Aggregate capacity planning for elective surgeries: A bi-objective optimization approach to balance patients waiting with healthcare costs

The usual bottleneck resource in a healthcare chain is the operating room (OR) which has to be scheduled in advance for different elective surgeries. With an ever growing demand, the treatment capacity has lagged behind resulting in the queuing up of patients for their turns to undergo the elective surgery commonly referred to as the ‘waiting lists’. The last two decades have seen a grown emphasis for reducing the number of patients in the waiting lists and an important public policy tool to tackle this has been some form of ‘maximum waiting time guarantee’. The waiting time guarantees are backed with additional resources making it an effective policy tool in restricting the patients waiting times. But, the additional funding also increases the healthcare expenditure which shows that the healthcare capacity planning is faced with competing objectives. This study uniquely formulates and solves a bi-objective healthcare aggregate capacity planning problem to simultaneously minimize the number of patients waiting for an elective surgery and the associated costs. The multi-objective combinatorial optimization problem of allocating the Operating Room (OR) capacity to different surgical specialties is solved by the Non Dominated Sorting Genetic Algorithm (NSGA II). This gives a range of Pareto plans that have important managerial repercussions because it may help set and analyze the impact of various waiting time guarantees.