Multi-Criteria Decision Making (MCDM) techniques are gaining popularity in sustainable energy management. The techniques provide solutions to the problems involving conflicting and multiple objectives. Several methods based on weighted averages, priority setting, outranking, fuzzy principles and their combinations are employed for energy planning decisions. A review of more than 90 published papers is presented here to analyze the applicability of various methods discussed. A classification on application areas and the year of application is presented to highlight the trends. It is observed that Analytical Hierarchy Process is the most popular technique followed by outranking techniques PROMETHEE and ELECTRE. Validation of results with multiple methods, development of interactive decision support systems and application of fuzzy methods to tackle uncertainties in the data is observed in the published literature.

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Application of multi-criteria decision making to sustainable energy planning—A review

Reliability has become a greater concern in recent years, because high-tech industrial processes with ever increasing levels of sophistication comprise most engineering systems today. To keep pace with this rapidly developing field, this paper provides a broad overview of recent research on reliability optimization problems and their solution methodologies. In particular, we address issues related to: 1) universal generating-function-based optimal multistate system design; 2) percentile life employed as a system performance measure; 3) multiobjective optimization of reliability systems, especially with uncertain component-reliability estimations; and 4) innovation and improvement in traditional reliability optimization problems, such as fault-tolerance mechanism and cold-standby redundancy-involved system design. New developments in optimization techniques are also emphasized in this paper, especially the methods of ant colony optimization and hybrid optimization. We believe that the interesting problems that are reviewed here are deserving of more attention in the literature. To that end, this paper concludes with a discussion of future challenges related to reliability optimization

https://www.springer.com/cda/content/document/cda_downloaddocument/9783540373674-c1.pdf?SGWID=0-0-45-317035-p173672426

Recent Advances in Optimal Reliability Allocation

This paper uses an ant colony meta-heuristic optimization method to solve the redundancy allocation problem (RAP). The RAP is a well known NP-hard problem which has been the subject of much prior work, generally in a restricted form where each subsystem must consist of identical components in parallel to make computations tractable. Meta-heuristic methods overcome this limitation, and offer a practical way to solve large instances of the relaxed RAP where different components can be placed in parallel. The ant colony method has not yet been used in reliability design, yet it is a method that is expressly designed for combinatorial problems with a neighborhood structure, as in the case of the RAP. An ant colony optimization algorithm for the RAP is devised & tested on a well-known suite of problems from the literature. It is shown that the ant colony method performs with little variability over problem instance or random number seed. It is competitive with the best-known heuristics for redundancy allocation.

An ant colony optimization algorithm for the redundancy allocation problem (RAP)

Associate member, ieee and smith, a senior member, ieee an ant colony optimization algorithm for the redundancy allocation problem (rap)

Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. In this paper, an artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring. The ANN model takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output. A function generalized from the Weibull failure rate function is used to fit each condition monitoring measurement series for a failure history, and the fitted measurement values are used to form the ANN training set so as to reduce the effects of the noise factors that are irrelevant to the equipment degradation. A validation mechanism is introduced in the ANN training process to improve the prediction performance of the ANN model. The proposed ANN method is validated using real-world vibration monitoring data collected from pump bearings in the field. A comparative study is performed between the proposed ANN method and an adapted version of a reported method, and the results demonstrate the advantage of the proposed method in achieving more accurate remaining useful life prediction.

An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring

This paper generalizes a redundancy optimization problem to multi-state systems, where the system and its components have a range of performance levels-from perfect functioning to complete failure. The components are: (1) chosen from a list of products available in the market; and (2) characterized by their nominal performance level, availability and cost. System availability is represented by a multi-state availability function, which extends the binary-state availability. To satisfy the required multi-state system availability, the redundancy for each component can be used. A procedure which determines the minimal-cost series-parallel system structure subject to a multi-state availability constraint is proposed. A fast procedure is developed, based on a universal generating function, to evaluate the multi-state system availability. Two important types of systems are considered and special operators for the universal generating function determination are introduced. A genetic algorithm is used as an optimization technique. Examples are given.

David Elmakis

Papers

Redundancy optimization for series-parallel multi-state systems

Power system structure optimization subject to reliability constraints

Structure optimization of power system with different redundant elements

Optimal sectionalizer allocation in electric distribution systems by genetic algorithm

Genetic algorithm for optimal sectionalizing in radial distribution systems with alternative supply