We analyze a problem of dynamic logistics planning given uncertain demands for a multi-location production-inventory system with transportable modular production capacity. In such systems, production modules provide capacity, and can be moved from one location to another to produce stock and satisfy demand. We formulate a dynamic programming model for a planning problem that considers production and inventory decisions, and develop suboptimal lookahead and rollout policies that use value function approximations based on geographic decomposition. Mixed-integer programming formulations are provided for several single-period optimization problems that define these policies. These models generalize a formulation for the single-period newsvendor problem, and in some cases the feasible region polyhedra contain only integer extreme points allowing efficient solution computation. A computational study with stationary demand distributions, which should benefit least from mobile capacity, provides an analysis of the effectiveness of these policies and the value that mobile production capacity provides. For problems with 20 production locations, the best suboptimal policies produce on average 13% savings over fixed capacity allocation policies when the costs of module movement, holding, and backordering are accounted for. Greater savings result when the number of locations increases.
Publication status: Under review; Draft version: 1; Institution: Georgia Institute of Technology, Atlanta, USA; Date: 26 November 2018.