Combining the advantages of discrete- and continuous-time scheduling models: Part 2. systematic methods for determining model parameters.

• Framework allows the synergistic integration of continuous and discrete time models. • Systematic methods of determining key model parameters are proposed. • The methods are general can be applied to any discrete-time formulation. • The methods yield speedups of up to 4 orders of magnitude and higher quality solutions. The Discrete-Continuous Algorithm (DCA) is a novel framework that harnesses the strengths of discrete- and continuous-time scheduling formulations (Lee and Maraveloas,2018). Its flexibility in the selection of two user-defined parameters, namely discretization step length (δ) and horizon relaxation (η), can lead to significantly improved computational performance and solution quality. In this paper, we propose systematic methods to determine these parameters. Specifically, we evaluate the parameters based on error evaluation functions and cumulative error functions that consider various aspects of the scheduling instances. Through an extensive computational study, we show that the proposed methods bring up to × 104 speedups, while leading to identical or better solutions in the majority of the instances compared to traditional methods. [ABSTRACT FROM AUTHOR]