Integrated Transportation and Queueing System Model for Optimizing the Logistics Efficiency of Freight-Forwarding Services at Nabire Port

Nabire Port is the main logistics node of Central Papua Province, yet its service performance has rarely been examined from a queueing perspective using operational data recorded at the level of individual arrivals. This study analyses ship services and freight-forwarding truck services at Nabire Port as two separately observed subsystems and combines their subsystem-level results into a constructed logistics-time indicator, not a verified tandem queue. The novelty of the study lies in its empirical comparison of classical queueing benchmarks with observed timestamps in a multi-cargo regional port, together with an explicit assessment of when those benchmarks are and are not applicable. Primary data comprising 60 ship arrivals (July-September 2026) and 60 forwarding-truck arrivals (July 2026) were analysed using M/M/1 and M/M/c models. Distributional assumptions were tested using the Kolmogorov-Smirnov test, and theoretical results were compared with observed waiting times and, for the single-server case, the Kingman approximation. The ship system was stable, with a utilisation of 0.391, a theoretical waiting time of 9.15 hours, and a time in system of 23.40 hours, whereas the observed mean waiting time was 1.33 hours. The single-server truck model gave a utilisation of 0.907 and a theoretical waiting time of 26.34 hours, far above the observed mean of 0.49 hours, because up to three trucks were served simultaneously. In the M/M/c scenario with two service channels, the theoretical truck waiting time fell to 0.70 hours and the combined ship-truck indicator to 26.81 hours, compared with 52.45 hours with one channel. The Kolmogorov-Smirnov tests rejected the exponential assumptions for all inter-arrival and service times; therefore, the M/M results are interpreted as theoretical baselines and model-based scenario indications, not as validated predictions or as an economically optimal capacity, since no cost data were analysed