Group Preventive Maintenance Model for Multi-unit Series System: A TLBO Algorithm-Based Approach
01 Jan 2021-pp 61-69
TL;DR: A recently developed meta-heuristic named teaching–learning-based optimization (TLBO) algorithm is applied to optimize the objective function to obtain an optimum group of components and PM intervals which minimizes the expected total maintenance cost of the system per unit time.
Abstract: Group maintenance deals with performing preventive maintenance (PM) on other components when the system stops due to induced failure or scheduled PM interval of a particular component. Group maintenance actions save downtime costs and other production losses. However, simultaneous maintenance of all units is not always economically beneficial. As a consequence, the decision to which group of components is maintained is very selective due to economic and stochastic interdependencies. In the present paper, we propose a novel and efficient group maintenance model in the multi-unit series system for grouping under PM intervals. The objective is to obtain an optimum group of components and PM intervals which minimizes the expected total maintenance cost of the system per unit time. A recently developed meta-heuristic named teaching–learning-based optimization (TLBO) algorithm is applied to optimize the objective function. The peculiarity of TLBO is that unlike other evolutionary-based heuristics it is a parameterless algorithm which makes it computationally easy to understand and implement. Computational results yield the effectiveness of the proposed approach when compared with traditional maintenance practices.
References
More filters
[...]
TL;DR: The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort and results show that TLBO is more effective and efficient than the other optimization methods.
Abstract: A new efficient optimization method, called 'Teaching-Learning-Based Optimization (TLBO)', is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the 'Teacher Phase' and the second part consists of the 'Learner Phase'. 'Teacher Phase' means learning from the teacher and 'Learner Phase' means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems.
2,400 citations
[...]
TL;DR: In this paper, the authors studied the maintenance of multi-component systems and proposed new maintenance policies for multichannel systems, and the number of papers with practical applications of optimal maintenance of multicloud systems is still growing.
Abstract: Over the last few decades the maintenance of systems has become more and more complex. One reason for this is that systems consist of many components which depend on each other. On the one hand, interactions between components complicate the modelling and optimization of maintenance. On the other hand, interactions also offer the opportunity to group maintenance which may save costs. It follows that planning maintenance actions is a big challenge and it is not surprising that many scholars have studied maintenance optimization problems for multi-component systems. In some articles new solution methods for existing problems are proposed, in other articles new maintenance policies for multi-component systems are studied. Moreover, the number of papers with practical applications of optimal maintenance of multi-component systems is still growing.
383 citations
[...]
TL;DR: This paper presents a review of applications of TLBO algorithm and a tutorial for solving the unconstrained and constrained optimization problems and is expected to be useful to the beginners.
Abstract: Article history: Received June25, 2015 Received in revised format: September 22, 2015 Accepted September 24, 2015 Available online September 25 2015 The teaching-learning-based optimization (TLBO) algorithm is finding a large number of applications in different fields of engineering and science since its introduction in 2011. The major applications are found in electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics, chemistry, biotechnology and economics. This paper presents a review of applications of TLBO algorithm and a tutorial for solving the unconstrained and constrained optimization problems. The tutorial is expected to be useful to the beginners.
146 citations
[...]
TL;DR: An opportunistic PM scheduling algorithm for the multi-unit series system based on dynamic programming with the integration of the imperfect effect into maintenance actions is proposed.
Abstract: It is understood that for a multi-unit series system, whenever one of the units stops to perform a preventive maintenance (PM) action, the whole series system must be stopped. At that time PM opportunities arise for the other units in the system. This paper proposes an opportunistic PM scheduling algorithm for the multi-unit series system based on dynamic programming with the integration of the imperfect effect into maintenance actions. An optimal maintenance practice is determined by maximizing the short-term cumulative opportunistic maintenance cost savings for the whole system. Matlab is considered for the optimization which is based on numerical simulation. Numerical examples are given throughout to show how this approach works. Finally, a comparison between the proposed PM model and other models is given.
103 citations
[...]
TL;DR: A model has been developed for integrating maintenance scheduling and process quality control policy decisions and it provided an optimal preventive maintenance interval and control chart parameters that minimize expected cost per unit time.
Abstract: Performance of a manufacturing system depends significantly on the shop floor performance. Traditionally, shop floor operational policies concerning maintenance scheduling, quality control and production scheduling have been considered and optimized independently. However, these three aspects of operations planning do have an interaction effect on each other and hence need to be considered jointly for improving the system performance. In this paper, a model is developed for joint optimization of these three aspects in a manufacturing system. First, a model has been developed for integrating maintenance scheduling and process quality control policy decisions. It provided an optimal preventive maintenance interval and control chart parameters that minimize expected cost per unit time. Subsequently, the optimal preventive maintenance interval is integrated with the production schedule in order to determine the optimal batch sequence that will minimize penalty-cost incurred due to schedule delay. An example is presented to illustrate the proposed model. It also compares the system performance employing the proposed integrated approach with that obtained by considering maintenance, quality and production scheduling independently. Substantial economic benefits are seen in the joint optimization.
75 citations