Journal
JOURNAL OF MANUFACTURING SYSTEMS
Volume 61, Issue -, Pages 300-310Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jmsy.2021.09.012
Keywords
Distributed manufacturing system; Integrated process planning and scheduling; Mathematical model; Artificial bee colony algorithm
Categories
Funding
- National Key R&D Program of China [2019YFB1704600]
- National Natural Science Foundation of China [51825502, 51775216]
- Program for HUST Academic Frontier Youth Team [2017QYTD04]
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With the development of globalization in economy and manufacturing industry, distributed manufacturing mode has become a hot topic in production research, aiming to efficiently complete processing tasks in different workshops. A novel network-based MILP model for distributed integrated process planning and scheduling, along with a discrete artificial bee colony algorithm (DABC) has been proposed to solve the DIPPS problem effectively.
With the development of the globalization of economy and manufacturing industry, distributed manufacturing mode has become a hot topic in current production research. In the context of distributed manufacturing, one job has different process routes in different workshops because of heterogeneous manufacturing resources and manufacturing environments in each factory. Considering the heterogeneous process planning problems and shop scheduling problems simultaneously can take advantage of the characteristics of distributed factories to finish the processing task well. Thus, a novel network-based mixed-integer linear programming (MILP) model is established for distributed integrated process planning and scheduling problem (DIPPS). The paper designs a new encoding method based on the process network and its OR-nodes, and then proposes a discrete artificial bee colony algorithm (DABC) to solve the DIPPS problem. The proposed DABC can guarantee the feasibility of individuals via specially-designed mapping and switching operations, so that the process precedence constraints contained by the network graph can be satisfied in the entire procedure of the DABC algorithm. Finally, the proposed MILP model is verified and the proposed DABC is tested through some open benchmarks. By comparing with other powerful reported algorithms and obtaining new better solutions, the experiment results prove the effectiveness of the proposed model and DABC algorithm successfully.
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