scholarly journals Model Terintegrasi Penjadwalan Batch dan Pm dengan Kriteria Minimalisasi Biaya Simpan, Setup dan Pm pada Mesin dengan Increasing Failure Rate

2013 ◽  
Vol 4 (1) ◽  
pp. 121
Author(s):  
Zahedi Zahedi
Keyword(s):  
Failure Rate ◽  
Preventive Maintenance ◽  
Product Life Cycle ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Flow Time ◽  
Maintenance Cost ◽  
Setup Cost ◽  
Increasing Failure Rate ◽  
Actual Flow

Development of today's manufacturing systems leads to the getting shorter in product life cycle, more variety of products, as well as increasing in consumer demand for quality and timeliness. Thus the accuracy and speed of decision-making within the manufacturing system becomes important. This paper proposes an integrated model of batch scheduling and preventive maintenance scheduling, in assumption no non-conforming parts to the criteria of minimizing the total saving cost, setup cost, preventive maintenance cost in minimizing the actual flow time on a machine with increasing failure rate. An example is provided to show how the model and algorithm work.

Performance optimization of a flexible manufacturing system using simulation: the Taguchi method versus OptQuest

SIMULATION ◽  
2019 ◽  
Vol 95 (11) ◽  
pp. 1085-1096 ◽  
Author(s):  
Abdessalem Jerbi ◽  
Achraf Ammar ◽  
Mohamed Krid ◽  
Bashir Salah
Keyword(s):  
Taguchi Method ◽  
Performance Optimization ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Flow Time ◽  
Mean Flow ◽  
Taguchi Optimization ◽  
Mean Flow Time

The Taguchi method is widely used in the field of manufacturing systems performance simulation and improvement. On the other hand, Arena/OptQuest is one of the most efficient contemporary simulation/optimization software tools. The objective of this paper is to evaluate and compare these two tools applied to a flexible manufacturing system performance optimization context, based on simulation. The principal purpose of this comparison is to determine their performances based on the quality of the obtained results and the gain in the simulation effort. The results of the comparison, applied to a flexible manufacturing system mean flow time optimization, show that the Arena/OptQuest optimization platform outperforms the Taguchi optimization method. Indeed, the Arena/OptQuest permits one, through the lowest experimental effort, to reliably minimize the mean flow time of the studied flexible manufacturing system more than the Taguchi method.

Real-Time Prognostic and Dynamic Maintenance Window Scheme for Reconfigurable Manufacturing Systems

2019 ◽  
Author(s):  
Yifan Dong ◽  
Tangbin Xia ◽  
Lei Xiao ◽  
Ershun Pan ◽  
Lifeng Xi
Keyword(s):  
Real Time ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Degradation Process ◽  
Maintenance Cost ◽  
Time To Failure ◽  
Reconfigurable Manufacturing Systems ◽  
Reconfigurable Manufacturing ◽  
Reconfigurable Manufacturing System ◽  
Dynamic Maintenance

Abstract Real-time condition acquisition and accurate time-to-failure (TTF) prognostic of machines are both crucial in the condition based maintenance (CBM) scheme for a manufacturing system. Most of previous researches considered the degradation process as a population-specific reliability characteristics and ignored the hidden differences among the degradation process of individual machines. Moreover, existing maintenance scheme are mostly focus on the manufacturing system with fixed structure. These proposed maintenance scheme could not be applied for the reconfigurable manufacturing system, which is quite adjustable to the various product order and customer demands in the current market. In this paper, we develop a systematic predictive maintenance (PM) framework including real-time prognostic and dynamic maintenance window (DMW) scheme for reconfigurable manufacturing systems to fill these gaps. We propose a real-time Bayesian updating prognostic model using sensor-based condition information for computing each individual machine’s TTFs, and a dynamic maintenance window scheme for the maintenance work scheduling of a reconfigurable manufacturing system. This enables the real-time prognosis updating, the rapid decision making for reconfigurable manufacturing systems, and the notable maintenance cost reduction.

scholarly journals Single machine scheduling with consideration of preventive maintenance and machine health

2018 ◽  
Vol 192 ◽  
pp. 01009 ◽  
Author(s):  
Kwei-Long Huang ◽  
Jakey Blue ◽  
Hao-Chen Weng ◽  
Shu-Han Liu
Keyword(s):  
Failure Rate ◽  
Single Machine ◽  
Preventive Maintenance ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Maintenance Cost ◽  
Health Index ◽  
Two Phase ◽  
Machine Failure ◽  
Machine Health

Because of Industry 4.0 and Internet of Things, it is easier to collect data from machines through sensors that are embedded inside machines. Once the status change of a machine is detected, production on that machine may need to be adjusted accordingly. In this research, we focus on single machine scheduling with considering the Preventive Maintenance (PM) and machine health index. Machine health index is categorized into three states: good, fair, and breakdown. When the machine moves from one state to another, the processing time of jobs will change as well as the machine failure rate. We develop a model to determine an optimal interval of performing PM and production sequence of jobs. A two-phase heuristic method is proposed to solve a large-size problem. Through different parameter settings, such as the machine failure rate, number of jobs, repair and maintenance cost, we show that the two-phase heuristic can obtain a solution with high quality.

Optimal preventive maintenance level for a repairable product under warranty subject to multimode failure process

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Azmat Ullah ◽  
Muhammad Ayat ◽  
Hakeem Ur Rehman ◽  
Lochan Kumar Batala
Keyword(s):  
Life Cycle ◽  
Failure Rate ◽  
Preventive Maintenance ◽  
Life Cycle Cost ◽  
Product Life Cycle ◽  
Scale Parameter ◽  
Failure Process ◽  
Rate Function ◽  
Failure Rate Function ◽  
Content Type

PurposeThe purpose of this paper is to develop a model that determines whether how much effort of preventive maintenance action is worthwhile for the consumer over the post-sale product life cycle of a repairable complex product where the product is under warranty and subject to stochastic multimode failure process, that is, damaging failure and light failure with different probabilities.Design/methodology/approachThe expected life cycle cost is designed for a warranted product from the consumer perspective. The product failure is quantified with failure rate function, which is the number of failures incurred over the product life cycle. The authors consider the failure rate function reduction method in their model where the scale parameter of a failure rate function is maximized by applying the optimal preventive maintenance level. The scale parameter of any failure distribution refers to the meantime to failure (MTTF). The first-order condition is applied with respect to the maintenance level in order to achieve the convexity of the nonlinear function of the expected life cycle cost function.FindingsThe authors have found analytically the close form of the preventive maintenance level, which can be used to find the optimal reduced form of the failure rate function of the product and the minimum product expected life cycle cost under the given condition of multimode stochastic failure process. The authors have suggested different maintenance policies to consumers in order to implement the proposed preventive maintenance model under different conditions. A numerical example further illustrated the analytical model by considering the Weibull distribution.Practical implicationsThe consumer may use this study in the accurate modeling of the life cycle cost of a product that is under warranty and fails with a multimode failure process. Also, the suggested preventive maintenance approach of this study helps the consumer in making appropriate maintenance decisions such as to minimize the expected life cycle cost of a product.Originality/valueThis study proposes an accurate estimation of a life cycle cost for a product that is under the support of warranty and fails with multimode. Furthermore, for such a kind of product, which is under warranty and fails with multimode, this study suggests a new preventive maintenance approach that assures the minimum expected life cycle cost.

Optimal lockout/tagout, preventive maintenance, human error and production policies of manufacturing systems with passive redundancy

2014 ◽  
Vol 20 (4) ◽  
pp. 453-470 ◽  
Author(s):  
Behnam Emami-Mehrgani ◽  
Sylvie Nadeau ◽  
Jean-Pierre Kenné
Keyword(s):  
Preventive Maintenance ◽  
Manufacturing Systems ◽  
Human Error ◽  
Manufacturing System ◽  
System Capacity ◽  
Planning Problem ◽  
Human Reliability ◽  
Content Type ◽  
Machine Age ◽  
Maintenance Activities

Purpose – The analysis of the optimal production and preventive maintenance with lockout/tagout planning problem for a manufacturing system is presented in this paper. The considered manufacturing system consists of two non-identical machines in passive redundancy producing one type of part. These machines are subject to random breakdowns and repairs. The purpose of this paper is to minimize production, inventory, backlog and maintenance costs over an infinite planning horizon; in addition, it aims to verify the influence of human reliability on the inventory levels for illustrating the importance of human error during the maintenance and lockout/tagout activities. Design/methodology/approach – This paper is different compared to other research projects on preventive maintenance and lockout/tagout. The influence of human error on lockout/tagout as well as on preventive maintenance activities are presented in this paper. The preventive maintenance policy depends on the machine age. For the considered manufacturing system the optimality conditions are provided, and numerical methods are used to obtain machine age-dependent optimal control policies (production and preventive maintenance rates with lockout/tagout). Numerical examples and sensitivity analysis are presented to illustrate the usefulness of the proposed approach. The system capacity is described by a finite-state Markov chain. Findings – The proposed model taking into account the preventive maintenance activities with lockout/tagout and human error jointly, instead of taking into account separately. It verifies the influence of human error during preventive maintenance and lockout/tagout activities on the optimal safety stock levels using an extension of the hedging point structure. Practical implications – The model proposed in this paper might be extended to manufacturing systems, but a number of conditions must be met to make effective use of it. Originality/value – The originality of this paper is to consider the preventive maintenance activities with lockout/tagout and human error simultaneously. The control policy is obtained in order to find the solution for the considered manufacturing system. This paper also brings a new vision on the importance of human reliability during preventive maintenance and lockout/tagout activities.

Group Scheduling Technique for Multiproduct, Multistage Manufacturing Systems

1977 ◽  
Vol 99 (3) ◽  
pp. 759-765 ◽  
Author(s):  
K. Hitomi ◽  
I. Ham
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Group Technology ◽  
Flow Time ◽  
Group Scheduling ◽  
Total Production ◽  
Total Flow ◽  
Multistage Manufacturing ◽  
Operations Scheduling ◽  
Scheduling Technique

“Group Scheduling,” which is operations scheduling based on the Group Technology concept, was analyzed in a multistage manufacturing system. In the case of fabricating multiple parts (jobs) grouped into several group cells, both optimal group and optimal job sequences were determined such that the total flow time (makespan) was minimized, by means of the branch-and-bound method. In addition, optimal machining speeds to be utilized on multiple stages (machines) for each job were determined to reduce the total production cost as much as possible. Optimizing algorithms for this Group Scheduling Technique were also presented with numerical examples.

scholarly journals Research on Management Strategy of Cost-based Intelligent Manufacturing System

2020 ◽  
Vol 218 ◽  
pp. 01029
Author(s):  
Haoting Cong ◽  
Jinyi Che
Keyword(s):  
Preventive Maintenance ◽  
Manufacturing Systems ◽  
Management Strategies ◽  
Intelligent Manufacturing ◽  
Maintenance Cost ◽  
Cnc Machine ◽  
Distribution Fitting ◽  
Failure Rate Function ◽  
Cnc Machines ◽  
Processing Level

The reliability, efficiency and accuracy of CNC machines as work cells of intelligent manufacturing systems (IMS) are criteria to measure the processing level of the latter. In order to improve the reliability of the IMS and reduce the maintenance cost, very sound preventive maintenance and management strategies concerning the CNC machines should be defined. We realized a parameter estimation of our reliability model for CNC machine units in an IMS environment, carried out a linear correlation test and a distribution fitting test for the model and obtained the failure distribution function and failure rate function. We then built a post-failure maintainability model and realized a maintainability evaluation. Following the above analyses, we built a cost-based preventive maintenance cycle model and obtained its optimal value by using the particle swarm optimization (PSO) algorithm. This research and its result can on the one hand guide the setting-up of preventive maintenance planning and management schemes and on the other hand reduce the production cost and enhance enterprise efficiency.

Dynamic Maintenance Strategy With Q-Learning for Workstations in a Flow Line Manufacturing System

2010 ◽  
Author(s):  
Sagar Kamarthi ◽  
Abe Zeid ◽  
Yusuf Ozbek
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Flow Line ◽  
Maintenance Cost ◽  
Q Learning ◽  
Intelligent Maintenance ◽  
Dynamic Maintenance ◽  
Maintenance Systems ◽  
Modern Manufacturing ◽  
Production Losses

Every machine or equipment in a manufacturing facility is subject to failure due to deterioration based on cumulative wear, crack growth, erosion, etc. This failure will cause production losses and delays resulting in high costs. As the modern manufacturing systems are getting more and more complex, intelligent maintenance schemes must replace the old labor intensive planned maintenance systems to ensure that equipment continues to function. If the maintenance decision is based on the state of the system rather than its age, this leads to the choice of a Condition Based Maintenance (CBM) policy to prevent catastrophic unexpected machine breakdowns and increase the availability of individual machines, but it also introduces randomness into the manufacturing operation. This paper presents a Q-Learning model to dynamically group maintenance actions on different machines and execute them simultaneously, so that one can reduce maintenance cost and increase the efficiency of the manufacturing system.

Sign in / Sign up

Export Citation Format

Share Document