Abstract
AbstractOne problem management scientists face in adapting heuristics to actual applications on the factory floor is eliciting preferences from plant managers about the importance of different jobs. Even when the goal of the firm is apparently as straightforward as profit maximization, reputation and goodwill can play such critical roles in affecting the revenue flow, that exclusively focusing on a few key variables (such as the price of individual items, their production times, and the raw material cost) may give a distorted picture about which jobs the firm values most highly. This paper offers a new way of eliciting preferences, utilizing high‐frequency data on plant operations that are routinely collected by many firms, in order to infer the direct and indirect cost of scheduling jobs, from actual job schedules that managers reveal by their choices. We then apply our method to a scheduling problem in a steel tube manufacturing plant. After estimating the preferences of the plant manager, we demonstrate how our estimates can be used to evaluate heuristics for hard scheduling problems, and to forecast the effects of structural change, such as expansion in plant capacity, or shifts in job order flow.
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