Energy-efficient flexible flow shop scheduling with worker flexibility

Abstract The classical flexible flow shop scheduling problem (FFSP) only considers machine flexibility. Thus far, the relevant literature has not studied FFSPs with worker flexibility, which is widely seen in practical manufacturing systems. Worker flexibility may greatly affect production efficiency and productivity. Furthermore, with the increase of environmental pollution and energy consumption, manufacturers require innovative methods to improve energy efficiency. In this paper, we propose an energy-efficient FFSP with worker flexibility (EFFSPW), in which the flexibility of machines and workers as well as the processing time, energy consumption and worker cost related factors are considered simultaneously. A hybrid evolutionary algorithm (HEA) is then presented to solve the proposed EFFSPW, where some effective operators and a new variable neighborhood search approach are designed. Comprehensive experiments including 54 benchmark instances of the EFFSPW are carried out, and Taguchi analysis is used to determine the best combination of key parameters for the HEA. Experimental results show that the proposed HEA can obtain better solutions for most of these benchmark instances comparing to two other well-known algorithms, demonstrating its superior performance both on solution quality and computational efficiency.