Simulation-based dynamic heuristic for the distributed optimisation of complex multi-objective multi-project multi-resource production processes

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Decision problems in scheduling and capacity planning exist in many sectors of economy and society. They can be found especially in industrial production but also in construction and healthcare. Typical properties of these problems are their complexity and diversity. The development and production of high-quality machinery, equipment and vehicles has the tendency towards a permanent increase of customer specificity and product diversification whilst product life cycles become shorter at the same time. This leads to extremely complex, non-deterministic and dynamic multi-resource production processes which can hardly be planned and controlled optimally in terms of an efficient production. The issue grows more acute particularly at assembly processes for customer-specific heavy machinery since the number of possible sequence variants increases exponentially due to the overlapping of several factors (e.g., precedence relations, process alternatives, trade-offs between resources etc.). In addition, the objective functions (e.g., cost, time, resource utilisation, etc.) vary job-specifically and may change over time even within an order in such multi-objective problems. Experiences from research and practice show that the effective solution of such problems opens an enormous potential. To date, there are no sufficiently powerful and application flexible models, procedures, and solution methods. Neither exact solution methods, heuristics nor meta-heuristics are currently able to solve these problems satisfactorily.With this basic research project, suitable reference process models and solution methods will be explored which are to be transferable to other application areas with similar problem characteristics. Core objective is the development of concepts respectively architectures and the incorporating methods which are able to solve the described complex problems efficiently and effectively within this structure. The conceptual basic components shall be:- A modular optimisation framework consisting of a generator, optimiser, simulator and analyser- An iterative, heuristic initialisation, optimisation and process concept using agent-similar and meta-heuristic functionalitiesThe key methodological objectives are:- The development of methods for the efficient handling of large search spaces- The exploration of a value system as a method for evaluating and comparing solutions produced under consideration of differentiated objectives and constraints.