Capacity-constrained production scheduling of multiple vehicle programs in an automotive pilot plant

This paper describes a simulation-based approach for scheduling a simultaneously run mix of multiple vehicle programs in an automotive pilot plant with multiple operational constraints. Pilot plants often operate with relatively long and highly variable cycle times, equipment failures and labour absenteeism, dedication schemes of assembly resources, frequent engineering changes and sign-offs, low inventory levels, and long sequence-dependent setups (programs ramp-up and changeover). Such characteristics often lead a stochastic process response and result in significant capacity limitations and scheduling inefficiencies. Hence, an approach is presented in this paper to resolve capacity complications in the pilot plant and to increase the efficiency of the production schedule. Because of the organic capacity–schedule relationship, the proposed approach is referred to as capacity-constrained production scheduling (CPS). Discrete event simulation (DES) is utilised to determine the plant's effective production capacity under the plant operational constraints and stochastic running conditions. A set of model-generated CPS measures with critical capacity and scheduling indications are defined to quantify potential imbalances between the plant's effective capacity and the demand-based schedule capacity. The plant capacity is improved systematically by elevating plant constraints through the application of a simulation-based theory of constraints (TOC) method. A resource reallocation algorithm (RRA) is applied to enhance the efficiency of the production schedule in terms of delivery shortages, idle time, makespan, and tardiness. An automotive pilot plant case study is presented to clarify the application and the benefits of the proposed approach with a focus on the details of the practical application of TOC and RRA.