Predictive, stochastic and dynamic extensions to aversion dynamics scheduling

Schedulers' decisions in real factories deal with perceived risks and impacts. They proactively anticipate and reactively mitigate risky events by altering what would be considered a normal schedule to minimize associated impacts. These risk mitigation concepts are called aversion dynamics (AD). Aversion dynamics describes the aversion that jobs exhibit to impacts resulting from risky events in dynamic and unstable production environments. The aversion manifests itself in either advancing or delaying the work to avoid the risky period. This paper extends the first AD heuristic, Averse-1, to capture additional real-world dynamics and to make the heuristic predictive (proactive) as to when the perceived risky event may happen. In particular, predictive and stochastic elements are incorporated within a dynamic job arrival framework to create an extended heuristic called Averse-2.