Maintenance Outsourcing Contracts for New Technology Adoptions

Energy Efficient Motion Control for a Light Rail Vehicle Using The Big Bang Big Crunch Algorithm

Identifying the optimal switching point between different invasive alien species (IAS) management policies is a very complex task and policy makers are in need of modelling tools to assist them. In this paper we develop an optimal control bioeconomic model to estimate the type of optimal policy and switching point of control efforts against a spreading IAS. We apply the models to the case study of Colorado potato beetle in the UK. The results demonstrate that eradication is optimal for small initial sizes of invasion at discovery. High capacity of the agency to reduce spread velocity for several years leads to smaller total overall costs of invasion and makes eradication optimal for larger sizes of initial invasion. In many cases, it is optimal to switch from control to acceptance within the time horizon. The switching point depends on the capacity of the agency, initial size of invasion, spread velocity of the IAS and the ratio of unit cost of damage and removal. We encourage the integration of the dispersal patterns of the invader and the geometry of the invasion in the theoretical development of the economics of IAS invasion management.

In this manuscript, an energy-efficient train operation between successive stations is studied. Cruising and coasting, two basic motion phases of train, should be taken into consideration in order to decrease energy consumption. Determining the optimal switching points from one motion phase into another is key in the energy saving. It is shown that, genetic algorithm and simulated annealing, when employed in a hybrid algorithm, complement each other in finding such switching points. For a performance verification of the hybrid optimization approach, multiple test tracks with different lengths are considered. Also certain real life constraints are taken into account such as punctuality and maximum speed limit. Obtained results are compared to the single genetic algorithm and it is shown that the hybrid algorithm built as a cascade combination of genetic algorithm and simulated annealing can reach optimum solution with better accuracy and lower time consumption.

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.

The maintenance of any system can be categorized in two ways: failure based maintenance (corrective maintenance) and life based maintenance (preventive maintenance). The time interval at which the preventive maintenance could be scheduled is dependent on both the life distribution of the subsystems/components and the total cost involved in the maintenance activity. However, the corrective maintenance cannot be avoided when a random failure of a component occurs. The total cost of the maintenance depends on the number of components replaced during the entire operating period of the system and the respective cost involved in maintenance actions. In this paper the preventive maintenance schedule (t/sub 0/) is calculated taking the failure distribution of a component as Weibull. The variation in (t/sub 0/) with respect to the shape parameter (/spl beta/) and characteristic life (/spl alpha/) of the Weibull distribution was studied by the author for a given preventive maintenance cost and corrective maintenance cost. The ratio of the corrective maintenance cost to the preventive maintenance cost (R/sub cp/) has a significant effect on the maintenance schedule (t/sub 0/). Thus the variation of (t/sub 0/) with (R/sub cp/) is addressed in this paper. The ratio is varied between 10 and 1.5. It is found that the (t/sub 0/) value increases as shape parameter increases when the ratio is 10 whereas the (to) value decreases when the ratio is 1.5. This shows that as we decrease the cost ratio (R/sub cp/) the frequency of preventive maintenance decreases with shape parameter. This in turn implies that the frequency of the preventive maintenance schedule can be kept at minimum by bringing down the cost of corrective maintenance. Sensitivity analysis carried out on the (t/sub 0/) values indicates that the variation in the cost of maintenance is about 5 to 8 percent :in all the cases even when the value of (t/sub 0/) is increased. by 50% over the optimum value. This indicates, while the optimum value of (to) obtained may be of academic interest, one can 'in practice' choose a convenient value near the optimum without appreciably affecting the advantage in terms of cost.

Maintenance is performed to restore a system's inherent reliability. However, reliability is not enough: a maintenance task must “pay for itself‘ in dollars or readiness. This applies to all types of maintenance: corrective, preventive, and alterative. We can reduce corrective maintenance costs through preventive and alterative maintenance. We can reduce preventive maintenance costs by eliminating inapplicable and ineffecitve tasks. We must weigh alterative maintenance costs against the value of improved readiness and cost‐avoidance of future corrective maintenance.The U. S. Navy is improving alterative maintenance by engineering for reduced maintenance (ERM). This process corrects the root cause of some high‐cost items by replacing the system, component, or coating with an improved design or material. It overcomes the limit of preventive maintenance: higher levels of inherent reliability require design improvement.The U. S. Navy is improving preventive maintenance through condition‐based maintenance (CBM). The Navy's CBM initiative encompasses three parallel efforts: rules (improving maintenance requirements and plans), tools (using computer and diagnostic technology to facilitate maintenance decision‐making), and schools (educating all levels of maintenance decision‐makers in reliability and condition‐based maintenance engineering principles).This paper addresses how the U. S. Navy surface ship community plans to avoid inapplicable and ineffective maintenance and reduce maintenance costs without sacrificing reliability.

An additional acceleration effect of the design process has been discovered on the basis of the generalized system design methodology by means of various design strategies analysis with the different initial points. Some principal characteristics of this effect were studied to select the optimal position of the initial point of the design process. The special Lyapunov function of the design process was proposed to analyze some conditions to obtain the optimal position of the switching points between the different design strategies. These optimal switching points serve as the basis for the structure determination of the time-optimal design algorithm.

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.

Two-stage maintenance of a production system with exponentially distributed On- and Off-periods

In view of the hold phase in front of and behind the uphill or downhill of a track, the optimal switch point with local optimal algorithm is not the one with the least cost. This paper finds its deficiencies, and presents an improved method to be applied in the global optimization to minimize the energy costs.

Avian formation flight has always inspired technology protagonists to mimic and harness associated benefits for the aviation industry. This paper discusses the performance benefits of similar and hybrid jet airliners flying in formation. The aerodynamic forces are modelled through potential flow theory. The lateral and vertical spacing is varied to estimate advantages in range. For simplicity, only cruise performance is analysed. By employing formation flying in wide-body aircrafts, an increase in range up to 65% is observed relative to standard flight. In addition to that, an optimal switching point during cruise flight is evaluated to give equal benefits to both aircrafts flying in formation. The study proposes a new concept of shared airspace where different aircrafts cooperate on long routes for fuel savings. However, the findings reported in this paper are of theoretical nature and high-fidelity models and experimentation is considered necessary to further evaluate the benefits of formation flying. Moreover, existing distancing restrictions between two aircrafts, imposed by regulatory authorities, are also a potential limitation in successful realisation of commercial formation flight.

The preventive maintenance (PM) is scheduled periodically to restore the system condition. However, PM activities cannot generally restore the system to as good as new condition but in an intermediate situation, which is called imperfect PM. The PM interval optimization is useful for improving system reliability. A popular approach to optimization interval is minimizing the maintenance costs. The goal of this work is to obtain optimal PM intervals that minimize expected cost considered PM imperfect, in other words we obtain the optimal PM intervals assuming that the PM action restores the system operating state to be somewhere between as good as new and as bad as old. The results are applied in a real data and the PM is maintained at a decreasing sequence of intervals, which is more practical since most systems need more frequent maintenance with increased usage and age. Furthermore consider a wrong PM perfect hypotheses increase the maintenance cost substantially.

This paper presents a new strategy for reliability-based Preventive Inspection and Maintenance (PIM) scheduling of coal-fired power plants considering maintenance costs. Preventive Inspection and Maintenance Interval (PIMI) for any series or parallel subsystem is optimised at the reliability level where total (preventive and corrective) life cycle maintenance cost is the minimum. PIM of any series subsystem in a continuous operating power plant is difficult since off-working period is unavailable. Therefore a time management strategy followed by maintenance class formation is introduced for their PIM according to the estimated PIMIs. Here corrective maintenance of any failed subsystem along with PIM of the remaining subsystems in the same maintenance class is suggested. The case studies reveal that the optimum PIMIs of the subsystems vary from 75.8% to 93.2% reliability level. The proposed maintenance strategy is very simple, easy to implement, improves availability and reduces the maintenance costs.

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.

3.1 Natural History of Splenomegaly in CLL from Automated Volumetric Analysis