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The distributed manufacturing mode is an important paradigm of networked development in the context of globalization. The integration of process planning and shop scheduling in distributed discrete manufacturing system can fully use the production system's flexibility, which is helpful to further enhance the anti-risk ability of enterprises, improve production efficiency, and reduce production costs. This paper established the mathematical model of the distributed integrated process planning and scheduling (DIPPS) in heterogeneous manufacturing environment and designed a high-dimensional integrated encoding to deal with the coupling problem of multiple sub-problems of the DIPPS. Based on this encoding method, an improved genetic algorithm (IGA) is proposed to solve the DIPPS problem. According to the characteristics of the DIPPS, a hierarchical crossover operator with three sub-crossover operations is designed to expand the search dimension of the IGA. The mutation operators are planned to maintain population variety. Finally, five sets of experiments are planned to test the viability of the suggested approach for resolving the DIPPS problem. The outcomes demonstrate that the IGA algorithm outperforms competing algorithms in various scale jobs and factories. [ABSTRACT FROM AUTHOR]

Research on integrated process planning and scheduling (IPPS) is of great significance to the improvement of the overall quality of machinery manufacturing system. In the actual manufacturing process, the manufacturing system is often accompanied by some unpredictable uncertain disturbance factors, for instance uncertain processing time of jobs and changes of due date, etc. These uncertain disturbance events will ultimately affect production efficiency and customer satisfaction. Consequently, this paper considers the multi-objective IPPS problem with uncertain processing time and uncertain due date. A multi-layer collaborative optimization (MLCO) method is designed for the fuzzy multi-objective IPPS (FMOIPPS) problem, including three layers. For the process planning layer, the basic genetic algorithm is used to provide various near optimal process plans for the process selection system. For the process selection layer, a multi-objective genetic algorithm (MOGA) is designed to optimize the process selection population. A sharing function method is introduced to maintain population diversity. An individual comprehensive evaluation method is introduced to evaluate non-dominated solutions. The crowded distance, fast non-dominated sorting and elite strategy based on NSGAII is adopted in the proposed MOGA. The external archive method is employed to preserve the non-dominated solutions generated during population evolution. For the scheduling layer, a MOGA with a boundary search strategy is proposed. The boundary search strategy is designed to improve the search ability of boundary solutions. Three optimization objectives are minimizing the spread of fuzzy makespan, minimizing fuzzy makespan and maximizing average customer satisfaction simultaneously. The target of scheduling layer is to make scheduling arrangements for the process information obtained by process selection layer. Through mutual cooperation among each layer, guide the overall optimization process, and finally get satisfactory solutions. Different problem examples of various scales are employed to verify feasibility and effectiveness of the MLCO method. The experimental results indicate that the MLCO method can effectively address FMOIPPS problem. [ABSTRACT FROM AUTHOR]

Scheduling optimization of maintenance, repair and operations (MRO) service resources of complex product can help an enterprise improve customer service satisfaction, build value-added products, and enhance the enterprise's market competitiveness. This paper studies a scheduling optimization method for the MRO service resources of complex product. First, the scheduling problem in service resources is analyzed, a mathematical model for the service scheduling problem is given, and three objective functions to be optimized are proposed, which are to reduce customer waiting time, reduce excessive human resources, and maximize the cost performance index of the resources. Then, three separate objectives for optimizing service resource scheduling are analyzed based on three proposed methods which are improved genetic algorithm method, combined weight coefficient optimization method, and multi-objective optimization method based on the nondominated sorting genetic algorithm II (NSGA-II). Finally, we use the three methods to carry out scheduling optimization of MRO service resources in the case of a large vertical mill. According to the analysis and comparison of results, the multi-objective optimization method based on the NSGA-II algorithm has an advantage in the scheduling optimization of complex product MRO service resources. In the engineering application, the service scheduling of complex products for managers provides theoretical basis, and can reduce the loss caused by subjective judgment. [ABSTRACT FROM AUTHOR]

Process planning and scheduling are two major sub-systems in a modern manufacturing system. In traditional manufacturing system, they were regarded as the separate tasks to perform sequentially. However, considering their complementarity, integrating process planning and scheduling can further improve the performance of a manufacturing system. Meanwhile, the multiple objectives are needed to be considered during the realistic decision-making process in a manufacturing system. Based on the above requirements from the real manufacturing system, developing effective methods to deal with the multi-objective integrated process planning and scheduling (MOIPPS) problem becomes more and more important. Therefore, this research proposes a multi-objective genetic algorithm based on immune principle and external archive (MOGA-IE) to solve the MOIPPS problem. In MOGA-IE, the fast non-dominated sorting approach used in NSGA-II is utilized as the fitness assignment scheme and the immune principle is exploited to maintain the diversity of the population and prevent the premature condition. Moreover, the external archive is employed to store and maintain the Pareto solutions during the evolutionary process. Effective genetic operators are also designed for MOIPPS. To test the performance of the proposed algorithm, three different scale instances have been employed. And the proposed method is also compared with other previous algorithms in literature. The results show that the proposed algorithm has achieved good improvement and outperforms the other algorithms. [ABSTRACT FROM AUTHOR]

In the modern manufacturing system, many flexible manufacturing system and NC machines are introduced to improve the production efficiency. Therefore, most parts have a large number of flexible process plans. However, a part can use only one process plan in the manufacturing process. So, the process planning problem has become a crucial problem in the manufacturing environment. It is a combinatorial optimization problem to conduct operations selection and operations sequencing simultaneously with various constraints deriving from the practical workshop environment as well as the parts to be processed. It is a NP-hard problem. In order to solve this problem effectively, this paper proposes a novel modified particle swarm optimization (PSO) algorithm to optimize the process planning problem. To improve the performance of the approach, efficient encoding, updating, and random search methods have been developed. To verify the feasibility and effectiveness of the proposed approach, seven cases have been conducted. The proposed algorithm has also been compared with the genetic algorithm and simulated annealing algorithm. The results show that the proposed modified PSO algorithm can generate satisfactory solutions and outperform other algorithms. [ABSTRACT FROM AUTHOR]