Artificial Ecosystem Algorithm Applied to Multi-Line Steel Scheduling

Steel production scheduling is recognised as a major global industry with some of the most difficult industrial logistical problems. Galvanised steel can be used as a raw material for the automotive or construction industries. This paper focuses on scheduling four lines involved in the production of galvanised steel. Specifically, we consider lines in ArcelorMittal's manufacturing plant. ArcelorMittal is one of the largest steel producers in the world. The lines considered include the following: (a) Pickling Line, (b) Tandem Mill Line, (c) Hot Dip Galvanizing Line 1, and (d) Hot Dip Galvanizing Line 2. We apply four variants of the Artificial Ecosystem Algorithm to the multi-line steel scheduling problem. In addition, we compare their performance against several alternative solutions including Simulated Annealing, Tabu Search, Hill Climbing, Branch and Bound, Monte Carlo Tree Search, Genetic Algorithm, Cuckoo Search and Particle Swarm Optimisation.