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2. No free lunch in search and optimization - Wikipedia

   en.wikipedia.org/wiki/No_free_lunch_in_search...

   The original no free lunch (NFL) theorems assume that all objective functions are equally likely to be input to search algorithms. [2] It has since been established that there is NFL if and only if, loosely speaking, "shuffling" objective functions has no impact on their probabilities.

3. SMART criteria - Wikipedia

   en.wikipedia.org/wiki/SMART_criteria

   S.M.A.R.T. (or SMART) is an acronym used as a mnemonic device to establish criteria for effective goal-setting and objective development. This framework is commonly applied in various fields, including project management, employee performance management, and personal development.

4. Sales and operations planning - Wikipedia

   en.wikipedia.org/wiki/Sales_and_operations_planning

   APICS defines S&OP as the "function of setting the overall level of manufacturing output (production plan) and other activities to best satisfy the current planned levels of sales (sales plan and/or forecasts), while meeting general business objectives of profitability, productivity, competitive customer lead times, etc., as expressed in the ...

5. Operational objective - Wikipedia

   en.wikipedia.org/wiki/Operational_objective

   In business, operational objectives (also known as tactical objectives) are short-term goals whose achievement brings an organization closer to its long-term goals. [1] It is slightly different from strategic objectives, which are longer term goals of a business, but they are closely related, as a business will only be able to achieve strategic objectives when operational objectives have been ...

6. AOL Mail

   mail.aol.com

   Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!

7. No free lunch theorem - Wikipedia

   en.wikipedia.org/wiki/No_free_lunch_theorem

   For example, it applies to the situations where the algorithm is fixed a priori and a worst-case problem for the fixed algorithm is chosen a posteriori. Therefore, if we have a "good" problem in practice or if we can choose a "good" learning algorithm for a given particular problem instance, then the NFL does not mention any limitation about ...