The problem of designing pipeless batch plants is tackled using a multiclass re-entrant queueing network model. The overall design procedure is divided into three hierarchical phases: the preliminary phase, neighbor search algorithm (NSA), and greedy mean value analysis (GMVA). In the preliminary phase, the types of products and stations, as well as the capacities of equipment items, are determined. GMVA, which is a generalization of conventional MVA, finds the optimal job constitution for the system within a short computer processing unit (CPU) time, after the station configuration is fixed, whereas the station configuration is successively improved by NSA. The moderate time complexity of the proposed method extends the range of applications to practical-sized problems. An alternative design approach that is based on the decomposition approximation and formulated as a mixed integer nonlinear programming (MINLP) problem is also developed. The effectiveness of the method is illustrated through the examples of mediumsized pipeless plant design problems. Simulation studies using a commercial simulation package are described and observed to support the validity of the analytic method.
The key issue of power systems is to match power demand and supply with the minimum gap and delay. In the steelmaking industry that is well-known for intensive power demand, multiple types of power supply sources are thus prepared. One is to use gas turbine generators consuming byproduct gases, another is to purchase power from external electricity companies, and yet another is to use in-house self-generators to prevent any interruptions. Because we have to prepare the power supply before demands are actually realized, the fuels for generators should be purchased based upon a relatively long-term plan, and redundant power should be consumed by being resold or in other ways. It is economically important to predict power load accurately for the profitability of the steelworks. A load prediction model is therefore mathematically formulated as a linear programming (LP) problem with a view to minimizing the overall power cost. A case of an actual steelmaking company in Korea is addressed to illustrate the applicability of the proposed model with some remarks.
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