Integrated Maintenance Planning Research Articles

Long-term maintenance optimization for integrated mining operations

AbstractMaintenance activities are inevitable and costly in integrated mining operations. Conducting maintenance may require the whole system, or sub-units of the system, to be shut down temporarily. These maintenance activities not only disrupt the unit being shut down, but they also have consequences for inventory levels and product flow downstream. In this paper, we consider an interconnected mining system in which there are complicated maintenance relationships and stock accumulation at intermediate nodes. We propose a time-indexed mixed-integer linear programming formulation to optimize the long-term integrated maintenance plan and maximize the total throughput. We also devise an algorithm, which combines Benders decomposition and Lagrangian relaxation, to accelerate the computational speed. To validate our mathematical model, we perform simulations for a real-world case study in the iron ore industry. The results show that our method can yield better solutions than optimization solver alone in faster time.

Prioritising Maintenance Work Orders in a Thermal Power Plant: A Multicriteria Model Application

Maintenance is one of the most rapidly expanding activities in the industrial environment, since its application is no longer limited to simple, regular fixes. In the case of thermal power plants maintenance is essential, since they only operate when the National Electric System Operator wants them to complement the production from renewable sources such as hydro, wind, and solar. To limit the frequency of failures that result in generation unavailability, the operation team performs daily inspections to evaluate the equipment’s condition and the risks to the generating process. If an anomaly is found, the maintenance team will create service notes to address it. This research aims to demonstrate how the method Measuring Attractiveness by a Category-Based Evaluation Technique (Macbeth) can be applied to the development of a multiple-criterion model to support decision making in ordering the criticality of systems in thermal plant operational inspection routes to propose new methodologies for routine execution to increase the operation team’s productivity. According to the results of the judgement matrix, the recommended ordering enabled a strategy for the performance of the current operational routes by redefining the criticality, periodicity, routing, and resources utilised, hence preserving the plant’s reliability. According to the results, the proposed ranking will enable a new strategy for integrated maintenance planning, redefining the criticality of service orders according to the judgement based on criteria and subcriteria, thereby allowing the application of resources appropriately and focusing on what is more important to maintain the thermal power plant’s continuity and operational safety.

Integrated maintenance planning approach to optimize budget allocation for subway operating systems

Millions of citizens worldwide heavily rely on subways as their main public transportation approach. However, public transit infrastructures are generally facing extensive deterioration that requires extensive funds by governments to retain back the desired performance levels. The real case is that most governments are heavily facing a budget scarcity dilemma, and cannot withstand the acquired maintenance costs to maintain a proper operation for subway network. This research aims at introducing a new risk-based multi-objective optimization framework for optimizing performance and budget allocation among subway systems in fulfillment to maintenance planning development in this domain. This would extensively ease the decision-making process for subway asset managers by providing the optimal allocated budget for each subway system in an automated way. The framework targets the vital subway operating systems, namely: railway tracks, overhead catenary, lighting & power supply, centralized control, communication, signaling, rolling stock, and ticketing. The research framework develops four main modules; Reliability Centered Maintenance Database, Deterioration Modelling, Multi-objective Optimization, and Decision-Making. Reliability Centered Maintenance Database where subway operating systems are decomposed to components levels showing their functions, operational failures, failure causes, failure effects, failure consequences, responsive maintenance actions, and the hourly maintenance costs. Deterioration Modeling using deterministic and artificial intelligence models to estimate future conditions of subway components. Multi-objective optimization, and decision-making are developed to decide the optimal maintenance frequencies that derive lowest maintenance costs and lowest risk of failure for each functional failure. An actual case-study is worked out to verify the proposed framework. The results reveal the greatest share of the maintenance budget was allocated to lighting & power supply rectifier station with a 54% of the total maintenance budget, followed by rolling stock system with 32%, railway tracks system with 7%, signaling system with 3%, and approximately 1% for each of the remaining systems.

An integrated maintenance planning, warranty policy, technology level and pricing model considering time value of money in a three-level servicing contract

A common way to address customer concerns in the post-warranty period is to provide an extended warranty. Although sometimes the manufacturer is reluctant to offer an extended warranty, an agent takes on this task to maintain market share. In this regard, a three-level-servicing-contract among manufacture, agent and customer is presented in which both warranty period and useful life of the product are considered as function of age and usage. The proposed model considers two approaches to control the number of product failures and reduce cost: (1) the technology level used in manufacturing as an effective factor in product reliability; and (2) non-periodic maintenance activities to maintain the product reliability at an acceptable level. In addition, in this study, to calculate the costs imposed on each side of the contract more accurately, the time-value-of-money is considered in the calculation of financial flows. To illustrate the effectiveness of the proposed approach, three comparative studies are provided. The first comparative study shows the impact of the provision of an extended warranty, while the second comparative study proves the importance of preventive maintenance to reduce costs. The results of the last one show the effect of considering the time-value-of-money in calculating cash flows.

RETRACTED: Integrated maintenance/spare parts management for manufacturing system according to variable production rate impacting the system degradation

This study develops an analytical model in order to determine an optimal integrated maintenance plan and spare parts management. We consider a manufacturing system, producing only one type of product, over a finite planning horizon H equal to the sum of all production periods and the production quantity of each period is known. This system is subject to a continuously increasing degradation rate. That is why a preventive maintenance strategy is adopted in order to face the increasing failure rate. We noted that contrarily to the majority of studies in literature, we take into account the impact of the production rate variation on the manufacturing system degradation and consequently on the adopted optimal maintenance strategy. In addition, the real need of spare parts relative to the scheduled maintenance actions is taken into account. In fact, the purpose of our study consists at determining the optimal preventive maintenance frequency and the optimal quantity of spare parts to order by minimizing a total cost, including maintenance and spare parts management. Numerical examples are presented along with a sensitivity study in order to prove the use of the developed model for deriving the optimal integrated strategy for any instance of the problem.

A two-stage coupled algorithm for an integrated maintenance planning and flowshop scheduling problem with deteriorating machines

We address a novel integrated maintenance and production scheduling problem in a multi-machine and multi-period production system, considering maintenance as a long-term decision. Deterioration of machines over time decreases production capacity. Since maintenance activities not only improve machine conditions, increasing production capacity, but also take time that cannot be used for production, the challenge is to assign maintenance to periods and to schedule maintenance and production activities within each period to minimize the combined cost of maintenance and lost production over the planning horizon. Motivated by logic-based Benders decomposition, we design an integrated two-stage algorithm to solve the problem. The first stage assigns maintenance to machines and time periods, abstracting the scheduling problem, while the second stage creates a schedule for the current time period. The first stage is then re-solved using feedback from the schedule. This iteration between maintenance planning and scheduling continues until the solution costs in two stages converge. The integrated approach models the interdependencies between maintenance and scheduling decisions in highly coupled processes such as wafer fabrication in the semiconductor manufacturing. Our results demonstrate that the benefit of integrated decision making increases when maintenance is less expensive relative to lost production cost and that a longer horizon for maintenance planning is beneficial when maintenance cost increases.

Integrated maintenance planning and production scheduling with Markovian deteriorating machine conditions

In many industries, production capacity diminishes as machine conditions deteriorate. Maintenance operations improve machine conditions, but also occupy potential production time, possibly delaying the customer orders. Therefore, one challenge is to determine the joint maintenance and production schedule to minimize the combined costs of maintenance and lost production over the long term. In this paper, we address the problem of integrated maintenance and production scheduling in a deteriorating multi-machine production system over multiple periods. Assuming that at the beginning of each period the demand becomes known and machine conditions are observable, we formulate a Markov decision process model to determine the maintenance plan and develop sufficient conditions guaranteeing its monotonicity in both machine condition and demand. We then formulate an integer programming model to find the maintenance and the production schedule in each period. Our computational results show that exploiting online condition monitoring information in maintenance and production decisions leads to 21% cost savings on average compared to a greedy heuristic and that the benefit of incorporating long-term information in making short-term decisions is highest in industries with medium failure rates.

An Integrated Maintenance Management System for an Advanced Manufacturing Company

Maintenance costs in advanced manufacturing companies, e.g. electronics manufacturing companies, have been steadily increasing as the companies upgrade their production facilities. Maintenance costs are normally a considerable portion of the product cost. To maintain a competitive place in the market, it is critical for an advanced manufacturing company to have a sound maintenance management system, which can control its maintenance costs at the lowest level and maintain its overall equipment effectiveness at the highest level. This paper reports on an integrated maintenance management system, which has been developed and implemented in an advanced electronics manufacturing company in Hong Kong. The methods and principles of the system are discussed in sections on maintenance performance auditing, cost recording and tracing, reliability centred maintenance planning and control, condition monitoring and on-line feedback control, and integrated maintenance planning and control.