Resource loading under uncertainty

The objective of this research is to develop resource loading methods that can deal with ETO inherent uncertainties

A scenario based approach for flexible resource loading under uncertainty

Order acceptance decisions in manufacture-to-order environments are often made based on incomplete or uncertain information. To promise reliable due dates and to manage resource capacity adequately, resource capacity loading is an indispensable supporting tool. We propose a scenario based approach for resource loading under uncertainty that minimises the expected costs. The approach uses an MILP to find a plan that has minimum expected costs over all relevant scenarios. We propose an exact and a heuristic solution approach to solve this MILP. A disadvantage of this approach is that the MILP may become too large to solve in reasonable time. We therefore propose another approach that uses an MILP with a sample of all scenarios. We use the same exact and heuristic methods to solve this MILP. Computational experiments show that, especially for instances with much slack, solutions obtained with deterministic techniques for a expected scenario can be improved with respect to their expected costs. We also show that for large instances the heuristic outperforms the exact approach given a computation time as a stopping criterion

A norm utilisation for scarce hospital resources: Evidence from operating rooms in a Dutch university hospital

Background:\ud Utilisation of operating rooms is high on the agenda of hospital managers and researchers. Many efforts in the area of maximising the utilisation have been focussed on finding the holy grail of 100% utilisation. The utilisation that can be realised, however, depends on the patient mix and the willingness to accept the risk of working in overtime.\ud \ud Materials and methods:\ud This is a mathematical modelling study that investigates the association between the utilisation and the patient mix that is served and the risk of working in overtime. Prospectively, consecutively, and routinely collected data of an operating room department in a Dutch university hospital are used. Basic statistical principles are used to establish the relation between realistic utilisation rates, patient mixes, and accepted risk of overtime.\ud \ud Results:\ud Accepting a low risk of overtime combined with a complex patient mix results a low utilisation rate. If the accepted risk of overtime is higher and the patient mix is less complex, the utilisation rate that can be reached is closer to 100%.\ud \ud Conclusion:\ud Because of the inherent variability of health-care processes, the holy grail of 100% utilisation is unlikely to be found. The method proposed in this paper calculates a realistic benchmark utilisation that incorporates the patient mix characteristics and the willingness to accept risk of overtime

Optimizing intensive care capacity using individual length-of-stay prediction models

Introduction Effective planning of elective surgical procedures requiring postoperative intensive care is important in preventing cancellations and empty intensive care unit (ICU) beds. To improve planning, we constructed, validated and tested three models designed to predict length of stay (LOS) in the ICU in individual patients. Methods Retrospective data were collected from 518 consecutive patients who underwent oesophagectomy with reconstruction for carcinoma between January 1997 and April 2005. Three multivariable linear regression models for LOS, namely preoperative, postoperative and intra-ICU, were constructed using these data. Internal validation was assessed using bootstrap sampling in order to obtain validated estimates of the explained variance (r2). To determine the potential gain of the best performing model in day-to-day clinical practice, prospective data from a second cohort of 65 consecutive patients undergoing oesophagectomy between May 2005 and April 2006 were used in the model, and the predictive performance of the model was compared with prediction based on mean LOS. Results The intra-ICU model had an r2 of 45% after internal validation. Important prognostic variables for LOS included greater patient age, comorbidity, type of surgical approach, intraoperative respiratory minute volume and complications occurring within 72 hours in the ICU. The potential gain of the best model in day-to-day clinical practice was determined relative to mean LOS. Use of the model reduced the deficit number (underestimation) of ICU days by 65 and increased the excess number (overestimation) of ICU days by 23 for the cohort of 65 patients. A conservative analysis conducted in the second, prospective cohort of patients revealed that 7% more oesophagectomies could have been accommodated, and 15% of cancelled procedures could have been prevented. Conclusion Patient characteristics can be used to create models that will help in predicting LOS in the ICU. This will result in more efficient use of ICU beds and fewe

Robust surgery loading

We consider the robust surgery loading problem for a hospital’s operating theatre department, which concerns assigning surgeries and sufficient planned slack to operating room days. The objective is to maximize capacity utilization and minimize the risk of overtime, and thus cancelled patients. This research was performed in collaboration with the Erasmus MC, a large academic hospital in the Netherlands, which has also provided historical data for the experiments. We propose various constructive heuristics and local search methods that use statistical information on surgery durations to exploit the portfolio effect, and thereby to minimize the required slack. We demonstrate that our approach frees a lot of operating room capacity, which may be used to perform additional surgeries. Furthermore, we show that by combining advanced optimization techniques with extensive historical statistical records on surgery durations can significantly improve the operating room department utilization

Dealing With Uncertainty in Multi-Project Rough-Cut Capacity Planning

In this presentation we propose a method for dealing with uncertainty in the resource-constrained multi-project planning (RCCP) problem, by incorporating stochasticity. This tactical planning level is typically characterized by many uncertainties, such as processing time uncertainty, project release date delay, network uncertainty, rush orders, resource availability, project scenarios, etc. So far

Scenario-based approach for flexible resource loading under uncertainty

Order acceptance decisions in manufacture-to-order environments are often made based on incomplete or uncertain information. To promise reliable due dates and to manage resource capacity adequately, resource capacity loading is an indispensable supporting tool. We propose a scenario based approach for resource loading under uncertainty that minimises the expected costs. The approach uses an MILP to find a plan that has minimum expected costs over all relevant scenarios. We propose an exact and a heuristic solution approach to solve this MILP. A disadvantage of this approach is that the MILP may become too large to solve in reasonable time. We therefore propose another approach that uses an MILP with a sample of all scenarios. We use the same exact and heuristic methods to solve this MILP. Computational experiments show that, especially for instances with much slack, solutions obtained with deterministic techniques for a expected scenario can be improved with respect to their expected costs. We also show that for large instances the heuristic outperforms the exact approach given a computation time as a stopping criterion