Integrated optimization of storage space allocation and crane scheduling in automated storage and retrieval systems

This paper addresses the challenge of integrated optimization for storage space allocation and crane scheduling in automated storage and retrieval systems. The problem encompasses tasks such as assigning storage/retrieval requests, allocating storage spaces, and planning crane routes within each operation cycle. To tackle this, we introduce a multi-layer adaptive length coding method to effectively map the solution space to the problem space. Employing a coevolutionary framework, we decompose and process the integrated optimization problem, further optimize it with a hybrid genetic algorithm. Numerical experiments across a wide range of scenarios are conducted to evaluate the algorithm’s performance under varying request sizes and crane capacities. The introduction of the coevolutionary framework improves optimization by up to 14.78%, with an average improvement of 34.09% compared to the method currently used in the company. In addition, we introduce a novel optimization metric, termed potential energy consumption, designed to enhance system energy efficiency. Comparative analysis against metrics like makespan reveals the superiority of our proposed approach in terms of coverage and optimality, particularly in large-scale scenarios. The combined implementation of integrated optimization and the new evaluation metric leads to substantial energy cost savings for real-world automated storage and retrieval systems.