Search published articles


Showing 4 results for Preventive Maintenance

Mohammad Saber Fallah Nezhad, Ali Mostafaeipour,
Volume 25, Issue 1 (2-2014)
Abstract

In order to perform Preventive Maintenance (PM), two approaches have evolved in the literature. The traditional approach is based on the use of statistical and reliability analysis of equipment failure. Under statistical-reliability (S-R)-based PM, the objective of achieving the minimum total cost is pursued by establishing fixed PM intervals, which are statistically optimal, at which to replace or overhaul equipments or components. The second approach involves the use of sensor-based monitoring of equipment condition in order to predict occurrence of machine failure. Under condition-based (C-B) PM, intervals between PM works are no longer fixed, but are performed only “when needed”. It is obvious that Condition Based Maintenance (CBM) needs an on-line inspection and monitoring system that causes CBM to be expensive. Whenever this cost is infeasible, we can develop other methods to improve the performance of traditional (S-R)-based PM method. In this research, the concept of Bayesian inference was used. The time between machine failures was observed, and with combining Bayesian Inference with (S-R)-based PM, it is tried to determine the optimal checkpoints. Therefore, this approach will be effective when it is combined with traditional (S-R)-based PM, even if large number of data is gathered.
Amir Noroozi, Saber Molla-Alizadeh-Zavardehi, Hadi Mokhtari,
Volume 27, Issue 2 (6-2016)
Abstract

Scheduling has become an attractive area for artificial intelligence researchers. On other hand, in today's real-world manufacturing systems, the importance of an efficient maintenance schedule program cannot be ignored because it plays an important role in the success of manufacturing facilities. A maintenance program may be considered as the heath care of manufacturing machines and equipments. It is required to effectively reduce wastes and have an efficient, continuous manufacturing operation. The cost of preventive maintenance is very small when it is compared to the cost of a major breakdown. However, most of manufacturers suffer from lack of a total maintenance plan for their crucial manufacturing systems. Hence, in this paper, we study a maintenance operations planning optimization on a realistic variant of parallel batch machines manufacturing system which considers non-identical parallel processing machines with non-identical job sizes and fixed/flexible maintenance operations. To reach an appropriate maintenance schedule, we propose solution frameworks based on an Artificial Immune Algorithm (AIA), as an intelligent decision making technique. We then introduce a new method to calculate the affinity value by using an adjustment rate. Finally, the performance of proposed methods are investigated. Computational experiments, for a wide range of test problems, are carried out in order to evaluate the performance of methods.


Aliakbar Hasani,
Volume 28, Issue 2 (6-2017)
Abstract

In this paper, a comprehensive mathematical model for designing an electric power supply chain network via considering preventive maintenance under risk of network failures is proposed. The risk of capacity disruption of the distribution network is handled via using a two-stage stochastic programming as a framework for modeling the optimization problem. An applied method of planning for the network design and power generation and transmission system via considering failures scenarios, as well as network preventive maintenance schedule, is presented. The aim of the proposed model is to minimize the expected total cost consisting of power plants set-up, power generation and the maintenance activities. The proposed mathematical model is solved by an efficient new accelerated Benders decomposition algorithm. The proposed accelerated Benders decomposition algorithm uses an efficient acceleration mechanism based on the priority method which uses a heuristic algorithm to efficiently cope with computational complexities. A large number of considered scenarios are reduced via using a k-means clustering algorithm to decrease the computational effort for solving the proposed two-stage stochastic programming model. The efficiencies of the proposed model and solution algorithm are examined using data from the Tehran Regional Electric Company. The obtained results indicate that solutions of the stochastic programming are more robust than the obtained solutions provided by a deterministic model.


Seyedhamed Mousavipour, Hiwa Farughi, Fardin Ahmadizar,
Volume 30, Issue 3 (9-2019)
Abstract

 Sequence dependent set-up times scheduling problems (SDSTs), availability constraint and transportation times are interesting and important issues in production management, which are often addressed separately. In this paper, the SDSTs job shop scheduling problem with position-based learning effects, job-dependent transportation times and multiple preventive maintenance activities is studied. Due to learning effects, jobs processing times are not fixed during plan horizon and each machine has predetermined number of preventive maintenance activities. A novel mixed integer linear programming model is proposed to formulate the problem for minimizing Make Span. Owing to the high complexity of the problem; we applied Grey Wolf Optimizer (GWO) and Invasive Weed Optimizer (IWO) to find nearly optimal solutions for medium and large instances. Finally, the computational Results are provided for evaluating the performance and effectiveness of the proposed solution approaches.

Page 1 from 1     

© 2020 All Rights Reserved | International Journal of Industrial Engineering & Production Research

Designed & Developed by : Yektaweb