2068 waiting due to hung-up point, waiting for truck, in planned interference, in transit), and distribution of extraction among active drawpoints. At the haulage level: tonnage transported per shift, truck cycle times per route, fleet utilization, waiting time at loading stations and dump points, and percentage of time with congestion in the underground road network. In the material handling system: physical availability and utilization of each element in the chain, tonnage processed per period, frequency and duration of cascading stoppages, occupancy levels of intermediate storage systems, and distribution of daily production delivered to surface. 4.3 Identification of the Dominant Constraint or Bottleneck A highly practical result that emerges naturally from the integrated model is the identification of the dominant system constraint in each evaluated scenario. In a system with three interdependent subsystems, the dominant constraint — the subsystem that limits the overall productive capacity — is not always the same: it changes according to the fleet condition, MHS failure rates, and operational configuration. The model enables quantification of the relative contribution of each subsystem to production losses relative to plan, expressing how many tonnes per period are lost due to PL, HL, and MHS constraints respectively. This information has direct value for priority assignment in operations management: if 70% of losses originate in the MHS, improvement efforts should be concentrated there, regardless of how much room for improvement exists in the other subsystems. 5.GENERATION AND EVALUATION OF SCENARIOS 5.1 Scenario Typology The calibrated model can be used to evaluate a wide variety of decision scenarios. To structure its use, it is useful to distinguish three categories according to the type of decision they support: Sizing scenarios evaluate the impact of changes in the size or composition of system resources: number of loading or haulage equipment, MHS capacity, size of intermediate storage systems. These scenarios answer questions such as "how many pieces of equipment do I need to reach my production target?" or "what silo capacity do I need to decouple the HL from the MHS with sufficient autonomy?". Technology scenarios evaluate the impact of changes in technology or operating method: transition from manual to semi-autonomous or autonomous equipment, changes in the dispatch system, adoption of new secondary breaking technologies. These scenarios answer questions such as "how much does my production increase if I move to remote operation?" or "to what extent does haulage automation reduce my operator requirements?". Sensitivity scenarios evaluate the robustness of the production plan against changes in operating conditions: increased hang-up frequency, higher equipment failure rates, reduced compliance with the maintenance programme. These scenarios answer questions such as "how much does my production fall if hang-up frequency increases by 30%?" or "what MHS availability level do I need to meet my plan with 90% probability?". 5.2 Loading Equipment Fleet Sizing The sizing of the LHD fleet at a production level emerges from the interaction of three interdependent factors. The available area defines how many pieces of equipment can operate simultaneously
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