Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance

2021 ◽  
Vol 61 ◽  
pp. 423-449
Author(s):  
Mageed Ghaleb ◽  
Sharareh Taghipour ◽  
Hossein Zolfagharinia
Keyword(s):  
Real Time ◽  
Production Scheduling ◽  
Job Shop ◽  
Condition Based Maintenance ◽  
Flexible Job Shop ◽  
Maintenance Planning ◽  
Integrated Production ◽  
Machine Deterioration

scholarly journals Multiobjective Joint Optimization of Production Scheduling and Maintenance Planning in the Flexible Job-Shop Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jianfei Ye ◽  
Huimin Ma
Keyword(s):  
Decision Making ◽  
Optimization Model ◽  
Preventive Maintenance ◽  
Production Scheduling ◽  
Job Shop ◽  
Joint Optimization ◽  
Planning Problem ◽  
Flexible Job Shop ◽  
Maintenance Planning ◽  
Joint Decision

In order to solve the joint optimization of production scheduling and maintenance planning problem in the flexible job-shop, a multiobjective joint optimization model considering the maximum completion time and maintenance costs per unit time is established based on the concept of flexible job-shop and preventive maintenance. A weighted sum method is adopted to eliminate the index dimension. In addition, a double-coded genetic algorithm is designed according to the problem characteristics. The best result under the circumstances of joint decision-making is obtained through multiple simulation experiments, which proves the validity of the algorithm. We can prove the superiority of joint optimization model by comparing the result of joint decision-making project with the result of independent decision-making project under fixed preventive maintenance period. This study will enrich and expand the theoretical framework and analytical methods of this problem; it provides a scientific decision analysis method for enterprise to make production plan and maintenance plan.

scholarly journals Predictive maintenance integrated production scheduling by applying deep generative prognostics models: approach, formulation and solution

2021 ◽  
Author(s):  
Simon Zhai ◽  
Meltem Göksu Kandemir ◽  
Gunther Reinhart
Keyword(s):  
Production Scheduling ◽  
Job Shop ◽  
Health Management ◽  
Mathematical Problem ◽  
Health Condition ◽  
Predictive Maintenance ◽  
Full Potential ◽  
Integrated Production ◽  
Operational Conditions ◽  
Simulated Condition

AbstractTo harness the full potential of predictive maintenance (PdM), PdM information has to be used to optimally plan production and maintenance actions. Hence, operation-specific modelling of degradation, i.e. predictions of the health condition under time-varying operational conditions, has to be realized. By utilizing operation-specific degradation information, maintenance and production can be planned with regard to each other and thus, predictive maintenance integrated production scheduling (PdM-IPS) is enabled. This publication proposes a novel PdM-IPS approach consisting of two interacting modules: an operation-specific Prognostics and Health Management (PHM) module and an integrated production scheduling and maintenance planning (IPSMP) module. Specifically, the mathematical problem of the IPSMP module based on an extended version of the maintenance integrated flexible job shop problem is formulated. A two-stage genetic algorithm to efficiently solve this problem is designed and subsequently applied to simulated condition monitoring, as well as real industrial data. Results indicate that the approach is able to find feasible high quality PdM integrated production schedules.

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