Effect of Changing Duty Cycles with a Panel in CM-Shuttle Car Matching: A Case Study

"Accounting for changing duty cycles in equipment matching maximizes equipment utilization and productivity. The objective of this study is to investigate if the optimal number of shuttle cars for a particular panel width is optimal in different segments of the panel. In this study, discrete event simulation (DES) is used to determine the optimal number of cars that maximizes productivity in each segment. Data from a real-life mine is used to validate the model. The study shows that the optimal number of cars for the entire panel may not be optimal for some of the defined segments. INTRODUCTION Fleet optimization is important for efficient mine management. Deploying the optimal fleet minimizes the capital and operating expenditure for particular operating conditions and production targets. In an operating mine, with an already existing fleet, deploying the optimal fleet maximizes productivity or minimizes production cost with the available resources. Hence, a lot of research has focused on how to determine the optimal fleet size for a mining operation, given particular operating conditions. Mine managers and engineers have successfully used discrete event simulation (DES) to determine the optimal fleet size in mines (Michalakopoulos, Roumpos, Galetakis, & Panagiotou, 2015; Awuah-Offei, Osei, & Askari-Nasab, 2011; Ben-Awuah, Kalantari, Pourrahimian, & Askari-Nasab, 2010; Govinda Raj, Vardhan, & Rao, 2009; Yuriy & Vayenas, 2008; Kwame Awuah-Offei, Temeng, & Al-Hassan, 2003). In most cases, the published work on this topic involves determining the number of hauling units to be matched to a loading unit. Many of these examples involve truck-shovel optimization. There are, however, some underground mining examples. For instance, we recently concluded work that involves fleet optimization in underground room and pillar (R&P) coal mining (Awuah-Offei & Anani, 2015). Generally, when DES is used in fleet optimization it involves three steps: (1) Build a valid model of the mining system capable of predicting the required outputs with varying fleet composition; (2) Design and conduct experiments that vary the fleet composition across all feasible options; and (3) Determine the optimal fleet based on the results of the simulation experiments. A major decision in the first step is which outputs should be modeled. Some examples in the literature include productivity, unit cost, loader utilization, queue length, and hauler waiting times (Awuah-Offei et al., 2011; Govinda Raj et al., 2009; Kwame Awuah-Offei et al., 2003). Often, more than one of these outputs are simulated and considered as the objective function of the optimization."