An efficient generalised opposition-based multi-objective optimisation method for factory cranes with time-space constraints

In order to improve the performance of large manufacturing enterprises, besides the adoption of new technologies, it is also feasible to efficiently schedule logistics equipment such as cranes, which costs much less since only software changes are involved. In this research, the objectives of minimising total waiting cost and total delay cost are optimised simultaneously when executing crane-delivery tasks in factories. Given the time-space constraints and NP-hard nature of the problem, a generalised opposition-based learning (GOBL) mechanism and two problem-based searching strategies are developed and fused into the multi-objective differential evolution approach, namely GOMODE. The introduction of GOBL mechanism enables the proposed algorithm to search in a more extensive solution space, which improves the population diversity and avoids the premature problem. The performance of the GOMODE has been compared with classical multi-objective optimisation algorithms. The experimental results indicate that the GOMODE achieves a better performance both on solutions' quality and diversity. [Received: 11 December 2018; Accepted: 23 December 2019]