By Jett Alcock at January 12 2019 13:49:06
Linear Programming, mathematical and operations-research technique, used in administrative and economic planning to maximize the linear functions of a large number of variables, subject to certain constraints. The development of high-speed electronic computers and data-processing techniques has brought about many recent advances in linear programming, and the technique is now widely used in industrial and military operations. Linear programming is basically used to find a set of values, chosen from a prescribed set of numbers, that will maximize or minimize a given polynomial form and this is illustrated by the finished; the manufacturer knows that as many articles as are produced can be sold.
They are more difficult to implement through discipline than administrative human-centric processes (although some discipline is needed). It is better to focus on obtaining buy-in from the people affected by the processes through early involvement, communication and expectations management. It is a known fact that knowledge workers are reluctant to change their habits. Some say knowledge workers don't like following procedures because they feel it limits their creativity; but most of the time they will be happy to follow a procedure as long as they see value in it, perceiving that it helps them work better and produce a better process output.
That is, they are increasingly becoming part of the basic circuitry of computers or are easily attached adjuncts, as well as standing alone in special devices such as office payroll machines. Many different applications algorithms are now available, and highly advanced systems such as artificial intelligence algorithms may become common in the future. Artificial Intelligence (AI), a term that in its broadest sense would indicate the ability of an artifact to perform the same kinds of functions that characterize human thought.
All process instances are executed in a very similar way and it is easy to draw a flowchart detailing the sequence in which tasks are executed. It is also possible to formalize the business rules that guide decisions, normally based on the evaluation of some process variables. But recently other kinds of processes have caught the attention of process management specialists. They are known as knowledge processes, or knowledge-based processes. Knowledge processes can be defined as "high added value processes in which the achievement of goals is highly dependent on the skills, knowledge and experience of the people carrying them out". Some examples could be management, R&D, or new product development processes.
It is usual that knowledge processes take the form of projects to manage their execution. If the output of the process is a unique product, managing work as a project will result in obvious advantages. There are certain guidelines that can help an organization willing to improve their knowledge processes: Provide process description on how to approach work Try to figure out the best way to carry out a knowledge process, by making the best practices existing in your organization (or in your industry) explicit. Publish process definitions in a format that is easy to consult and understand. Provide tools that facilitate and standardize work Decide which tools are best to help knowledge workers carry out their work. Involving all affected knowledge workers in the process of deciding which tools will be used is very convenient, in order to obtain user buy in.
In medicine, programs have been developed that analyze the disease symptoms, medical history, and laboratory test results of a patient, and then suggest a diagnosis to the physician. The diagnostic program is an example of so-called expert systems-programs designed to perform tasks in specialized areas as a human would. Expert systems take computers a step beyond straightforward programming, being based on a technique called rule-based inference, in which preestablished rule systems are used to process the data. Despite their sophistication, systems still do not approach the complexity of true intelligent thought.