DEVELOPMENT OF A SIMULATION-INTEGRATED DYNAMIC INTEGER PROGRAMMING MODEL FOR OPTIMIZING TRUCK REPLACEMENT DECISIONS IN MINING

Date

2026-1-22

Author

Serbest, İnanç Taha

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Capital equipment replacement planning is a critical decision problem in cyclic continuous operations, especially in mining, where haulage fleets operate over long project lives under changing production and operating conditions. Equipment aging, expanding haul distances, and time-varying production targets jointly influence productivity, operating costs, and investment requirements. Conventional equipment replacement approaches often address operational performance and economic evaluation separately, which limits their ability to support long-term, system-level decision-making under realistic conditions. This thesis study develops a simulation-integrated dynamic integer programming framework to support long-term replacement and investment planning for truck-based haulage systems. A discrete-event simulation model is first constructed to represent detailed haulage operations, including route-specific cycle times, age-dependent equipment availability, fuel consumption, and productivity under evolving road networks and operational conditions. These simulation outputs are then transferred into a mathematical optimization model as age-, period-, and location-dependent parameters of individual trucks. The integer-programming optimization model determines acquisition, utilization, replacement, and retirement decisions for a heterogeneous truck fleet over a multi-period planning horizon. The objective is to minimize the Net Present Value of total fleet-related costs, including capital investments, operating costs, idle capacity production losses, and salvage and second-hand sale revenues, while satisfying location-specific production targets in each period. The model explicitly considers new and second-hand equipment alternatives, availability-based productivity degradation, and multi-location deployment constraints. The proposed framework is applied to a real open-pit mining operation with 195 different routes over a 15-year planning horizon. The application results show that all production targets are met with a dynamically adjusted fleet size and replacement schedule. The optimized solution yields a minimum discounted total cost of €27.96 million and demonstrates that replacement and investment decisions evolve in response to equipment aging, mine expansion, and changing haulage conditions. Sensitivity analyses indicate that production targets strongly influence fleet size and investment timing, while financial parameters mainly affect overall cost levels. The results confirm that integrating discrete-event simulation with dynamic optimization provides a practical and reliable tool for long-term equipment replacement planning in mining and other cyclic continuous systems.

Subject Keywords

Capital Equipment Replacement Planning, Dynamic Integer Programming Model, Discrete-Event Simulation, Cyclic Haulage Operations, Open-Pit Mining, Truck Fleet Optimization

URI

https://hdl.handle.net/11511/118767

Collections

Graduate School of Natural and Applied Sciences, Thesis