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There are numerous techniques in operations research to analyze coal supply logistical problems. A recent operations research-management science handbook documented 152 topics, including: Age Replacement, Ant Colony, Branch and Bound, Clustering, Consensus Building, Fuzzy Search, Genetic Algorithms, JIT, Linear Programming, Markov, MRP, Risk Analysis, Scenario Analysis, Percolation Theory, Simplex, Spanning Tree, Stakeholder Participation, Queuing, Wardrop Equilibria, Warrant Models, and many other techniques (Cochran et al., 2011). Some of these are procedures for qualitative data more so than techniques (e.g., consensus building), while the field of quantitative data approaches ranges from stochastic forecasting (using probability theory), deterministic linear programming (when constraints are known), to nonlinear goal or search heuristics where infinite or no solutions may be possible. The implications are that one or several of the available techniques may be necessary to solve a complex logistical dilemma.