Optimizing collaborative operations for yard cranes (YCs) and yard trucks (YTs) is vital to the overall performance of a container terminal. This research investigates four different hybrid approaches developed for dealing with yard crane scheduling problem (YCSP) and yard truck scheduling problem (YTSP) simultaneously for export containers in the yard side area of a container terminal. First, these approaches use a load-balancing heuristic to assign containers to YCs evenly. Following this, each of them employs a specific heuristic/metaheuristic, such as genetic algorithm (GA), particle swarm optimization (PSO) or subgroups PSO (SGPSO), to generate alternative container loading sequences for each YC. Finally, a simulation model is used to simulate loading and transporting of these export containers, evaluate alternative planning results, and finally output the best planning result. Experiments have been conducted to compare these hybrid approaches. The results show Hybrid4 (SGPSO) outperforms Hybrid1 (Sort-by-bay), Hybrid2 (GA), and Hybrid3 (PSO) in terms of makespan.

The printed circuit board assembly (PCBA) tends to become a bottleneck in an assembly line of electronic products. For improvement, many assembly firms have introduced chip shooter machines. This, however, in turn, raises the issue of how to best utilize these machines. To deal with the PCBA problems, swarm intelligence (SI)-based metaheuristics have been increasingly popular due to the use of guided search that can better drive solutions toward optimality. In this study, we have proposed a novel SI-based metaheuristics, termed improved shuffled frog-leaping algorithm (I-SFLA2), to deal with the feeder assignment problem (FAP), and component sequencing problem (CSP) simultaneously for a chip shooter machine. With novel features such as self-adaptive jump, push jump, direct-jump prevention, and self-adaptive variant, the I-SFLA2 can develop smart jumps for frogs to approach and search around elites quickly while avoiding being trapped in local optima. The I-SFLA2 includes the strategy of transitioning from exploration to exploitation by decreasing the number of memeplexes iteratively. Our small-sized experiments showed the I-SFLA2 had a high hit rate to the optimal solution whereas big-sized experiments showed the I-SFLA2 outperformed the basic SFLA (B-SFLA), B-SFLA(2), the improved SFLA (I-SFLA1), as well as the PSO2 proposed in previous studies. The computational times for the I-SFLA2 were found also reasonable for practical usage. Note to Practitioners-Chip shooter machines have been widely used in industry for printed circuit board assembly (PCBA). Approaches such as exact approach, simple heuristics, and metaheuristics have been proposed for PCBA planning. Although with the capability to find the optimal solution, the exact approaches are found to be computationally intractable when used to deal with a problem of a practical size. Although simple heuristics are easy to use, they usually have the difficulty to find the optimal/near-optimal solution. One recent trend is using metaheuristics to solve PCBA problems as they can avoid the computational intractability of exact approaches while improving over simple heuristics to find a better solution. In this study, we propose an improved shuffled frogs leaping algorithm (I-SFLA2), an advanced swarm intelligence (SI)-based metaheuristic to solve the CSP and FAP simultaneously for a chip shooter machine. With new features and smart jumps, the I-SFLA2 is able to find a better solution compared to some previously-proposed methods.

Printed circuit board assembly (PCBA) is a process of connecting various electronic components through printed circuit boards (PCBs). Due to the need to assemble a lot of components and PCBs at the same time, the PCBA process tends to become the bottleneck in an assembly line. Many assembly firms have thus introduced automated PCBA machines to expedite this process. However, to best operate these machines, effective PCBA planning is still required. Some nature-inspired metaheuristics such as simulated annealing and genetic algorithm (GA) have been increasingly used for the PCBA planning. Also, we find that particle swarm optimization (PSO) has never been employed to deal with the feeder assignment problem (FAP) and component sequencing problem (CSP) at the same time, though it has been regarded as a good competitor to GAs. In this paper, we developed two PSO-based approaches to deal with the two problems simultaneously for a chip shooter machine. In addition, we have conducted experiments to compare the two PSO-based approaches with two GA-based approaches. The experimental results showed that PSO2, the PSO-based approach with sigmoid functions, outperformed others in terms of assembly cycle time. The comparison with an exact approach further shows that PSO2 has a high rate to find the optimal/near-optimal solution.

This paper focuses on graphic-based approaches and proposes a fuzzy knowledge-based disassembly process planning (FDPP) system for DPP. This FDPP system is based on a novel high-level Petri net (PN), termed fuzzy attributed and timed predicate/transition net (FATP/T net). An FATP/T net model for DPP is developed and the model is found very compact and general to any products, due to the comprehensive modeling capability and the inclusion of a bill of material component. It avoids issues such as unmanageable big model size and specific to a product, which are often found in the DPP models based on ordinary PNs. Another feature of the FATP/T net model is that it also includes a functional test operation to discern the usability of detached subassemblies/components. In addition, we have proposed a systemic approach to transform the FATP/T net model into an FDPP system to support DPP at a system level. A telephone kit is used as an example to illustrate the applicability of the FDPP system. The FDPP system is found flexible and expandable due to the independence of its components.