Nonlinear Integer Programming for Solving Preventive Maintenance Scheduling Problem for Cogeneration Plants with Production

Q: Due to constraints in our biomed budget, I was wondering: Is there anyway to reduce the rotations for sterilizers in our preventive maintenance (PM) program?A: Sure. In fact, several years ago, we reduced the rotation and the items we change on sterilizers. Our facility has 11 operating rooms (OR) and two labor-and-delivery rooms. Our sterilizers get fairly heavy use between 6 a.m. and 11 p.m. But we're definitely not as busy as facilities that run 24 hours a day, 365 days a year. So we reduced the PM schedule for some of them to a semiannual basis. This process has worked very well. We have not had any more failures than we had when we did them quarterly. We saved ourselves time and the materials we would have had in replacing those quarterly parts. Certain sterilizers, like the washer-sterilizers, still undergo quarterly PM due to their fairly heavy use and the fact that we only have two in the OR. If one goes down, the impact to our customer is significant.I am making my suggestions based on the assumption that your facility has a good preventive maintenance program and some sort of a program that enables you to track equipment. By tracking equipment repairs, you can monitor the results if you reduce your facility's PM schedule. Our facility continuously monitors work requests through a program in our database. This program allows use to “write” almost any report one can imagine. For example, one of our performance indicators is repeat work requests. We have a report that compiles all such requests during a given period—three months, in our case. Each technician is given the piece of the report related to the equipment that he or she is responsible for. The technician then checks the work requests for that equipment, looking for anything that can be incorporated into a regular PM schedule and prevent a failure. This allows your facility to “hold all the cards,” so to speak. If someone at your facility periodically monitors your success, you will be able to adjust your PM program accordingly.As for what parts can be reduced, that depends on several factors such as use, steam quality, water quality, and maintenance program effectiveness. Our water here tends to be higher in calcium than most parts can tolerate. So we keep water traps and water solenoids on a semiannual schedule. That said, our steam quality is fairly good, and we push our steam parts as long as possible. When we looked at ways to reduce cost, we looked along the line of steam—not water.Another key factor is staff care with equipment. For example, we once had someone in the instrument room who would slam the doors of the sterilizers so hard that the machines shook. We talked to him on several occasions, but he slammed the door right up until his last day at our facility. If that was not bad enough, after slamming it one time, this now-former employee cranked hard on the handle and then brought it to me to report, “It's broke.” Ha, ha! While he worked at our facility, we stepped up the PM frequency on the doors. We would check the handles for tightness, the arms that wind out to make sure they were not missing their holders, the door seal which would snap if brittle and the door was slammed, and the door cover to make sure it had not been bent.But let me end by returning to the big picture. You can and should alter your PM schedule to maximize your resources. Working with a good database, a good PM program can save you time and money. Remember: Garbage in, garbage out.

Security constrained Preventive Maintenance (PM) scheduling is addressed as a crucial issue especially coping with new challenges of smart grids. Under the smart grid environment, Demand Response Resources (DRRs) are considered as virtual power plants in energy policy decisions which affect the controllability of power system ranging from short-term to long-term. Here, DRRs are regarded as a virtual resource of capacity reserve acquisition. Furthermore, under new environment, multifarious objectives will be imposed to PM scheduling which can be replicated into a single objective problem via a weighted approach with consideration of arbitrary weighting coefficients. In this paper, a new structure is proposed for determining the efficient weighting factors for reserve cost, operation cost, and generated emissions in PM scheduling associated with DR Programs (DRPs) the so-called PMDRPs. Utilizing Entropy method as one of the Multi Attribute Decision Making (MADM) techniques, the aforementioned factors are determined in a two phase hierarchy. Using the nominated structure, the maintenance scheme, commitment status, energy scheduling, and demand-as well as supply-side reserve scheduling are determined along the scheduling horizon. The numerical studies are carried out on the IEEE-RTS to confirm the capability of the proposed framework.

Repairable systems deteriorate with age and usage. In order to maintain acceptable reliability and prevent sudden failures, preventive maintenance (PM) is often applied to such systems. Scheduled PM actions can improve system availability and minimize losses due to breakdowns and failures. However, condition‐based PM is considered more relevant to system status than age‐based PM for deteriorating repairable systems. In this study, we consider the production yield rate as the condition variable when determining the optimal PM schedule. A non‐homogeneous Poisson process is used to describe the system deterioration, and the concept of system effective (virtual) age is also considered to make the proposed model more realistic. Three different condition‐based PM strategies are presented to provide more diverse choices for decision makers in terms of solving problems based on the situation at hand. The results show that the optimal yield rate threshold can reduce system failure while maintaining acceptable system availability and product quality with affordable costs due to a proper PM schedule. Furthermore, the results of the numerical application showed that the maintenance and penalty costs are the most sensitive to the total cost, where the adoption of the proposed PM strategies are more profitable when related costs are low.

Preventive maintenance (PM) is a maintenance program with activities initiated at predetermined intervals, or according to prescribed criteria, and intended to reduce the probability of failure, or the degradation of the functioning of an item. In the literature, a number of PM models have been introduced to depict the effectiveness of PM. Based on these models, approaches to scheduling PM policies have been considerably studied. This paper attempts to review existing PM models, and investigate their inter-relationships. We then categorize these models into three classes: linear, nonlinear, and a hybrid of both. These three PM model classes depict the relationships of the hazard functions before, and after a PM. Possible extensions to these three PM models are discussed. The statistical properties for models are derived, and approaches to optimizing the PM policy are given.

Traditionally, gas turbine power plant preventive maintenance schedules are set with constant intervals based on recommendations from the equipment suppliers. Preventive maintenance is based on fleet-wide experience as a guideline as long as individual unit experience is not available. In reality, the operating conditions for each gas turbine may vary from site to site and from unit to unit. Furthermore, the gas turbine is a repairable deteriorating system, and preventive maintenance usually restores only part of its performance. This suggests a gas turbine needs more frequent inspection and maintenance as it ages. A unit-specific sequential preventive maintenance approach is therefore needed for gas turbine power plant preventive maintenance scheduling. Traditionally, the optimization criteria for preventive maintenance scheduling is usually cost based. However, in the deregulated electric power market, a profit-based optimization approach is expected to be more effective than the cost-based approach. In such an approach, power plant performance, reliability, and the market dynamics are considered in a joint fashion. In this paper, a novel idea that economic factors drive maintenance frequency and expense to more frequent repairs and greater expense as equipment ages is introduced, and a profit-based unit-specific sequential preventive maintenance scheduling methodology is developed. To demonstrate the feasibility of the proposed approach, a conceptual level study is performed using a base load combined cycle power plant with a single gas turbine unit.

Economic production lot-sizing for an unreliable machine under imperfect age-based maintenance policy

Jet engines are critical assets in aircraft, and their availability is crucial in the modern aircraft industry. Therefore, their maintenance scheduling is one of the major tasks an airline has to make during an engine’s lifetime. A proper engine maintenance schedule can significantly reduce maintenance costs without compromising the aircraft's reliability and safety. Different maintenance scheduling approaches have been used for jet engines, such as corrective, preventive, and predictive maintenance strategies. Regarding the safety demands in aircraft industries, preventive maintenance is a frequent maintenance method for jet engines. However, preventive maintenance schedules are often use fixed maintenance intervals, which is usually suboptimal. This paper focuses on minimizing a jet engine's overall maintenance cost by optimizing its preventive maintenance schedule based on an engine’s comprehensive reliability model. A hierarchical optimization framework including the golden section search and genetic algorithms is applied to find the optimal set of preventive maintenance number and their times and the components to be replaced at those times during the jet engine's overall lifetime. The Monte Carlo simulation is used to estimate the engine’s failure times using their lifetime distributions from the reliability model. The estimated failure times are then used to determine the engine's overall corrective and preventive maintenance costs during its lifetime. Finally, an optimal preventive maintenance schedule is proposed for an RB 211 jet engine using the presented method. In the end, comparing the proposed method's overall maintenance cost with two other maintenance methods demonstrates the proposed schedule's effectiveness. The method presented in this paper is generic, and it can be used for other similar engineering systems.

Opportunistic preventive maintenance scheduling for multi-unit reusable rocket engine system based on the variable maintenance task window method

Maintenance is regarded as one of the effective methods to enhance the reliability of nuclear power plant (NPP) system. The existing research works on NPP maintenance optimization mainly focus on the preventive maintenance (PM) scheduling, with the common assumption that activities are performed at scheduled time. Though the straightforward asset management brought by PM scheduling, its inflexibility could result to either unnecessary maintenance activities or undiscovered failures. On the other hand, due to the budget limit it's inappropriate to neglect the shortage of maintenance workers and the missed plans caused by it. In this research, an integrated scheduling of maintenance workers and activities for NPP subsystem is considered. Effective age is used to determine the performed maintenance activities while measuring its effectiveness. To deal with this problem, a mathematical model is established with the objective of minimizing the total cost. Maintenance starting time, workers and activities of each component over the scheduling horizon are regarded as decision variables. The proposed model is applied to an NPP feed water subsystem and solved by specially designed genetic algorithm.

Optimal Design of Heat Exchanger Networks by Using SQP Algorithm Based on GPU Acceleration

In general, it is difficult to directly solve an optimization problem of systems reliability formulated as a nonlinear integer programming (NIP) model. In this paper, we propose a method for solving the NIP problem for the best compromise solution while holding a nonlinear property by using the genetic algorithm. We also report that the optimization problem of systems reliability as a case study is solved using the proposed method. The numerical comparison experiments between the 0-1 LP/0-1 NP formulations are demonstrated and, from the quantitative evaluation, the efficiency of the proposed method is demonstrated. >

This paper shows the application of Discrete Event Simulation to study the effect of proposed maintenance strategies to the availability. The simulation study was conducted on a Surface Mount Technology (SMT) line. The model is built in the Discrete Event Simulation software WITNESS 14 to imitate this selected case study line to test out two proposed maintenance strategies. The first proposed strategy consists of Corrective Maintenance (CM) task only whereas the second proposed strategy is the introduction of Preventive Maintenance (PM) to the line. The effect of the two cases to the availability of each machine and system availability were verified with the simulation model to determine the most optimal solution. Although the simulation testing did not show improvement in availability for all station after the introduction of PM, the increment in system availability indicate that PM is the most optimal maintenance strategy for the SMT line.

To solve the problem of long logistics delivery time in supply chain, a Mixed Integer Non-linear Program (MINLP) model is built by using Mixed Integer nonlinear programming theory. Firstly, the General algebraic modeling system (GAMS) is used to build the model to fully integrate each parameter of logistics transportation, the total distribution time of the supply chain network, the coverage radius of the logistics base, the number of users, the total capacity of the logistics base, the mode of railway and road transportation, the nonlinear programming model is built and solved by DICOPT solver in GAMS. The cost of logistics can be decreased, transportation time can be reduced, and the logistics system's operating efficiency can be increased in the long term with the help of this algorithm. The proper operation of the logistics system is critical in encouraging the supply chain circulation of various industries and has a direct impact on the society's economic development. The optimal logistics distribution plan with 5 logistics bases covered users of 18 and railway capacity of 2. With the same railway capacity and the same total budget, the larger the number of covered users, the greater the total distribution time increases, but the larger the total budget, the growth of the total distribution time slows down significantly. Experiments show that MINLP model can solve the problem of logistics-based layout optimization in nonlinear logistics management.

Cell formation problem mainly addresses how machines should be grouped and parts be processed in cells. In dynamic environments, product mix and demand are changed in each period of the planning horizon. Incorporating such assumption in the model increases flexibility of the system to meet customers' requirements. In this model, to ensure the reliability of the system in presence of unreliable machines, alternative routing process as well as buffer storage are considered to reduce detrimental effects of machine failure. This problem is presented by a nonlinear mixed integer programming model attempting to minimize the overall cost of the system. To solve the model in large scale for practical purposes, a genetic algorithm approach is adopted as the model belongs to NP-hard class of problems. Sensitivity analysis is used to show the validity of the proposed model. Besides, numerical examples are used both, for small-sized and large- sized instances to show the efficiency of the method in finding near optimal solution.

Optimization of alternative structures of integrated power and desalination plants