From Morphological Analysis to optimizing complex industrial operation scenarios

Peter A. Haydo

Technological Forecasting and Social Change, 2018, vol. 126, issue C, 147-160

Abstract: This article describes a method to generate, analyze, and optimize complex industrial operation scenarios. The model is based on a hybrid architecture incorporating two phases. The first phase accomplishes information acquisition and representation whereas the second phase imposes an optimization process on the collected datasets to generate an optimum state situation for a given industrial scenario. In the first phase, Morphological Analysis is used for breaking down complex industrial systems into manageable fragments. These provide the basis for knowledge and data acquisition and their representation. Each subset is represented in an individual table and defines one characteristic of the system like all kinds of resources, logistics, tax regulations, etc. In the second phase, the morphological table attributes are considered as variables which are subject to optimization. Values are assigned to all variables targeting a global optimum state. The solution reflects an optimized operation scenario for the relevant industrial organization. In the model, tables are linked by means of an objective function. The applied non-linear optimization process uses a hill-climbing algorithm. Due to certain constraints, all the volume demands at sales locations must be matched with the entire production capacities at production facilities. At the same time, either costs are minimized or profits are maximized. Additional constraints are imposed on the model to define feasible solution spaces. Both Morphological Analysis phase and the optimization processes employed have proved to be feasible and effective.

Keywords: Morphological analysis; Industrial operation scenarios; Optimization; Business scenarios (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:126:y:2018:i:c:p:147-160

DOI: 10.1016/j.techfore.2017.06.009

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