Quality function deployment ( QFD ) is a method developed in Japan beginning in 1966 to help transform the voice of the customer into engineering characteristics for a product. Yoji Akao , the original developer, described QFD as a "method to transform qualitative user demands into quantitative parameters, to deploy the functions forming quality, and to deploy methods for achieving the design quality into subsystems and component parts, and ultimately to specific elements of the manufacturing process." The author combined his work in quality assurance and quality control points with function deployment used in value engineering .
29-406: QFD may refer to: Quality function deployment Quantum flavordynamics Queensland Fire Department Question-focused dataset Boufarik Airport , Algeria ( IATA : QFD ) Qufu East railway station , China Railway pinyin code QFD Topics referred to by the same term [REDACTED] This disambiguation page lists articles associated with
58-401: A globally optimal solution can be found on some class of problems. Many metaheuristics implement some form of stochastic optimization , so that the solution found is dependent on the set of random variables generated. In combinatorial optimization , there are many problems that belong to the class of NP-complete problems and thus can no longer be solved exactly in an acceptable time from
87-416: A heuristic (partial search algorithm ) that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a subset of solutions which is otherwise too large to be completely enumerated or otherwise explored. Metaheuristics may make relatively few assumptions about
116-501: A part of QFD, is the basic design tool of quality function deployment. It identifies and classifies customer desires (WHATs), identifies the importance of those desires, identifies engineering characteristics which may be relevant to those desires (HOWs), correlates the two, allows for verification of those correlations, and then assigns objectives and priorities for the system requirements. This process can be applied at any system composition level (e.g. system, subsystem, or component) in
145-576: A particular problem instance using techniques provided’’. There are many candidate optimization tools which can be considered as a MOF of varying feature. The following list of 33 MOFs is compared and evaluated in detail in: Comet, EvA2, evolvica, Evolutionary::Algorithm, GAPlayground, jaga, JCLEC, JGAP, jMetal, n-genes, Open Beagle, Opt4j, ParadisEO/EO, Pisa, Watchmaker, FOM, Hypercube, HotFrame, Templar, EasyLocal, iOpt, OptQuest, JDEAL, Optimization Algorithm Toolkit, HeuristicLab, MAFRA, Localizer, GALIB, DREAM, Discropt, MALLBA, MAGMA, and UOF. There have been
174-399: A relatively low degree of complexity. Metaheuristics then often provide good solutions with less computational effort than approximation methods, iterative methods, or simple heuristics. This also applies in the field of continuous or mixed-integer optimization. As such, metaheuristics are useful approaches for optimization problems. Several books and survey papers have been published on
203-470: A special emphasis on modularity. There are three main differences to QFD as applied in modular function deployment compared to house of quality: The benchmarking data is mostly gone; the checkboxes and crosses have been replaced with circles, and the triangular "roof" is missing. Metaheuristic In computer science and mathematical optimization , a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select
232-1225: A wide variety of metaheuristics and a number of properties with respect to which to classify them. The following list is therefore to be understood as an example. One approach is to characterize the type of search strategy. One type of search strategy is an improvement on simple local search algorithms. A well known local search algorithm is the hill climbing method which is used to find local optimums. However, hill climbing does not guarantee finding global optimum solutions. Many metaheuristic ideas were proposed to improve local search heuristic in order to find better solutions. Such metaheuristics include simulated annealing , tabu search , iterated local search , variable neighborhood search , and GRASP . These metaheuristics can both be classified as local search-based or global search metaheuristics. Other global search metaheuristic that are not local search-based are usually population-based metaheuristics. Such metaheuristics include ant colony optimization , evolutionary computation such as genetic algorithm or evolution strategies , particle swarm optimization , rider optimization algorithm and bacterial foraging algorithm. Another classification dimension
261-515: Is one that combines a metaheuristic with other optimization approaches, such as algorithms from mathematical programming , constraint programming , and machine learning . Both components of a hybrid metaheuristic may run concurrently and exchange information to guide the search. On the other hand, Memetic algorithms represent the synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. An example of memetic algorithm
290-415: Is single solution vs population-based searches. Single solution approaches focus on modifying and improving a single candidate solution; single solution metaheuristics include simulated annealing , iterated local search , variable neighborhood search , and guided local search . Population-based approaches maintain and improve multiple candidate solutions, often using population characteristics to guide
319-451: Is sought over a discrete search-space. An example problem is the travelling salesman problem where the search-space of candidate solutions grows faster than exponentially as the size of the problem increases, which makes an exhaustive search for the optimal solution infeasible. Additionally, multidimensional combinatorial problems, including most design problems in engineering such as form-finding and behavior-finding, suffer from
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#1732884492614348-560: Is the design of nature-inspired metaheuristics. Many recent metaheuristics, especially evolutionary computation-based algorithms, are inspired by natural systems. Nature acts as a source of concepts, mechanisms and principles for designing of artificial computing systems to deal with complex computational problems. Such metaheuristics include simulated annealing , evolutionary algorithms , ant colony optimization and particle swarm optimization . A large number of more recent metaphor-inspired metaheuristics have started to attract criticism in
377-426: Is the use of a local search algorithm instead of or in addition to a basic mutation operator in evolutionary algorithms. A parallel metaheuristic is one that uses the techniques of parallel programming to run multiple metaheuristic searches in parallel; these may range from simple distributed schemes to concurrent search runs that interact to improve the overall solution. With population-based metaheuristics,
406-401: The curse of dimensionality , which also makes them infeasible for exhaustive search or analytical methods . Metaheuristics are also frequently applied to scheduling problems. A typical representative of this combinatorial task class is job shop scheduling, which involves assigning the work steps of jobs to processing stations in such a way that all jobs are completed on time and altogether in
435-578: The no-free-lunch theorems , which state that there can be no metaheuristic that is better than all others for any given problem. Especially since the turn of the millennium, many metaheuristic methods have been published with claims of novelty and practical efficacy. While the field also features high-quality research, many of the more recent publications have been of poor quality; flaws include vagueness, lack of conceptual elaboration, poor experiments, and ignorance of previous literature. These are properties that characterize most metaheuristics: There are
464-400: The design of a product, and can allow for assessment of different abstractions of a system. It is intensely progressed through a number of hierarchical levels of WHATs and HOWs and analyse each stage of product growth (service enhancement), and production (service delivery). The house of quality appeared in 1972 in the design of an oil tanker by Mitsubishi Heavy Industries . The output of
493-623: The evaluation function should be subject to greater demands than a mathematical optimization. Not only does the desired target state have to be formulated, but the evaluation should also reward improvements to a solution on the way to the target in order to support and accelerate the search process. The fitness functions of evolutionary or memetic algorithms can serve as an example. Metaheuristics are used for all types of optimization problems, ranging from continuous through mixed integer problems to combinatorial optimization or combinations thereof. In combinatorial optimization, an optimal solution
522-414: The house of quality is generally a matrix with customer desires on one dimension and correlated nonfunctional requirements on the other dimension. The cells of matrix table are filled with the weights assigned to the stakeholder characteristics where those characteristics are affected by the system parameters across the top of the matrix. At the bottom of the matrix, the column is summed, which allows for
551-563: The house of quality relevant to product development, and called metaheuristic methods "a promising approach for solving complicated problems of FQFD." The process of quality function deployment (QFD) is described in ISO 16355-1:2021. Pugh concept selection can be used in coordination with QFD to select a promising product or service configuration from among listed alternatives. Modular function deployment uses QFD to establish customer requirements and to identify important design requirements with
580-454: The mixture of combinatorial and continuous optimization is the planning of favourable motion paths for industrial robots. A MOF can be defined as ‘‘a set of software tools that provide a correct and reusable implementation of a set of metaheuristics, and the basic mechanisms to accelerate the implementation of its partner subordinate heuristics (possibly including solution encodings and technique-specific operators), which are necessary to solve
609-418: The optimization problem being solved and so may be usable for a variety of problems. Their use is always of interest when exact or other (approximate) methods are not available or are not expedient, either because the calculation time is too long or because, for example, the solution provided is too imprecise. Compared to optimization algorithms and iterative methods , metaheuristics do not guarantee that
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#1732884492614638-530: The population itself can be parallelized by either processing each individual or group with a separate thread or the metaheuristic itself runs on one computer and the offspring are evaluated in a distributed manner per iteration. The latter is particularly useful if the computational effort for the evaluation is considerably greater than that for the generation of descendants. This is the case in many practical applications, especially in simulation-based calculations of solution quality. A very active area of research
667-467: The research community for hiding their lack of novelty behind an elaborate metaphor. As a result, a number of renowned scientists of the field have proposed a research agenda for the standardization of metaheuristics in order to make them more comparable, among other things. Another consequence is that the publication guidelines of a number of scientific journals have been adapted accordingly. Most metaheuristics are search methods and when using them,
696-453: The search; population based metaheuristics include evolutionary computation and particle swarm optimization . Another category of metaheuristics is Swarm intelligence which is a collective behavior of decentralized, self-organized agents in a population or swarm. Ant colony optimization , particle swarm optimization , social cognitive optimization and bacterial foraging algorithm are examples of this category. A hybrid metaheuristic
725-615: The shortest possible time. In practice, restrictions often have to be observed, e.g. by limiting the permissible sequence of work steps of a job through predefined workflows and/or with regard to resource utilisation, e.g. in the form of smoothing the energy demand. Popular metaheuristics for combinatorial problems include genetic algorithms by Holland et al., scatter search and tabu search by Glover. Another large field of application are optimization tasks in continuous or mixed-integer search spaces. This includes, e.g., design optimization or various engineering tasks. An example of
754-486: The stakeholder needs may need to be refined . The concepts of fuzzy logic have been applied to QFD ("Fuzzy QFD" or "FQFD"). A review of 59 papers in 2013 by Abdolshah and Moradi found a number of conclusions: most FQFD "studies were focused on quantitative methods" to construct a house of quality matrix based on customer requirements, where the most-employed techniques were based on multiple-criteria decision analysis methods. They noted that there are factors other than
783-441: The subject. Literature review on metaheuristic optimization, suggested that it was Fred Glover who coined the word metaheuristics. Most literature on metaheuristics is experimental in nature, describing empirical results based on computer experiments with the algorithms. But some formal theoretical results are also available, often on convergence and the possibility of finding the global optimum. Also worth mentioning are
812-574: The system characteristics to be weighted according to the stakeholder characteristics. System parameters not correlated to stakeholder characteristics may be unnecessary to the system design and are identified by empty matrix columns, while stakeholder characteristics (identified by empty rows) not correlated to system parameters indicate "characteristics not addressed by the design parameters". System parameters and stakeholder characteristics with weak correlations potentially indicate missing information, while matrices with "too many correlations" indicate that
841-474: The title QFD . If an internal link led you here, you may wish to change the link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=QFD&oldid=1253858088 " Category : Disambiguation pages Hidden categories: Short description is different from Wikidata All article disambiguation pages All disambiguation pages Quality function deployment The house of quality,
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