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MGT613 - Production / Operations Management - Lecture Handout 09

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FORECASTING-II

Time Series Forecasts

  • Trend - long-term upward or downward movement in data often relates to population shifts, changing incomes, and cultural changes.
  • Seasonality - short-term fairly regular variations in data related to factors like weather, festive holidays and vacations. Mostly experienced by supermarkets, restaurants, theatres, theme parks.
  • Cycle – wavelike variations of more than one year’s duration these occurs because of political, economic and even agricultural conditions
  • Irregular variations - caused by unusual circumstances such as severe weathers, earthquakes, worker strikes, or major change in product or service. They do not capture or reflect the true behavior of a variable and can distort the overall picture. These should be identified and removed from the data.
  • Random variations - caused by chance and are in reality are the residual variations that remain after the other behaviors have been identified and accounted for.
    Forecast Variations

Techniques for Averaging

  • Moving average
  • Weighted moving average
  • Exponential smoothing
  1. Moving average – A technique that averages a number of recent actual values, updated as new values become available.
  2. Weighted moving average – More recent values in a series are given more weight in computing the forecast.

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CS604 - Operating Systems - Lecture Handout 14

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Summary

  • Basic concepts
  • Scheduling criteria
  • Preemptive and non-preemptive algorithms
  • First-Come-First-Serve scheduling algorithm

Basic Concepts

The objective of multiprogramming is to have some process running at all times, in order to maximize CPU utilization. In a uniprocessor system, only one process may run at a time; any other processes much wait until the CPU is free and can be rescheduled.

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