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    Area of Science:

    • Decision analysis
    • Operations research
    • Cognitive psychology

    Background:

    • Linear decision rules with random or unit coefficients are proposed as superior to human judgment.
    • This assertion is based on high correlations between rule-based and human decisions.
    • The scheduling production problem is a relevant domain for evaluating decision-making strategies.

    Purpose of the Study:

    • To compare the cost-effectiveness of linear decision rules versus human judgment in a production scheduling context.
    • To investigate whether high correlations with optimal decisions translate to lower actual costs.
    • To challenge the assertion that random or unit coefficient rules are universally superior to human judges.

    Main Methods:

    • An experiment was designed using the scheduling production problem.
    • The study compared actual costs incurred by unit rules, random rules, and human judgment.
    • Correlation coefficients between rule-based decisions and optimal decisions were analyzed alongside cost data.

    Main Results:

    • Unit and random coefficient rules yielded significantly higher costs than human judgment.
    • Despite high correlations with optimal decisions, these rules did not minimize actual production costs.
    • Human judgment proved more cost-effective in the tested scheduling production problem.

    Conclusions:

    • The superiority of linear decision rules with random or unit coefficients over human judgment is questionable in practical applications like production scheduling.
    • High correlation with optimal decisions does not guarantee optimal cost outcomes.
    • Human judgment can be more effective than simple linear rules when considering actual costs in complex operational environments.