Multi-Objective Energy-Efficient Interval Scheduling in Hybrid Flow Shop Using Imperialist Competitive Algorithm

Energy-efficient hybrid flow shop scheduling problem has attracted much attention in deterministic case; however, uncertainty is seldom considered in previous works. In this paper, energy-efficient interval hybrid flow shop scheduling problem (EIHFSP) is investigated, and a new imperialist competitive algorithm with empire grouping (EGICA) is proposed to minimize total energy consumption and make span simultaneously. Groups of empires are obtained by defining normalized cost and normalized total cost in interval case, constructing initial empires, and grouping all empires. Assimilation is implemented in a new way, and an adaptive revolution is adopted in each group. Two-phase imperialist competition is newly proposed, and an adaptive search of member from archive is adopted. A number of computational experiments are conducted. The computational results demonstrate that the EGICA has promising advantages on solving the EIHFSP.