Multi-objective modeling for preventive maintenance scheduling in a multiple production line

Ebrahimipour, V.; Najjarbashi, Amirhossein; Sheikhalishahi, Mohammad

doi:10.1007/s10845-013-0766-6

Cited by 60 publications

(20 citation statements)

References 31 publications

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“…They developed a unit-cost cumulative reliability expectation measure to evaluate the extent to which maintaining each individual component benefits the total maintenance cost and system reliability over the operational lifetime. Ebrahimipour et al (2013) developed a multi-objective PM scheduling model in a multiple production line. They defined the reliability of production lines and the costs of maintenance, failure and downtime of a system as multiple objectives, and different thresholds for available manpower, spare part inventory and periods under maintenance were applied.…”

Section: Introductionmentioning

confidence: 99%

Multi-principle preventive maintenance: a design-oriented scheduling study for mechanical systems

Gao

Feng

Tan

2014

J. Zhejiang Univ. Sci. A

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Preventive maintenance (PM) is very important for the safe, efficient, and reliable operation of mechanical systems. This paper focuses on one of the most challenging tasks for PM: PM scheduling. Two basic principles are integrated to support the PM scheduling of mechanical systems: (1) the cost principle, and (2) the reliability principle. These two PM scheduling principles are regarded as conflicting objectives, and the improved strength Pareto evolutionary algorithm is used to find the Pareto-optimal set within which the best compromise solution can be obtained according to fuzzy set theory. Both conceptual and mathematical models of the proposed multi-principle PM scheduling method are explained, and a case study is provided to illustrate the practical application of the new method.

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Section: Introductionmentioning

confidence: 99%

Multi-principle preventive maintenance: a design-oriented scheduling study for mechanical systems

Gao

Feng

Tan

2014

J. Zhejiang Univ. Sci. A

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“…The producing iron castings problem is the motivation for present work in foundry enterprise. In recent years, experts and scholars have put many researches' focus on the scheduling optimization algorithm and proposed some effective methods or models, such as multiobjective evolutionary algorithms [9] (DLP, Deterministic Linear Programming [10], ACO, Ant Colony Optimization [11], ABC, Artificial Bee Colony [12], AIA, Artificial Immune Algorithm [13], UGF, Universal Generation Function [14], and MIP, Mixed-Integer Programming [15]) and decision support optimization and simulation (SLP, System Layout Planning [16], MIND, Method for Analysis of Industrial Energy Systems [17], M&FS, Mass and Fuzzy Sets [18], SDST, Spreadsheet Decision Support Tool [19], SDSM, Scheduling Decision Support Model [20], TOFPS, Two-phase Order Fulfillment Planning Structure [21], and ERP&NN, ERP System including Neural Network [22]).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The greater fitness value of chromosome is, the better quality of chromosome is. The minimum value (min ) of total cost of production planning is obtained by (14). Because the total amount of all orders ( ) is fixed, in order to standardize calculation process of IGA, the maximum profit rate of all orders (max Pr) is defined as the objective of IGA which is shown as (15):…”

Section: Fitness Evaluation Fitness Function Of Genetic Algorithmmentioning

confidence: 99%

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

Guo

Liu

et al. 2017

Mathematical Problems in Engineering

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The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.

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“…Multi-objective decisions are often used in line- seru conversion (Kaku et al 2009 ; Yu et al 2013 , 2014 ). However, multi-objective optimization is more difficult to solve than single-objective optimization (Ebrahimipour et al 2015 ). Enumeration algorithm based on non-dominated sorting (Deb et al 2002 ) for multi-objective line- seru conversion is described as follows.…”

Section: Two Improved Exact Approaches For Multi-objective Line- mentioning

confidence: 99%

Complexity of line-seru conversion for different scheduling rules and two improved exact algorithms for the multi-objective optimization

Wang

Tang

et al. 2016

SpringerPlus

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Productivity can be greatly improved by converting the traditional assembly line to a seru system, especially in the business environment with short product life cycles, uncertain product types and fluctuating production volumes. Line-seru conversion includes two decision processes, i.e., seru formation and seru load. For simplicity, however, previous studies focus on the seru formation with a given scheduling rule in seru load. We select ten scheduling rules usually used in seru load to investigate the influence of different scheduling rules on the performance of line-seru conversion. Moreover, we clarify the complexities of line-seru conversion for ten different scheduling rules from the theoretical perspective. In addition, multi-objective decisions are often used in line-seru conversion. To obtain Pareto-optimal solutions of multi-objective line-seru conversion, we develop two improved exact algorithms based on reducing time complexity and space complexity respectively. Compared with the enumeration based on non-dominated sorting to solve multi-objective problem, the two improved exact algorithms saves computation time greatly. Several numerical simulation experiments are performed to show the performance improvement brought by the two proposed exact algorithms.

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Multi-objective modeling for preventive maintenance scheduling in a multiple production line

Cited by 60 publications

References 31 publications

Multi-principle preventive maintenance: a design-oriented scheduling study for mechanical systems

Multi-principle preventive maintenance: a design-oriented scheduling study for mechanical systems

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

Complexity of line-seru conversion for different scheduling rules and two improved exact algorithms for the multi-objective optimization

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