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  2. Silver–Meal heuristic - Wikipedia

    en.wikipedia.org/wiki/Silver–Meal_heuristic

    The Silver–Meal heuristic is a production planning method in manufacturing, composed in 1973 [1] by Edward A. Silver and H.C. Meal. Its purpose is to determine production quantities to meet the requirement of operations at minimum cost.

  3. Dynamic lot-size model - Wikipedia

    en.wikipedia.org/wiki/Dynamic_lot-size_model

    We have available a forecast of product demand d t over a relevant time horizon t=1,2,...,N (for example we might know how many widgets will be needed each week for the next 52 weeks). There is a setup cost s t incurred for each order and there is an inventory holding cost i t per item per period ( s t and i t can also vary with time if desired).

  4. DTIME - Wikipedia

    en.wikipedia.org/wiki/DTIME

    In computational complexity theory, DTIME (or TIME) is the computational resource of computation time for a deterministic Turing machine. It represents the amount of time (or number of computation steps) that a "normal" physical computer would take to solve a certain computational problem using a certain algorithm .

  5. Program evaluation and review technique - Wikipedia

    en.wikipedia.org/wiki/Program_Evaluation_and...

    expected time: the best estimate of the time required to accomplish an activity (te) or a path (TE), accounting for the fact that things don't always proceed as normal (the implication being that the expected time is the average time the task would require if the task were repeated on a number of occasions over an extended period of time).

  6. Deterministic time hierarchy theorem - Wikipedia

    en.wikipedia.org/wiki/Time_hierarchy_theorem

    Time Hierarchy Theorem. If f(n) is a time-constructible function, then there exists a decision problem which cannot be solved in worst-case deterministic time o(f(n)) but can be solved in worst-case deterministic time O(f(n)log f(n)).

  7. Minimax estimator - Wikipedia

    en.wikipedia.org/wiki/Minimax_estimator

    Example 3: Bounded normal mean: When estimating the mean of a normal vector (,), where it is known that ‖ ‖. The Bayes estimator with respect to a prior which is uniformly distributed on the edge of the bounding sphere is known to be minimax whenever M ≤ n {\displaystyle M\leq n\,\!} .