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Erhan Kutanoglu - One of the best experts on this subject based on the ideXlab platform.
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Integrating preventive Maintenance Planning and production scheduling for a single machine
IEEE Transactions on Reliability, 2005Co-Authors: C. Richard Cassady, Erhan KutanogluAbstract:Preventive Maintenance Planning, and production scheduling are two activities that are inter-dependent but most often performed independently. Considering that preventive Maintenance, and repair affect both available production time, and the probability of machine failure, we are surprised that this inter-dependency seems to be overlooked in the literature. We propose an integrated model that coordinates preventive Maintenance Planning decisions with single-machine scheduling decisions so that the total expected weighted completion time of jobs is minimized. Note that the machine of interest is subject to minimal repair upon failure, and can be renewed by preventive Maintenance. We investigate the value of integrating production scheduling with preventive Maintenance Planning by conducting an extensive experimental study using small scheduling problems. We compare the performance of the integrated solution with the solutions obtained from solving the preventive Maintenance Planning, and job scheduling problems independently. For the problems studied, integrating the two decision-making processes resulted in an average improvement of approximately 2% and occasional improvements of as much as 20%. Depending on the nature of the manufacturing system, an average savings of 2% may be significant. Certainly, savings in this range indicate that integrated preventive Maintenance Planning, and production scheduling should be focused on critical (bottleneck) machines. Because we use total enumeration to solve the integrated model for small problems, we propose a heuristic approach for solving larger problems. Our analysis is based on minimizing total weighted completion time; thus, both the scheduling, and Maintenance problems favor processing shorter jobs in the beginning of the schedule. Given that due-date-based objectives, such as minimizing total weighted job tardiness, present more apparent trade-offs & conflicts between preventive Maintenance Planning, and job scheduling, we believe that integrated preventive Maintenance Planning & production scheduling is a worthwhile area of study.
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Integrating preventive Maintenance Planning and production scheduling for a single machine
IEEE Transactions on Reliability, 2005Co-Authors: C. Richard Cassady, Erhan KutanogluAbstract:Preventive Maintenance Planning, and production scheduling are two activities that are inter-dependent but most often performed independently. Considering that preventive Maintenance, and repair affect both available production time, and the probability of machine failure, we are surprised that this inter-dependency seems to be overlooked in the literature. We propose an integrated model that coordinates preventive Maintenance Planning decisions with single-machine scheduling decisions so that the total expected weighted completion time of jobs is minimized. Note that the machine of interest is subject to minimal repair upon failure, and can be renewed by preventive Maintenance. We investigate the value of integrating production scheduling with preventive Maintenance Planning by conducting an extensive experimental study using small scheduling problems. We compare the performance of the integrated solution with the solutions obtained from solving the preventive Maintenance Planning, and job scheduling problems independently. For the problems studied, integrating the two decision-making processes resulted in an average improvement of approximately 2% and occasional improvements of as much as 20%. Depending on the nature of the manufacturing system, an average savings of 2% may be significant. Certainly, savings in this range indicate that integrated preventive Maintenance Planning, and production scheduling should be focused on critical (bottleneck) machines. Because we use total enumeration to solve the integrated model for small problems, we propose a heuristic approach for solving larger problems. Our analysis is based on minimizing total weighted completion time; thus, both the scheduling, and Maintenance problems favor processing shorter jobs in the beginning of the schedule. Given that due-date-based objectives, such as minimizing total weighted job tardiness, present more apparent trade-offs & conflicts between preventive Maintenance Planning, and job scheduling, we believe that integrated preventive Maintenance Planning & production scheduling is a worthwhile area of study. © 2005 IEEE.
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Minimizing job tardiness using integrated preventive Maintenance Planning and production scheduling
IIE Transactions (Institute of Industrial Engineers), 2003Co-Authors: C. Richard Cassady, Erhan KutanogluAbstract:Production scheduling and preventive Maintenance Planning decisions are inter-dependent but most often made independently. Given that Maintenance affects available production time and elapsed production time affects the probability of machine failure, this interdependency seems to be overlooked in the literature. We propose an integrated model that simultaneously determines production scheduling and preventive Maintenance Planning decisions so that the total weighted tardiness of jobs is minimized. We investigate the benefits of integration through a numerical study of small problems. We compare the integrated solution and its performance with the solutions obtained from solving the production scheduling and preventive Maintenance Planning problems independently. The numerical results show an average reduction of 30% in expected total weighted tardiness. Finally, we discuss the issues related to solving larger problems and extensions for future study.
C. Richard Cassady - One of the best experts on this subject based on the ideXlab platform.
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Integrating preventive Maintenance Planning and production scheduling for a single machine
IEEE Transactions on Reliability, 2005Co-Authors: C. Richard Cassady, Erhan KutanogluAbstract:Preventive Maintenance Planning, and production scheduling are two activities that are inter-dependent but most often performed independently. Considering that preventive Maintenance, and repair affect both available production time, and the probability of machine failure, we are surprised that this inter-dependency seems to be overlooked in the literature. We propose an integrated model that coordinates preventive Maintenance Planning decisions with single-machine scheduling decisions so that the total expected weighted completion time of jobs is minimized. Note that the machine of interest is subject to minimal repair upon failure, and can be renewed by preventive Maintenance. We investigate the value of integrating production scheduling with preventive Maintenance Planning by conducting an extensive experimental study using small scheduling problems. We compare the performance of the integrated solution with the solutions obtained from solving the preventive Maintenance Planning, and job scheduling problems independently. For the problems studied, integrating the two decision-making processes resulted in an average improvement of approximately 2% and occasional improvements of as much as 20%. Depending on the nature of the manufacturing system, an average savings of 2% may be significant. Certainly, savings in this range indicate that integrated preventive Maintenance Planning, and production scheduling should be focused on critical (bottleneck) machines. Because we use total enumeration to solve the integrated model for small problems, we propose a heuristic approach for solving larger problems. Our analysis is based on minimizing total weighted completion time; thus, both the scheduling, and Maintenance problems favor processing shorter jobs in the beginning of the schedule. Given that due-date-based objectives, such as minimizing total weighted job tardiness, present more apparent trade-offs & conflicts between preventive Maintenance Planning, and job scheduling, we believe that integrated preventive Maintenance Planning & production scheduling is a worthwhile area of study.
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genetic algorithms for integrated preventive Maintenance Planning and production scheduling for a single machine
Computers in Industry, 2005Co-Authors: N Sortrakul, Heather Nachtmann, C. Richard CassadyAbstract:Despite the inter-dependent relationship between them, production scheduling and preventive Maintenance Planning decisions are generally analyzed and executed independently in real manufacturing systems. This practice is also found in the majority of the studies found in the relevant literature. In this paper, heuristics based on genetic algorithms are developed to solve an integrated optimization model for production scheduling and preventive Maintenance Planning. The numerical results on several problem sizes indicate that the proposed genetic algorithms are very efficient for optimizing the integrated problem.
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Integrating preventive Maintenance Planning and production scheduling for a single machine
IEEE Transactions on Reliability, 2005Co-Authors: C. Richard Cassady, Erhan KutanogluAbstract:Preventive Maintenance Planning, and production scheduling are two activities that are inter-dependent but most often performed independently. Considering that preventive Maintenance, and repair affect both available production time, and the probability of machine failure, we are surprised that this inter-dependency seems to be overlooked in the literature. We propose an integrated model that coordinates preventive Maintenance Planning decisions with single-machine scheduling decisions so that the total expected weighted completion time of jobs is minimized. Note that the machine of interest is subject to minimal repair upon failure, and can be renewed by preventive Maintenance. We investigate the value of integrating production scheduling with preventive Maintenance Planning by conducting an extensive experimental study using small scheduling problems. We compare the performance of the integrated solution with the solutions obtained from solving the preventive Maintenance Planning, and job scheduling problems independently. For the problems studied, integrating the two decision-making processes resulted in an average improvement of approximately 2% and occasional improvements of as much as 20%. Depending on the nature of the manufacturing system, an average savings of 2% may be significant. Certainly, savings in this range indicate that integrated preventive Maintenance Planning, and production scheduling should be focused on critical (bottleneck) machines. Because we use total enumeration to solve the integrated model for small problems, we propose a heuristic approach for solving larger problems. Our analysis is based on minimizing total weighted completion time; thus, both the scheduling, and Maintenance problems favor processing shorter jobs in the beginning of the schedule. Given that due-date-based objectives, such as minimizing total weighted job tardiness, present more apparent trade-offs & conflicts between preventive Maintenance Planning, and job scheduling, we believe that integrated preventive Maintenance Planning & production scheduling is a worthwhile area of study. © 2005 IEEE.
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Minimizing job tardiness using integrated preventive Maintenance Planning and production scheduling
IIE Transactions (Institute of Industrial Engineers), 2003Co-Authors: C. Richard Cassady, Erhan KutanogluAbstract:Production scheduling and preventive Maintenance Planning decisions are inter-dependent but most often made independently. Given that Maintenance affects available production time and elapsed production time affects the probability of machine failure, this interdependency seems to be overlooked in the literature. We propose an integrated model that simultaneously determines production scheduling and preventive Maintenance Planning decisions so that the total weighted tardiness of jobs is minimized. We investigate the benefits of integration through a numerical study of small problems. We compare the integrated solution and its performance with the solutions obtained from solving the production scheduling and preventive Maintenance Planning problems independently. The numerical results show an average reduction of 30% in expected total weighted tardiness. Finally, we discuss the issues related to solving larger problems and extensions for future study.
Chunming Ye - One of the best experts on this subject based on the ideXlab platform.
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Single-machine-based joint optimization of predictive Maintenance Planning and production scheduling
Robotics and Computer-Integrated Manufacturing, 2019Co-Authors: Qinming Liu, Wenyuan Lv, F.f. Chen, Ming Dong, Chunming YeAbstract:Maintenance Planning and production scheduling are two activities that are inter-dependent but most often performed independently in manufacturing. The Maintenance Planning affects both available production time and failure probability. However, in previous research, the Maintenance Planning only considers preventive Maintenance and may result in Maintenance shortage or overage. And the deterioration and health status of machines from prognostics are often ignored. The paper presents an integrated decision model that coordinates predictive Maintenance decisions based on prognostics information with a single-machine scheduling decisions so that the total expected cost is minimized. In the integrated model, the health status and dummy age subjected to machine degradation is considered. Finally, a case study is used to demonstrate the value of the proposed methods. And the performance of the integrated solution is compared with solutions obtained from solving the predictive Maintenance Planning and production scheduling problems independently. The results prove its efficiency.
Travis S Waller - One of the best experts on this subject based on the ideXlab platform.
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optimal long term infrastructure Maintenance Planning accounting for traffic dynamics
Computer-aided Civil and Infrastructure Engineering, 2009Co-Authors: Manwo Ng, Travis S WallerAbstract:Periodic infrastructure Maintenance is crucial for the operation of safe and efficient transportation systems. Numerous decision models for the Maintenance Planning problem have been proposed in the literature. However, to the best of the authors' knowledge, no model exists that simultaneously accounts for traffic dynamics and is intended for long-term Planning purposes. This article addresses this gap in the literature. A mixed-integer bi-level program is introduced that minimizes the long-term Maintenance cost as well as the total system travel time. For the solution approach, a genetic algorithm is utilized in conjunction with mesoscopic traffic simulation. The model is illustrated via a numerical example.
Dan M. Frangopol - One of the best experts on this subject based on the ideXlab platform.
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generalized probabilistic framework for optimum inspection and Maintenance Planning
Journal of Structural Engineering-asce, 2013Co-Authors: Sunyong Kim, Dan M. Frangopol, Mohamed SolimanAbstract:This paper proposes a generalized probabilistic framework for optimum inspection and Maintenance Planning of deteriorating structures. The proposed framework covers (1) the damage occurrence and propagation and service life prediction under uncertainty, (2) the relation between degree of damage and probability of damage detection of an inspection method, and (3) the effects of inspection and Maintenance on service life and life-cycle cost. Optimum inspection and Maintenance types and times are obtained through an optimization formulation by maximizing the expected service life and minimizing the expected total life-cycle cost consisting of inspection and Maintenance costs. The service life, life-cycle cost, and Maintenance delay, along with inspection and Maintenance actions, are formulated using a decision tree model. The selection of the appropriate Maintenance type depends on the degree of damage. The proposed framework is general and can be applied to any types of deteriorating structures or materials. Applications of the proposed framework may include, but are not limited to, bridges, buildings, aircrafts, and naval ships.
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probabilistic service life assessment and Maintenance Planning of concrete structures
Journal of Structural Engineering-asce, 2006Co-Authors: Fabio Biondini, Dan M. Frangopol, Franco Bontempi, Pier Giorgio MalerbaAbstract:This paper presents a general approach to the probabilistic prediction of the structural service life and to the Maintenance Planning of deteriorating concrete structures. The proposed formulation is based on a novel methodology for the assessment of the time-variant structural performance under the diffusive attack of external aggressive agents. Based on this methodology, Monte Carlo simulation is used to account for the randomness of the main structural parameters, including material properties, geometrical parameters, area and location of the reinforcement, material diffusivity and damage rates. The time-variant reliability is then computed with respect to proper measures of structural performance. The results of the lifetime durability analysis are finally used to select, among different Maintenance scenarios, the most economical rehabilitation strategy leading to a prescribed target value of the structural service life. Two numerical applications, a box-girder bridge deck and a pier of an existing bridge, show the effectiveness of the proposed methodology.
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multiobjective Maintenance Planning optimization for deteriorating bridges considering condition safety and life cycle cost
Journal of Structural Engineering-asce, 2005Co-Authors: Min Liu, Dan M. FrangopolAbstract:Many of the currently available bridge management system tools focus on minimizing life-cycle Maintenance cost of deteriorating bridges while imposing constraints on structural performance. The computed single optimal Maintenance Planning solution, however, may not necessarily meet a bridge manager's specific requirements on lifetime bridge performance. In this paper the life-cycle Maintenance Planning of deteriorating bridges is formulated as a multiobjective optimization problem that treats the lifetime condition and safety levels as well as life-cycle Maintenance cost as separate objective functions. A multiobjective genetic algorithm is used as the search engine to automatically locate a large pool of different Maintenance scenarios that exhibits an optimized tradeoff among conflicting objectives. This tradeoff provides improved opportunity for bridge managers to actively select the final Maintenance scenario that most desirably balances life-cycle Maintenance cost, condition, and safety levels of deteriorating bridges.
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Maintenance Planning of Deteriorating Bridges by Using Multiobjective Optimization
Transportation Research Record, 2005Co-Authors: Min Liu, Dan M. FrangopolAbstract:Cost-effective bridge Maintenance Planning requires balanced consideration of long-term bridge performance and life-cycle Maintenance cost. Many of the existing methodologies determine an optimal Maintenance Planning solution based solely on life-cycle cost minimization while enforcing constraints on bridge performance. The resulting single Planning solution, however, may not always satisfy bridge managers' specific requirements for bridge performance over an intended time horizon. In response, the life-cycle Maintenance Planning of deteriorating bridges is formulated as a multiobjective optimization problem and is solved by a genetic algorithm. The visual inspection-based condition state, structural assessment-based safety state, and cumulative life-cycle Maintenance cost are all treated as competing criteria. A group of different Maintenance strategies is considered. A multilinear computational model is adopted to predict time-varying deterioration processes under no-Maintenance and Maintenance interven...
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optimal bridge Maintenance Planning based on probabilistic performance prediction
Engineering Structures, 2004Co-Authors: Min Liu, Dan M. FrangopolAbstract:Current automated Maintenance Planning procedures for deteriorating bridges are usually based on deterministic prediction of bridge performance and whole-life Maintenance costing. In these procedures, uncertainties associated with the deterioration process under no Maintenance and under Maintenance are not taken into consideration. In this paper, such uncertainties are confined to the parameters that define the selected computational models and their effects are evaluated by means of Monte Carlo simulations. A multiobjective genetic algorithm based numerical procedure is used to locate, in the Pareto optimal sense, the best possible tradeoff Maintenance Planning solutions with respect to three objective functions, namely, condition index, safety index, and cumulative life-cycle Maintenance cost. By computing these objectives in terms of either sample mean or sample percentile values, bridge managers’ specific confidence levels on the performance of Maintenance solutions can therefore be conveniently incorporated into the optimization process.
