9th IAM Workshop

The goal of this workshop is to bridge the gap between Academia and Industry, offering an event to promote research works on Evolutionary Computation based on Real World and mainly industry. The event is coorganized by Iridia (Artificial Intelligence Laboratory at ULB) and ArcelorMittal Global R&D.

Metaheuristics have been applied successfully to many aspects of applied Mathematics and Science, showing their capabilities to deal effectively with problems that are complex and otherwise difficult to solve. There are a number of factors that make the usage of metaheuristics in industrial applications more and more interesting. These factors include the flexibility of these techniques, the increased availability of high-performing algorithmic techniques, the increased knowledge of their particular strengths and weaknesses, the ever increasing computing power, and the adoption of computational methods in applications. In fact, metaheuristics have become a powerful tool to solve a large number of real-life optimization problems in different fields and, of course, also in many industrial applications such as production scheduling, distribution planning, and inventory management.

This workshop proposes to present and debate about the current achievements of applying these techniques to solve real-world problems in industry and the future challenges, focusing on the (always) critical step from the laboratory to the shop floor. A special focus will be given to the discussion of which elements can be transferred from academic research to industrial applications and how industrial applications may open new ideas and directions for academic research.

Topic areas of IAM include (but are not restricted to):

• Success stories for industrial applications of metaheuristics

• Pitfalls of industrial applications of metaheuristics.

• Metaheuristics to optimize dynamic industrial problems.

• Multi-objective optimization in real-world industrial problems.

• Meta-heuristics in very constraint industrial optimization problems: assuring feasibility, constraint-handling techniques.

• Reduction of computing times through parameter tuning and surrogate modelling.

• Parallelism and/or distributed design to accelerate computations.

• Algorithm selection and configuration for complex problem solving.

• Advantages and disadvantages of metaheuristics when compared to other techniques such as integer programming or constraint programming.

• New research topics for academic research inspired by real (algorithmic) needs in industrial applications.