TY - JOUR KW - Sustainable production KW - Aggregate planning KW - Decision support system KW - Queuing networks KW - Chemical process industry AU - Gerd Hahn AU - Marcus Brandenburg AB - 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. BT - Computers & Operations Research DO - 10.1016/j.cor.2017.12.011 N2 - 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. PY - 2018 SP - 154 EP - 168 T2 - Computers & Operations Research TI - A sustainable aggregate production planning model for the chemical process industry UR - https://www.sciencedirect.com/science/article/pii/S0305054817303118 VL - 94 SN - 0305-0548 ER -