Stochastic customer order scheduling on heterogeneous parallel machines with resource allocation consideration

This study considers a stochastic customer order scheduling and resource allocation problem in an unrelated parallel machine environment. Customer orders dynamically arrive at a machine station, and each consists of multiple product types with random workloads. Speeds of the machines are controllable through the allocation of limited resources such as overtime or dedicated manpower. The objective is to minimize the long run expected order cycle time by optimizing workload schedule and resource allocation. The impacts of workload variance, product similarity and machine speed are evaluated, and several optimal properties are explored. Three heuristic algorithms are proposed based on the theoretical results developed. The effectiveness of the proposed algorithms is demonstrated through extensive numerical experiments. This study brings new perspectives to resource allocation problems in stochastic environment, and provides insights into the relationship between resource allocation decisions and overall production efficiency.