2.1 Field-Site: Wood Chip Terminal and Land-Side Operations
The bulk wood chip export terminal used for the simulation modelling in this project operates in a medium-size port in Australia. Wood chips are delivered to the terminal from three processing facilities by a dedicated fleet of trucks and are stored at the terminal in preparation for vessel arrivals. Sufficient product has to be available to complete the loading at the time of vessel arrival since the vessel hourly loading rate costs are far greater than those of truck unloading. The truck delivery cycle average, depending on the distance from the facilities, can be 40, 90 or 300 min for a round-trip, excluding terminal unloading.
At the terminal, trucks are weighed before and after unloading on a weighbridge. Operators swipe an RFID card that records arrival and departure time, gross and net truck weight and the product delivered. Trucks can be unloaded by two hydraulic ramps. Product falls into a common collector bin and is moved to stockpiles using by conveyor belt.
On average trucks wait to unload between 7–10 min. However, close to 30% of the trucks wait between 15 and 45 min to unload. In the context of relatively short delivery cycles, excessive waiting times can lead to substantial truck productivity losses. Terminal visits and discussions with staff also confirmed the terminal was regularly experiencing significant truck congestion at the terminal gate and unloading ramps.
2.2 Discrete Event Simulation Model of Terminal Gate Operations
A simulation approach allows for representation of some of the complex interactions taking place at a terminal  and insights into operations that may support the development of tangible solutions for industry . Simulation also allows a ‘what-if’ analysis under certain scenarios and comparison between multiple alternatives . This research deployed a discrete event simulation model to represent terminal gate operations.
The literature on dry bulk terminal distinguishes between export terminals, and import terminals  as they and generally serve only one of the two functions. Researchers have primarily focused on ores as the primary dry bulk commodities of interest: coal export , coal and iron import  and bauxite imports . Munisamy  is one of the few examples of a timber terminal related research. One of the main problems explored in the dry bulk terminal literature is how to increase the capacity of dry bulk terminals [5, 19] with the aim of reducing vessel waiting times and associated penalties. Throughput capacity increases on the maritime side are not always met with a similar approach on the land side. Financial penalties for vessel waiting times (demurrage) are one of the most frequently mentioned reasons for optimizing and improving the loading or unloading process at terminals.
Terminal arrival and departure data were collected from reports generated by the weigh-bridge. Three months of truck arrivals were included, totaling more than 15,000 trips. The duration of individual unloading stages was determined using geo-fences implemented in a commercial navigation software which used inputs from on-board GPS data from one trucking operator. The data collected were then fitted to distributions, using the Arena Input Analyzer, so they could be sampled during the simulation using a Monte Carlo sampling technique. The simulation model logic follows closely the process flow at the terminal and is illustrated in Fig. 1 and includes the alternatives modelled to address congestion.
The model is based on a number of assumptions drawn from observations at the terminal and the distribution fitting process:
Two companies carry two types of products that cannot be mixed. Unloading of one product must be completed prior to unloading the other;
Each company operates a fleet with two types of trucks in different proportions. The payloads of the two types of trucks are represented through normal distributions fitted on empirical data: μ1 = 25.8, σ1 = 0.798 and μ2 = 33.7, σ2 = 1.82;
Both unloading ramps can unload both types of truck. Unloading times vary depending on the payload of the truck and are described by a lognormal distribution (μ = 5.16, σ = 3.97);
Concurrent unloading of the same product can take place if the other unloading ramp has completed 60% of the unloading stage;
The conveyor belt system allows a new product to be unloaded when one unloading ramp has reached 80% of the unloading stage;
The possibility of breakdowns is not considered at this time;
The weigh-bridge weighing in time was not included to maintain consistency with existing terminal measurements;
The travel time between the weigh-bridge and unloading ramp and back across the weigh-bridge is considered to be fixed at 1 and 2 min respectively.
The inter-arrival time (IAT) is best represented by a gamma distribution (k = 1.49, θ = 6.97) and, the weigh-bridge weighing time on departure is described by a normal distribution (μ = 3.46, σ = 1.68).