Novel Energy- and Maintenance-Aware Collaborative Scheduling for A Hybrid Flow Shop Based on Dual Memetic Algorithms

Abstract

Limited energy supply and uncertain equipment state increase the complexity of production process. Peak power and maintenance-based demand response facilitates factories to adjust scheduling strategies to actual production circumstances, so that a rise in energy cost penalty and machine breakdown could be avoided without affecting normal production on the shop floor. This letter proposes a mixed integer programming (MIP) model for a hybrid flow shop (HFS) to minimize makespan, with the consideration of machine maintenance plans and peak power consumption constraint. Two paradigms of a dual memetic algorithm (DMA) that combines genetic algorithm with two novel heuristic algorithms are proposed according to the characteristics of the problem. To enable maintenance awareness, a heuristic algorithm for machine maintenance is designed to reorganize current production sequences. To enable peak power awareness, a heuristic algorithm based on chromosome priority sequence is put forward. A case study from benchmarks demonstrates the effectiveness of the proposed model and algorithms. The performance of both paradigms is discussed.