As the “first service station” for ships in the whole port logistics system, the tugboat operation system is one of the most important systems in port logistics. This paper formulated the tugboat scheduling problem as a multiprocessor task scheduling problem (MTSP) after analyzing the characteristics of tugboat operation. The model considers factors of multianchorage bases, different operation modes, and three stages of operations (berthing/shifting‐berth/unberthing). The objective is to minimize the total operation times for all tugboats in a port. A hybrid simulated annealing‐based ant colony algorithm is proposed to solve the addressed problem. By the numerical experiments without the shifting‐berth operation, the effectiveness was verified, and the fact that more effective sailing may be possible if tugboats return to the anchorage base timely was pointed out; by the experiments with the shifting‐berth operation, one can see that the objective is most sensitive to the proportion of the shifting‐berth operation, influenced slightly by the tugboat deployment scheme, and not sensitive to the handling operation times.

To relieve pressure from large vessels and intensive arrival of trucks, an increasing number of marine terminals require trucks to make appointments prior to delivering containers. The quotas are supposed not to impede truck deliveries but should be sufficiently used. For terminals where trucks are allowed to make appointments and arrive on the same day, a critical problem is the simultaneous allocation of appointment quotas and yard cranes. A bi-objective integer model is set up to balance the trade-off between terminals and trucks. A three-dimensional quota is used for characterizing the variabilities in delivery operation. It also overcomes the challenges involved, including the inside workload, and the yard closing deadline. A non-dominated genetic algorithm II-based approach is developed to solve the problem. The method is validated through extensive numerical experiments, and the results show the advantages of simultaneously balancing the workload of yard cranes instead of merely smoothing truck arrivals. Parameter analysis then reveals the influence of both larger vessel size and an increase in trade, as well as the utility ratio of quotas.

Many countries rely heavily on seaports to promote their well-being and economy. Unreliability of seaport services can considerably impact cargo owners, seaport customers and shipping lines resulting to their dissatisfaction. This study aimed to evaluate service quality of Tanzanian seaports considering Dar es Salaam seaport as a study area. The study discovered the negative gap on the service quality after evaluating seaport user’s perceptions and expectations of delivered seaport services, meanwhile the service quality of Dar es Salaam seaport is poor. Opinions of seaport user’s concerning seaport service quality were computed based on the SERVQUAL model which developed by Parasuraman by considering the essential measurements of Tangibility, Empathy, Reliability, Assurance and Responsiveness. Senior administrators and managers employed in numerous shipping lines in Tanzania were interviewed to check the model validity, then 314 members of Tanzania shipping and logistics associations were engaged in a study survey. Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied to validate the seaport service quality measurements and to observe their association with satisfaction of seaport customers using Amos Graphics software version 26. It found that seaport service quality is a five-measure construct; responsiveness was significantly influence customer satisfaction while empathy and assurance could insignificantly influence customer satisfaction. Reliability was found significantly to cause customer dissatisfaction and tangibility was insignificantly to cause customer dissatisfaction. Academically, this research donates to management practice as seaport administrators, managers and executives can utilize the seaport service quality measurements to evaluate their customer’s satisfaction and defending for seaport service quality investments as a relational marketing strategy.

The beam-type placement machine is capable of picking up multiple components simultaneously from the feeders in printed circuit board (PCB) assembly. Simultaneous pickup occurs only if the heads in the beam are aligned with the feeders and the nozzle-types on these heads match with the component-types on the feeders. In order to minimise the assembly cycle time, the optimisation problem is decomposed into two sub-problems, the pickup combination and sequencing problem, and the placement cluster and sequencing problem. These two sub-problems are simultaneously solved by the proposed hybrid genetic algorithm (HGA). The pickup combination and sequencing problem is similar to the popular multi-compartment vehicle routing problem (MCVRP); a genetic algorithm (GA) for the MCVRP is therefore modified and applied to solving the pickup combination and sequencing problem. A greedy heuristic algorithm is used to solve the placement cluster and sequencing problem. The numerical experiments reveal that the HGA outperforms the algorithms proposed by previous papers.