Publikationen

Supply chain management in chemical process industry focuses onproduction planning and scheduling to reduce production cost and inventories andsimultaneously increase the utilization of production capacities and the service level.These objectives and the specific characteristics of chemical production processesresult in complex planning problems. To handle this complexity, advanced planningsystems (APS) are implemented and often enhanced by tailor-made optimization algo-rithms. In this article, we focus on a real-world problem of production planning arisingfrom a specialty chemicals plant. Formulations for finished products comprise severalproduction and refinement processes which result in all types of material flows. Mostprocesses cannot be operated on only one multi-purpose facility, but on a choice ofdifferent facilities. Due to sequence dependencies, several batches of identical pro-cesses are grouped together to form production campaigns. We describe a methodfor multicriteria optimization of short- and mid-term production campaign schedulingwhich is based on a time-continuous MILP formulation. In a preparatory step, deter-ministic algorithms calculate the structures of the formulations and solve the bills ofmaterial for each primary demand. The facility selection for each production campaignis done in a first MILP step. Optimized campaign scheduling is performed in a secondstep, which again is based on MILP. We show how this method can be successfullyadapted to compute optimized schedules even for problem instances of real-worldsize, and we furthermore outline implementation issues including integration with anAPS.

We consider a complex scheduling problem in the chemical process industry involving batch production. The application described comprises a network of production plants with interdependent production schedules, multi-stage production at multi-purpose facilities, and chain production. The paper addresses three distinct aspects: (i) a scheduling solution obtained from a genetic algorithm (GA) based optimizer, (ii) a mechanism for collaborative planning among the involved plants, and (iii) a tool for manual updates and schedule changes. The tailor made optimization algorithm simultaneously considers alternative production paths and facility selection as well as product and resource specific parameters such as batch sizes, and setup and cleanup times. The collaborative planning concept allows all the plants to work simultaneously as partners in a supply chain resulting in higher transparency, greater flexibility, and reduced response time as a whole. The user interface supports monitoring production schedules graphically and provides custom-built utilities for manual changes to the production schedule, investigation of various what-if scenarios, and marketing queries.

This paper considers a complex scheduling problem in the chemical process industry involving batch production. The application described comprises a network of production plants with interdependent production schedules, multi-stage production at multi-purpose facilities, and chain production. The paper addresses three distinct aspects: (i) a scheduling solution obtained from a genetic algorithm based optimizer, (ii) a mechanism for collaborative planning among the involved plants, and (iii) a tool for manual updates and schedule changes. The tailor made optimization algorithm simultaneously considers alternative production paths and facility selection as well as product and resource specific parameters such as batch sizes, and setup and cleanup times. The collaborative planning concept allows all the plants to work simultaneously as partners in a supply chain resulting in higher transparency, greater flexibility, and reduced response time as a whole. The user interface supports monitoring production schedules graphically and provides custom-built utilities for manual changes to the production schedule, investigation of various what-if scenarios, and marketing queries.