A subgradient descent algorithm for optimization of initially controllable flow shop systems

Abstract

We consider an optimization problem for deterministic flow shop systems processing identical jobs. The service times are initially controllable; they can only be set before processing the first job, and cannot be altered between processes. We derive some waiting and completion time characteristics for fixed service time flow shop systems, independent of the cost formulation. Exploiting these characteristics, an equivalent convex optimization problem, which is non-differentiable, is derived along with its subgradient descent solution algorithm. This algorithm not only eliminates the need for convex programming solvers but also allows for the solution of larger systems due to its smaller memory requirements. Significant improvements in solution times are also observed in the numerical examples.