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Process optimization is the discipline of adjusting a process so as to make the best or most effective use of some specified set of parameters without violating some constraint. Common goals are minimizing cost and maximizing throughput and/or efficiency. Process optimization is one of the major quantitative tools in industrial decision making.
Evolutionary Operation (EVOP) is a manufacturing process-optimization technique developed in the 1950s by George E. P. Box. [1] In EVOP, experimental designs and improvements are introduced, while an ongoing full-scale manufacturing process continues to produce satisfactory results. The idea is that process improvement should not interrupt ...
Process–architecture–optimization is a development model for central processing units (CPUs) that Intel adopted in 2016. Under this three-phase (three-year) model, every microprocessor die shrink is followed by a microarchitecture change and then by one or more optimizations.
Sequential minimal optimization; Sequential quadratic programming; Simplex algorithm; Simulated annealing; Simultaneous perturbation stochastic approximation; Social cognitive optimization; Space allocation problem; Space mapping; Special ordered set; Spiral optimization algorithm; Stochastic dynamic programming; Stochastic gradient Langevin ...
Process engineering activities can be divided into the following disciplines: [7] Process design: synthesis of energy recovery networks, synthesis of distillation systems (), synthesis of reactor networks, hierarchical decomposition flowsheets, superstructure optimization, design multiproduct batch plants, design of the production reactors for the production of plutonium, design of nuclear ...
PIDO stands for Process Integration and Design Optimization.Process Integration is needed as many software tools are used in a multi-domain system design. Control software is developed in a different toolchain than the mechanical properties of a system, where structural analysis is done using again some different tools.
Successive Linear Programming (SLP), also known as Sequential Linear Programming, is an optimization technique for approximately solving nonlinear optimization problems. [1] It is related to, but distinct from, quasi-Newton methods .
Multi-task Bayesian optimization is a modern model-based approach that leverages the concept of knowledge transfer to speed up the automatic hyperparameter optimization process of machine learning algorithms. [8] The method builds a multi-task Gaussian process model on the data originating from different searches progressing in tandem. [9]