Assessing the Accuracy of Expert-Based Decisions in Dispatching Ready Mixed Concrete

Abstract

In ready mixed concrete (RMC) dispatching, common practice is to rely on human experts for any necessary real-time decision making. This is because of the perceived complexity of RMC dispatching and the general lack of highly applicable optimization tools for the task. Critically, the accuracy of expert decisions (compared to optimization approaches) has not been comprehensively examined in the literature. To address the question of current practice expert accuracy in the context of optimized outcomes, this paper first mathematically models the RMC dispatching problem according to methods introduced in the literature. Two approaches are taken: integer programming (without time windows) and mixed-integer programming (with time windows). Further, the constructed models are tested with field data and compared to the decisions made by experts. The RMC data set has four depots and approximately 40 trucks that typically supply 40–200 deliveries per day. The results show that, on average, experts’ decisions are 90% accurate compared to the optimization models under examination. Finally, further investigation suggests that at least a portion of this optimality gap between expert and optimization models occurs because experts in critical situations accept a higher cost to ensure a more stable dispatching system.