415-2422.

• Evolutionary techniques
• Extremal optimization
• Hot rolling scheduling problem
• Modified algorithms
• Multiple constraint
• Optimal production
• Other algorithms
• Population evolutionary algorithm
• Prize-collecting
• Production data
• Rolling scheduling
• Solution methods
• Transition costs

The paper presents a valid model and a solution method named population-based extremal optimization algorithm (PEOA) to solve the hot rolling scheduling problem (HRSP). Firstly, the problem is formulated as a prize-collecting vehicle problem (PCVRP), which considers two major requirements: (a) selecting a subset of slabs from manufacturing slabs to be processed;(b) determining the optimal production sequence under multiple constraints, such as sequence-dependant transition costs, non-execution penalties etc. Secondly, a new algorithm which combined the extremal optimization (EO) with population evolutionary technique is proposed to solve the problem. The proposed algorithm is applied to a set of real production data and the performance of the algorithm is evaluated by comparing the results with other algorithms such as EO, modified algorithm (MGA), etc. Comparing results indicate that this new algorithm is an effective and competitive approach for the HRSP.

• 出版日期2010
• 单位上海交通大学