Simulation optimization on order-up-to levels of a complex logistics with nested partitions method

A complex logistics (CL) is a netlike supply chain (SC) logistics system which is always in an uncertain environment, consisting of manufacturers, wholesalers and retailers. This paper describes a simulation optimization tool based on the nested partitions (NP) method, CLSim-NP, developed to configure the optimal order-up-to level of each entity in a CL where customer demand is uncertain. Three types of models are combined in CLSim-NP: (1) CL fuzzy models to get the optimal order-up-to level of each entity in a CL where customer demand is described by fuzzy sets and two inventory control policies, decentralized control and retailer based control, are considered, (2) a nested partitions search model to partition CL order-up-to level configuration space on those recommended by the fuzzy models, and (3) a CL simulation model to evaluate CL performance achieved over time for each CL order-up-to level configuration. The application of CLSim-NP is proved by an illustrative example.