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Natashia Boland(natashia.bolandisye.gatech.edu) Abstract: The pooling problem is a nonconvex nonlinear programming problem (NLP) with applications in the refining and petrochemical industries, but also the coal mining industry. The problem can be stated as follows: given a set of raw material suppliers (inputs) and qualities of the supplies, find a costminimising way of blending these raw materials in intermediate pools and outputs so as to satisfy requirements on the output qualities. The blending in two stages (in pools and outputs) introduces bilinear constraints. The pooling problem can alternatively be described as a minimum cost network flow problem with additional bilinear constraints to capture the blending of raw materials. In this paper we study a variation of the pooling problem that arises naturally in the coal mining industry. Coal is madetoorder according to customers' desired product qualities. Deviations from these target qualities result in contractually agreed bonuses and penalties. In the pooling problem variation we study, costs are associated with these bonuses and penalties instead of network flows. While in the original pooling problem we have hard bounds on the qualities and unmet demand is penalised in the objective function, in our coal mining variation we have hard demand constraints and deviations from target qualities are penalised. This makes finding a feasible solution easy, while in the pooling problem finding a nontrivial feasible solution that satisfies the quality requirements is already hard. An implication of this is that we are able to solve larger problem instances than those typically studied in the pooling problem literature. To model the coal blending process accurately, we define a timeexpanded network where the intermediate pools represent coal stockpiles over time. Since coal is transported in large quantities, we study the tradeoff between continuous and discretized flows in coal blending, i.e., solving a continuous flow problem where arbitrarily small flows are allowed versus solving a discretized flow problem where flows must be in multiples of some basic unit, e.g. trainloads. We also study two exact mixedinteger linear programming (MILP) linearizations of these mixedinteger nonlinear programs (MINLPs), which can be derived from unary and binary expansions of the flow integrality constraint. Such discretizations are typically studied as approximations to an originally continuous problem, however, in our application, a discretized formulation describes the original problem more accurately than a continuous formulation. The paper is organized as follow: in Section 1, we introduce the pooling problem formally as it is commonly stated in the literature. In Section 2, we formulate the coal supply chain extension of the pooling problem as MINLPs and MILPs. We conclude the paper with Section 3 where we provide computational results for four different formulations which we run for a reallife industry setting. Keywords: coal blending; pooling problem; bilinear programming; nonlinear programming; mixedinteger bilinear programming; mixedinteger nonlinear programming Category 1: Global Optimization (Applications ) Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization ) Category 3: Integer Programming ((Mixed) Integer Nonlinear Programming ) Citation: Download: [PDF] Entry Submitted: 07/23/2015 Modify/Update this entry  
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