scholarly journals Multi Equipment Condition Based Maintenance Optimization Using Multi-Objective Evolutionary Algorithms

2019 ◽  
Vol 9 (22) ◽  
pp. 4849
Author(s):  
Aitor Goti ◽  
Aitor Oyarbide-Zubillaga ◽  
Ana Sanchez ◽  
Tugce Akyazi ◽  
Elisabete Alberdi
Keyword(s):  
Preventive Intervention ◽  
Discrete Event ◽  
Condition Based Maintenance ◽  
Optimization Approach ◽  
Actual System ◽  
Multi Objective ◽  
Event Simulation ◽  
Industrial Chain ◽  
Efficiency And Effectiveness ◽  
Optimal Values

Thanks to the digitalization of industry, maintenance is a trending topic. The amount of data available for analyses and optimizations in this field has increased considerably. In addition, there are more and more complex systems to maintain, and to keep all these devices in proper conditions, which requires maintenance management to gain efficiency and effectiveness. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, but often these programs are complex to manage and understand. The problem becomes more complex when equipment is analyzed in the context of a plant, where equipment can be more or less saturated, critical regarding quality, etc. Thus, this paper focuses on CBM optimization of a full industrial chain, with the objective of determining its optimal values of preventive intervention limits for equipment under economic criteria. It develops a mathematical plus discrete-event-simulation based model that takes the evolution in quality and production speed into consideration as well as condition based, corrective and preventive maintenance. The optimization process is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case, where the data gathered by the IoT (Internet of Things) devices at edge level can detect when some premises of the CBM model are no longer valid and request a new simulation. The simulation performed in a centralized way can thus obtain new optimal values who fit better to the actual system than the existing ones. Finally, these new optimal values can be transferred to the model whenever it is necessary. The approach developed has raised the interest of a partner of the Deusto Digital Industry Chair.

scholarly journals Enhanced coverage by integrating site interdependencies in capacitated EMS location models

2021 ◽  
Author(s):  
Matthias Grot ◽  
Tristan Becker ◽  
Pia Mareike Steenweg ◽  
Brigitte Werners
Keyword(s):  
Discrete Event ◽  
Mathematical Formulation ◽  
Optimization Approach ◽  
Solution Quality ◽  
Similar System ◽  
Event Simulation ◽  
Simulation Based ◽  
Average System ◽  
Simplifying Assumptions ◽  
A Site

AbstractIn order to allocate limited resources in emergency medical services (EMS) networks, mathematical models are used to select sites and their capacities. Many existing standard models are based on simplifying assumptions, including site independency and a similar system-wide busyness of ambulances. In practice, when a site is busy, a call is forwarded to another site. Thus, the busyness of each site depends not only on the rate of calls in the surrounding area, but also on interactions with other facilities. If the demand varies across the urban area, assuming an average system-wide server busy fraction may lead to an overestimation of the actual coverage. We show that site interdependencies can be integrated into the well-known Maximum Expected Covering Location Problem (MEXCLP) by introducing an upper bound for the busyness of each site. We apply our new mathematical formulation to the case of a local EMS provider. To evaluate the solution quality, we use a discrete event simulation based on anonymized real-world call data. Results of our simulation-optimization approach indicate that the coverage can be improved in most cases by taking site interdependencies into account, leading to an improved ambulance allocation and a faster emergency care.

scholarly journals Application of Discrete Event Simulation (DES) for Queuing System Improvement at Hypermarket

2019 ◽  
Author(s):  
Fazeeda Mohamad ◽  
Siti Filza Saharin
Keyword(s):  
Discrete Event Simulation ◽  
System Performance ◽  
Discrete Event ◽  
Simulation Software ◽  
Queuing System ◽  
Computer Simulation Model ◽  
Time Study ◽  
Actual System ◽  
Event Simulation ◽  
System Improvement

This paper focuses on the development of a computer simulation model for improving the queuing system at a hypermarket using Discrete Event Simulation (DES) and to propose the most efficient hypermarket queuing system for overall improvement. Data were collected from the Hypermarket A using the time study. The method of this study is using modeling and simulation. Arena Simulation Software is used to develop the model to replicate the actual system. Three scenarios had been tested, and the alternatives will be ranked based on the level of the efficiency of the system performance. The most efficient queuing system is identified based on the scenario analysis. In this study, the waiting time for each customer can be improved by up to 26%, which equivalent to 5.24 minutes. Overall, this study contributes to a better understanding of the queuing system performance.

scholarly journals Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms

2019 ◽  
Vol 9 (15) ◽  
pp. 3068 ◽  
Author(s):  
Aitor Goti ◽  
Aitor Oyarbide-Zubillaga ◽  
Elisabete Alberdi ◽  
Ana Sanchez ◽  
Pablo Garcia-Bringas
Keyword(s):  
Preventive Maintenance ◽  
Manufacturing Industry ◽  
Industrial Revolution ◽  
Preventive Intervention ◽  
Cost Benefit ◽  
Economic Consequences ◽  
Maintenance Cost ◽  
Optimization Approach ◽  
Multi Objective ◽  
Optimal Maintenance

Maintenance has always been a key activity in the manufacturing industry because of its economic consequences. Nowadays, its importance is increasing thanks to the “Industry 4.0” or “fourth industrial revolution”. There are more and more complex systems to maintain, and maintenance management must gain efficiency and effectiveness in order to keep all these devices in proper conditions. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, even though often these programs are complex to manage and understand. For this reason, several research papers propose approaches that are as simple as possible and can be understood by users and modified by experts. In this context, this paper focuses on CBM optimization in an industrial environment, with the objective of determining the optimal values of preventive intervention limits for equipment under corrective and preventive maintenance cost criteria. In this work, a cost-benefit mathematical model is developed. It considers the evolution in quality and production speed, along with condition based, corrective and preventive maintenance. The cost-benefit optimization is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case.

scholarly journals Mengurangi Antrean pada Stasiun Pengisian Bahan Bakar Umum dengan Pendekatan Simulasi Menggunakan ProModel

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Mirna Lusiani ◽  
Anie Belita
Keyword(s):  
Service Time ◽  
Discrete Event ◽  
Primary Data ◽  
Time Data ◽  
Actual System ◽  
Large Cities ◽  
Event Simulation ◽  
Gas Station ◽  
Increasing Demand ◽  
Number Of Customers

<p>The gas station has become an important facility for the public, especially for people in large cities such as Jakarta. This condition was caused by increasing demand from year to year. The number of facilities has less number than the customer that came, which could cause queueing in the station. This research was made with the purpose to reduce the number of the queue and increase the number of customers serviced at the gas station. This research located on one of the gas stations in Jakarta at 16.00-18.00 from Monday to Friday. The data used for this research is primary data which was observed directly by the researcher. The data used for this research are the number of customers arrived and service time. Data analysis is using discrete-event simulation with ProModel software. The conclusion of this research is the actual system has a relatively high number of queueing customer which also affect the reduced number of customers that was served. Improvement model was created by decreasing the service time by 10% with the average number of queues by 4 customers for Pertalite and 4<br />customers for Premium. Scenario model is also designed as a proposal by using customer migration scenario from Premium to Pertalite by 30%, 60%, and 100%. The proposed system for the first scenario is by decreasing the service time by 20% with the average number of queues by 8 customers for Pertalite and 1 customer for Premium. For the second scenario is by modifying the queueing system and decreasing the service time by 10% with the average number of queues by 8 customers for Pertalite and 7 customers for Premium. For the third scenario is by opening a new server for Pertalite with the average number of queues by 5 customers.</p>

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