Publikationen

Process industries typically involve complex manufacturing operations and thus require adequate decision support for aggregate production planning (APP). In this paper, we focus on two relevant features of APP in process industry operations: (i) sustainable operations planning involving multiple alternative production modes/routings with specific production-related carbon emission and the social dimension of varying operating rates, (ii) integrated campaign planning with the operational level in order to anticipate production mix/volume/routing decisions on campaign lead times and WIP inventories as well as the impact of variability originating from a stochastic manufacturing environment. We focus on the issue of multi-level chemical production processes and highlight the mutual trade-offs along the triple bottom line concerning economic, environmental and social factors. To this end, production-related carbon emission and overtime working hours are considered as externalized factors as well as internalized ones in terms of resulting costs. A hierarchical decision support tool is presented that combines a deterministic linear programming model and an aggregate stochastic queuing network model. The approach is exemplified at a case example from the chemical industry to illustrate managerial insights and methodological benefits of our approach.

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.