A computational framework and solution algorithms for two-stage adaptive robust scheduling of batch manufacturing processes under uncertainty

A novel two-stage adaptive robust optimization (ARO) approach to production scheduling of batch processes under uncertainty is proposed. We first reformulate the deterministic mixed-integer linear programming model of batch scheduling into a two-stage optimization problem. Symmetric uncertainty sets are then introduced to confine the uncertain parameters, and budgets of uncertainty are used to adjust the degree of conservatism. We then apply both the Benders decomposition algorithm and the column-and-constraint generation (C&CG) algorithm to efficiently solve the resulting two-stage ARO problem, which cannot be tackled directly by any existing optimization solvers. Two case studies are considered to demonstrate the applicability of the proposed modeling framework and solution algorithms. The results show that the C&CG algorithm is more computationally efficient than the Benders decomposition algorithm, and the proposed two-stage ARO approach returns 9% higher profits than the conventional robust optimization approach for batch scheduling.