With the emergence of the concept of green manufacturing, more manufacturers have attached importance to energy consumption indicators. The process planning and shop scheduling procedures involved in manufacturing processes can both independently achieve energy savings, however independent optimization approaches limit the optimization space. In order to achieve a better optimization effect, the optimization of energy savings for integrated process planning and scheduling (IPPS) was studied in this paper. A mathematical model for multi-objective optimization of IPPS was established to minimize the total energy consumption, makespan, and peak power of the job shop. A hierarchical multi-strategy genetic algorithm based on non-dominated sorting (NSHMSGA) was proposed to solve the problem. This algorithm was based on the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) framework, in which an improved hierarchical coding method is used, containing a variety of genetic operators with different strategies, and in which a population degradation mechanism based on crowding distance is adopted. The results from the case study in this paper showed that the proposed method reduced the energy consumption by approximately 15% for two different scheduling schemes with the same makespan. The computational results for NSHMSGA and NSGA-Ⅱ approaches were evaluated quantitatively in the case study. The C-metric values for NSHMSGA and NSGA-Ⅱ were 0.78 and 0, the spacing metric values were 0.4724 and 0.5775, and the maximum spread values were 1.6404 and 1.3351, respectively. The evaluation indexes showed that the NSHMSGA approach could obtain a better non-dominated solution set than the NSGA-Ⅱ approach in order to solve the multi-objective IPPS problem proposed in this paper.

Industry consumes approximately half of the total worldwide energy usage. With the increasingly rising energy costs in recent years, it is critically important to consider one of the most widely used energies, electricity, during the production planning process. We propose a new mathematical model that can determine efficient scheduling to minimize the makespan and electricity consumption cost (ECC) for the flexible job shop scheduling problem (FJSSP) under a time‐of‐use (TOU) policy. In addition to the traditional two subtasks in FJSSP, a new subtask called speed selection, which represents the selection of variable operating speeds, is added. Then, a modified biogeography‐based optimization (MBBO) algorithm combined with variable neighborhood search (VNS) is proposed to solve the biobjective problem. Experiments are performed to verify the effectiveness of the proposed MBBO algorithm for obtaining an improved scheduling solution compared to the basic biogeography‐based optimization (BBO) algorithm, genetic algorithm (GA), and harmony search (HS).

Process planning is a typical combinatorial optimization problem. When the scale of the problem increases, combinatorial explosion occurs, which makes it difficult for traditional precise algorithms to solve the problem. A hybrid particle swarm optimization (HPSO) algorithm is proposed in this paper to solve problems of process planning. A hierarchical coding method including operation layer, machine layer and logic layer is designed in this algorithm. Each layer of coding corresponds to the decision of a sub-problem of process planning. Several genetic operators of the genetic algorithm are designed to replace the update formula of particle position and velocity in the particle swarm optimization algorithm. The results of the benchmark example in case study show that the algorithm proposed in this paper has better performance.

To successfully implement open innovation, it is essential for firms to build close relationships with external innovators. Although many studies have suggested that relational governance, also known as social control, can effectively manage inter-organizational relationships, the role of relational governance on firms’ open innovation remains equivocal. Using a survey of 318 manufacturing firms from China, this study used a conditional process model to analyze the interplay of relational governance, open innovation, and firms’ innovation performance and examine the influence of environmental dynamism. The results demonstrate that relational governance has a positive impact on firms’ open innovation (both inbound and outbound) and innovation performance. We also found that open innovation mediates the association between relational governance and firms’ innovation performance. A high level of environmental dynamism enhances the indirect effect of relational governance on firms’ innovation performance through open innovation. These findings help clarify the interaction between relational governance and open innovation. They also deepen understanding of the value co-creation principle of open innovation.