A novel two-phase decomposition-based algorithm to solve MINLP pipeline scheduling problem

Decomposition-based algorithms have been successfully applied in the literature to solve NP-hard optimization problems. This paper presents an efficient decomposition-based heuristic to solve a new variant of the pipeline scheduling problem in which, besides minimizing the interface and demand shortage, the flow-rate stability of batches is also taken into account. Flow-rate stability has a great impact on the reduction of the energy consumed by pumping, and to the best of our knowledge, it has not been addressed in the continuous-time models of the pipeline scheduling problem. Thus, from the modeling perspective, a new continuous-time mixed-integer nonlinear programming (MINLP) model is developed, and from the solution viewpoint, nonlinear terms are remedied by a decomposition technique. Computational results over real-world case studies and randomly generated instances confirm that the proposed method is able to generate near-optimal solutions within a short amount of time; further, they show that the proposed model can result in more stable flow-rates compared to existing models.

Automated summary

This paper presents an efficient decomposition-based heuristic to solve a new variant of the pipeline scheduling problem, considering the impact of flow-rate stability on energy consumption. By developing a new continuous-time mixed-integer nonlinear programming model and applying decomposition technique, the proposed method is able to generate near-optimal solutions and achieve more stable flow-rates.