2022
DOI: 10.1177/09544054221123470
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Self-reconfiguration and rescheduling of aero-engine assembly shop with rework disruption in knowledgeable manufacturing environment

Abstract: An aero-engine has to be assembled twice or more to satisfy stringent technical standards. Substandard engines are required to re-enter for additional reassembly, which may trigger rework disruption. Work groups with flexible skills are responsible for the operations and can be reassigned when rescheduling triggers. An integrated problem of rescheduling of engines and reconfiguration of work groups with rework disruption is formulated here. A heuristic is proposed to solve the problem. In self-reconfiguration … Show more

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“…In recent years, various types of job shop scheduling problems, considering job reworking, have received increasing attention. Yan et al [13] established an integrated optimization model for engine rescheduling and work group reconstruction with reworking interruption and proposed a heuristic algorithm to optimize the rescheduling process through local optimal sorting and a new neighborhood structure search. Using the Markov method of cost and success probability distribution, Mahmoud et al [14] proposed an artificial-intelligenceassisted method to optimize tolerance distributions and effectively reduce reworking costs.…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, various types of job shop scheduling problems, considering job reworking, have received increasing attention. Yan et al [13] established an integrated optimization model for engine rescheduling and work group reconstruction with reworking interruption and proposed a heuristic algorithm to optimize the rescheduling process through local optimal sorting and a new neighborhood structure search. Using the Markov method of cost and success probability distribution, Mahmoud et al [14] proposed an artificial-intelligenceassisted method to optimize tolerance distributions and effectively reduce reworking costs.…”
Section: Introductionmentioning
confidence: 99%