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This paper investigates an energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines (HFSP-UPM) with the energy-saving strategy of turning off and on. We first analyse the energy consumption of HFSP-UPM and formulate five mixed integer linear programming (MILP) models based on two different modelling ideas namely idle time and idle energy. All the models are compared both in size and computational complexities. The results show that MILP models based on different modelling ideas vary dramatically in both size and computational complexities. HFSP-UPM is NP-Hard, thus, an improved genetic algorithm (IGA) is proposed. Specifically, a new energy-conscious decoding method is designed in IGA. To evaluate the proposed IGA, comparative experiments of different-sized instances are conducted. The results demonstrate that the IGA is more effective than the genetic algorithm (GA), simulating annealing algorithm (SA) and migrating birds optimisation algorithm (MBO). Compared with the best MILP model, the IGA can get the solution that is close to an optimal solution with the gap of no more than 2.17% for small-scale instances. For large-scale instances, the IGA can get a better solution than the best MILP model within no more than 10% of the running time of the best MILP model.

To enable immediate and efficient emergency scheduling during forest fires, we propose a novel emergency scheduling model for such fires subject to priority disaster areas and limited rescue team resources to minimize the total travel distance for rescue teams. Moreover, a hybrid intelligent algorithm integrating genetic algorithm (GA) and particle swarm optimization (PSO) is adopted to solve the proposed model. A case study is presented to illustrate the proposed model and the effectiveness of the proposed algorithm. The goal of this work is to analyze the emergency scheduling problem of forest fires subject to limited rescue teams and priority disaster areas. Both theoretical and simulation results demonstrate that the proposed model can perform effectively the quantitative analysis of an emergency involving forest fires. Such results can help decision makers to make better judgment when dealing with an emergency involving fires.

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