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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.
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 ...
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 ...
System dynamics is a methodology and mathematical modeling technique to frame, understand, and discuss complex issues and problems. Originally developed in the 1950s to help corporate managers improve their understanding of industrial processes, SD is currently being used throughout the public and private sector for policy analysis and design.
Optimization (role-playing games), a gaming play style; Optimize; Process optimization, in business and engineering, methodologies for improving the efficiency of a production process; Product optimization, in business and marketing, methodologies for improving the quality and desirability of a product or product concept
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.
Its name comes from the information path in the system: process inputs (e.g., voltage applied to an electric motor) have an effect on the process outputs (e.g., speed or torque of the motor), which is measured with sensors and processed by the controller; the result (the control signal) is "fed back" as input to the process, closing the loop. [4]
Goal programming is a branch of multiobjective optimization, which in turn is a branch of multi-criteria decision analysis (MCDA). It can be thought of as an extension or generalisation of linear programming to handle multiple, normally conflicting objective measures. Each of these measures is given a goal or target value to be achieved.