Abstract
We present a computationally tractable framework for decentralized, yet coordinated, management and control of supply chains (SCs) capturing the salient dynamics of production systems. The SC is modeled as a network of super nodes representing production facilities. Each facility is modeled as a stochastic queueing network of workstations operated by a general material flow/routing policy. Each facility's stochastic dynamics are fast (they vary hourly) relative to the planning horizon's deterministic dynamics (they vary weekly). This enables a time scale based decomposition to assign facility-specific performance and sensitivity evaluation tasks to a decentralized sub-problem layer, while SC production planning is assigned to a centralized deterministic mathematical programming layer. We optimize weekly production schedules that minimize inventory and backlog costs subject to non-linear constraints on production imposed by weekly varying dynamic lead-times and inter-facility quality of service driven inventory hedging policies. Extensive computational experience demonstrates significantly faster supply chain velocity relative to static lead-time state of the art industry practices.
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