Abstract
Welding, an irreplaceable process in the modern manufacturing industry, consumes enormous amounts of energy. The schedule in a welding shop greatly impacts both its energy consumption and productivity. Thus, it is of great significance to solve the welding shop scheduling problem (WSSP) considering both energy efficiency and productivity. In this paper, to solve a real-life WSSP, a multiobjective mathematical model is proposed and an effective multiobjective artificial bee colony algorithm (MOABC) is developed. The results of a designed numerical experiment indicate that the proposed MOABC performs better than Strength Pareto Evolutionary Algorithm 2 and Nondominated Sorting Genetic Algorithm II. Finally, the proposed model and MOABC algorithm are applied to solve a real-life girder WSSP of a Chinese crane company. The results also demonstrate that the proposed method can greatly reduce energy consumption and makespan compared to other algorithms.
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