Search

Automated container terminals (ACTs) represent state-of-the-art facilities for container handling and are a current development trend. However, enhancing their operational efficiency while minimizing energy consumption remains a challenge. While metaheuristics are helpful in addressing container terminal problems, their capabilities are limited when used alone to tackle complex or integrated problems. Hybrid models can be more effective in facing these challenges. This research proposes a hybrid model, termed WOA + PSO, which combines the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) as a novel approach to address the integrated scheduling problem of automated quay cranes (AQCs), automated lift vehicles (ALVs), and automated stacking cranes (ASCs) in an ACT. Additionally, the WOA + PSO collaborates with a simulation model in a framework to become a simulation-based optimization approach. The performance of WOA + PSO is evaluated by comparing it with its base models, WOA and PSO, as well as a genetic algorithm (GA), through extensive experiments. The results show that WOA + PSO outperforms the others in achieving the objective of balancing operational efficiency and energy consumption. [ABSTRACT FROM AUTHOR]

• An energy model is set up based on energy consumption analyses of machining processes; • NC codes are structurally evaluated and then popularised into the energy model; • A hybrid GA-ACA algorithm is designed to minimise air-cutting toolpaths to optimise the energy model. Machining processes comprise numerous energy consumption activities. Given the significance of the circular economy and manufacturing sustainability to modern societies, it is paramount to design effective methodologies to accomplish energy-efficient machining processes. With this aim, this research presents a new approach of energy consumption optimisation for machining processes based on numerical control (NC) programs. In the approach, the following innovative characteristics are exhibited: (i) An energy model is systematically established based on a detailed analysis of energy consumption activities in machining processes; (ii) NC programs for specific machining processes are assessed in detail and popularised into the energy model for instantiation; (iii) An optimisation algorithm hybridising the genetic algorithm and the ant colony algorithm is designed to minimise air-cutting toolpaths to optimise the energy model. Two case studies were conducted to validate the presented approach. The case studies revealed that the accuracy of the energy model was 95.3% of the actual energy consumption. The studies also showed that, based on the optimised energy model, the total length of air-cutting toolpaths was reduced by 43.8%, and the total machining time was diminished by 25.8%. It can be concluded that the developed approach can achieve substantial energy savings, and therefore it is highly promising to support machining industries to meet their sustainable targets. [ABSTRACT FROM AUTHOR]