‘Aversion dynamics’ scheduling when the system changes

Real schedulers are observed to avoid scheduling rare and expensive jobs immediately after preventive maintenance or a machine repair. The repairs or similar events produce ‘aversion’ in these jobs. The authors create a class of heuristics called aversion dynamics (AD) which focus on similar ideas of attraction and aversion in production planning. A preliminary heuristic (Averse-1) introducing the topic and related issues is formulated and the computational analysis is presented. AD is specially designed to piggy-back on a traditional heuristic and functions for a limited time after which the traditional heuristic regains control. The study shows that when impact results from an event such as a repair, Averse-1 significantly outperforms other dispatching heuristics for a wide range of scheduling problems. Since there is uncertainty about the expected impact, the heuristic is also analysed for the situations when the schedule is adjusted but the impact does not occur. In these cases, the heuristic takes a conservative posture (in hindsight) and sub-optimizes for a limited time. The study shows that while there is an added cost, the cost is relatively small. We conclude that (i) it is possible to reasonably extend traditional heuristics to include dynamic phenomena from the real world, and (ii) modelling the secondary impact of events is a significant factor in schedule generation.