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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]

Demander-dominated market scenarios are becoming increasingly common owing to the emergence of alternative service providers and competitive market environment. However, these scenarios have not been considered in previous studies pertaining to decentralized scheduling problems. Thus, service providers cannot formulate optimal production scheduling schemes for such scenarios. In this study, we investigate a demander-dominated decentralized scheduling problem in which the demander can adopt the private-strategic behavior of transferring partial orders towards its alternative service providers. The aim of this study is to provide guidance to service providers for developing high-quality production scheduling solutions under asymmetric information. First, we design a multi-round information interaction mechanism with a learning strategy to realize information interaction. Subsequently, a metaheuristic algorithm termed MAM is developed based on the multi-round information interaction mechanism to solve the proposed problem. A problem-dependent initialization method and a solution generation method integrating the learning strategy are developed to improve the search efficiency. Experimental results indicate the usefulness of the initialization method and learning strategy. Based on a comparison with two well-established adapted algorithms, the effectiveness of the proposed algorithm is confirmed, particularly for instances with loose due dates. Furthermore, we analyze the effect of MAM on both the service provider and the demander by comparing it with traditional centralized approaches. Statistical results show that the proposed algorithm yields high-quality solutions for the service provider and that maintaining the confidentiality of private information is conducive to the demander, particularly when the due dates are tight. [ABSTRACT FROM AUTHOR]