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A two-level hierarchical simulation-based framework is proposed for real-time planning in one of the largest coal mines in the world. At the coal mine, various decisions (e.g. truck locks, hopper-silo connections and silo blend values) have to be made to ship coal to customers via trains. To resolve machinery scheduling and train loading problems in an integrated manner, mathematical formulations are developed and embedded within the proposed hierarchical framework. At the upper level, the coal flow in the simulation model is directly from pits to trains and in the lower level a full simulation model of the coal mine is used for simulating the flow of coal from pits to hoppers via trucks, then from hoppers to silos and silos to loadouts via conveyors. In this work, Arena software is used for the simulation model: it retrieves real-time status and historical performance data from Microsoft SQL Server situated remotely at the coal mine. OptQuest software is used to resolve optimization problems. Finally, two types of experiments are conducted to illustrate the performance of the proposed framework for the actual coal mine. Firstly, the bounds of the constraints in the upper level are varied to study the behavior of the total revenue in shift and revenue by train. Secondly, the effects of increasing variations in truck travel times, loading and dumping rates on machine utilization are studied at the lower level.

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This paper presents a comprehensive framework for the analysis of the impact of information sharing in hierarchical decision-making in manufacturing supply chains. In this framework, the process plan selection and real-time resource allocation problems are formulated as hierarchical optimization problems, where problems at each level in the hierarchy are solved by separate multi-objective genetic algorithms. The considered multi-objective genetic algorithms generate near optimal solutions for NP-hard problems with less computational complexity. In this work, a four-level hierarchical decision structure is considered, where the decision levels are defined as enterprise level, shop level, cell level, and equipment level. Using this framework, the sources of information affecting the achievement of best possible decisions are then identified at each of these levels, and the extent of their effects from sharing them are analyzed in terms of the axis, degree and the content of information. The generality and validity of the proposed approach have been successfully tested for diverse manufacturing systems generated from a designed experiment.

A robust simulation-based optimization approach is proposed for truck-shovel systems in surface coal mines to maximize the expected value of revenue obtained from customer trains. To this end, a large surface coal mine in North America is considered as case study, and a highly detailed simulation model of that mine is constructed in Arena. Factors encountered in material handling operations that may affect the robustness of revenue are then classified into 1) controllable, 2) uncontrollable and 3) constant categories. Historical production data of the mine is used to derive probability distributions for the uncontrollable factors. Then, Response Surface Methodology is applied to derive an expression for the variance of revenue under the influence of controllable and uncontrollable factors. The resulting variance expression is applied as a constraint to the mathematical formulation for optimization using OptQuest. Finally, coal production is observed under variation in number of trucks and down events.