Multi-objective optimization for energy-efficient hybrid flow shop scheduling problem in panel furniture intelligent manufacturing with transportation constraints

Abstract

The manufacture of customized panel furniture today faces significant environmental challenges in terms of energy consumption and the associated impact on the environment. From an operations management perspective, the energy efficiency of production systems is greatly influenced by production scheduling. Therefore, to solve the problem of energy-efficient hybrid flow shop scheduling problem (HFSP) for panel furniture manufacturing, we construct a standard mathematical model to trade-off between makespan and total energy consumption. A hybrid VNS-NSGA-II algorithm is proposed, which combines the variable neighborhood search (VNS) and the non-dominated sorting genetic algorithm II (NSGA-II) based on double chain coding and the greedy insertion method decoding rule, aiming to provide a set of compromise solutions. To evaluate the effectiveness of this algorithm, the performance results are analyzed with other five multi-objective optimization algorithms (MOEA/D, SPEA2, MOPSO, MOSA and AdaW). The VNS-NSGA-II algorithm provides promising results for HFSP in panel furniture manufacturing. In addition, the results of the optimal scheduling scheme obtained through the decision-making method are used to evaluate the performance of the proposed model and algorithm in a real-world panel furniture manufacturing scenario. This may provide valuable insights for furniture companies in developing energy-efficient scheduling management.