An Efficient Imperialist Competitive Algorithm for Solving the QFD Decision Problem
TL;DR: Wang et al. as discussed by the authors reformulated the QFD decision problem as a mixed integer nonlinear programming (MINLP) model, which aims to maximize overall customer satisfaction with the consideration of the enterprises' capability, cost, and resource constraints.
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Abstract: It is an important QFD decision problem to determine the engineering characteristics and their corresponding actual fulfillment levels. With the increasing complexity of actual engineering problems, the corresponding QFD matrixes become much huger, and the time spent on analyzing these matrixes and making decisions will be unacceptable. In this paper, a solution for efficiently solving the QFD decision problem is proposed. The QFD decision problem is reformulated as a mixed integer nonlinear programming (MINLP) model, which aims to maximize overall customer satisfaction with the consideration of the enterprises’ capability, cost, and resource constraints. And then an improved algorithm G-ICA, a combination of Imperialist Competitive Algorithm (ICA) and genetic algorithm (GA), is proposed to tackle this model. The G-ICA is compared with other mature algorithms by solving 7 numerical MINLP problems and 4 adapted QFD decision problems with different scales. The results verify a satisfied global optimization performance and time performance of the G-ICA. Meanwhile, the proposed algorithm’s better capabilities to guarantee decision-making accuracy and efficiency are also proved.
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Citations
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TL;DR: Many scenarios where one can or cannot use different NI methods in tackling real-world optimization problems are discussed and many studies are enriched with many studies to prove the efficiency and efficacy of using NI methods to tackle many real- world applications.
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Liquefaction potential analysis using hybrid multi-objective intelligence model
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Design of phase-only reconfigurable planar array antenna in selected phi cuts using various meta-heuristic optimization algorithms
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A Genetic Algorithm for Solving a QFD(Quality Function Deployment) Optimization Problem
TL;DR: A genetic algorithm (GA) solution approach to finding the optimum set of TAs in QFD in the above situation is proposed and a numerical example is provided for illustrating the proposed approach.
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References
Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition
Esmaeil Atashpaz-Gargari,Caro Lucas +1 more
- 01 Sep 2007
TL;DR: Applying the proposed algorithm for optimization inspired by the imperialistic competition to some of benchmark cost functions shows its ability in dealing with different types of optimization problems.
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A real coded genetic algorithm for solving integer and mixed integer optimization problems
TL;DR: The proposed MI-LXPM is a suitably modified and extended version of the real coded genetic algorithm, LXPM, of Deep and Thakur and incorporates a special truncation procedure to handle integer restrictions on decision variables along with a parameter free penalty approach for handling constraints.
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The leading edge in QFD: past, present and future
Yoji Akao,Glenn H. Mazur +1 more
TL;DR: Quality Function Deployment (QFD) has been used by leading companies around the world since 1966 as mentioned in this paper to ensure that true customer needs are properly deployed throughout the design, build and delivery of a new product, whether it be assembled, processed, serviced, or even software.
530
Determination of an Optimal Set of Design Requirements Using House of Quality
Taeho Park,Kwang-Jae Kim +1 more
TL;DR: A new integrative decision model for selecting an optimal set of DRs is presented using a modified HOQ model that employs a multi-attribute decision method for assigning relationship ratings between CRs and DRs instead of a conventional relationship rating scale.
285
Extended ant colony optimization for non-convex mixed integer nonlinear programming
TL;DR: Two novel extensions for the well known ant colony optimization (ACO) framework are introduced here, which allow the solution of mixed integer nonlinear programs (MINLPs) and a hybrid implementation based on this extended ACO framework, specially developed for complex non-convex MINLPs is presented.
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