Combined strategic and operational planning – an MILP success story in chemical industry |
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Authors: | Josef Kallrath |
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Affiliation: | (1) BASF-AG, GVC/S (Scientific Computing) - C13, 67056 Ludwigshafen, Germany (e-mail: josef.kallrath@basf-ag.de) , DE;(2) Astronomy Department, University of Florida, Gainesville, FL 32661, USA (e-mail: kallrath@astro.ufl.edu) , US |
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Abstract: | We describe and solve a real world problem in chemical industry which combines operational planning with strategic aspects.
In our simultaneous strategic & operational planning (SSDOP) approach we develop a model based on mixed-integer linear (MILP)
optimization and apply it to a real-world problem; the approach seems to be applicable in many other situations provided that
people in production planning, process development, strategic and financial planning departments cooperate.
The problem is related to the supply chain management of a multi-site production network in which production units are subject
to purchase, opening or shut-down decisions leading to an MILP model based on a time-indexed formulation. Besides the framework
of the SSDOP approach and consistent net present value calculations, this model includes two additional special and original
features: a detailed nonlinear price structure for the raw material purchase model, and a detailed discussion of transport
times with respect to the time discretization scheme involving a probability concept. In a maximizing net profit scenario
the client reports cost saving of several millions US$.
The strategic feature present in the model is analyzed in a consistent framework based on the operational planning model,
and vice versa. The demand driven operational planning part links consistently to and influences the strategic. Since the
results (strategic desicions or designs) have consequences for many years, and depend on demand forecast, raw material availability,
and expected costs or sales prices, resp., a careful sensitivity analysis is necessary showing how stable the decisions might
be wit h respect to these input data. |
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Keywords: | : MILP modelling – Strategic and operational planning – Supply chain – Chemical industry – Transportation |
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