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The Uncertainty Principle

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...

Uncertainty in Measurement: Reading Instruments

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...

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Uncertainty in Measurement: Significant Figures

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

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A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

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Multi-objective integrated planning and scheduling model for operating rooms under uncertainty.

Javad Ansarifar¹, Reza Tavakkoli-Moghaddam^1,2, Faezeh Akhavizadegan¹

¹1 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine

|September 22, 2018

Summary

This study optimizes operating room scheduling by integrating real-world constraints and uncertainty. The developed model, solved using meta-heuristic algorithms, significantly improves upon manual scheduling for efficiency and resource utilization.

Keywords:

Scheduling decision-making style multi stages operating rooms planning uncertainty

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Area of Science:

Operations Research
Healthcare Management
Applied Mathematics

Background:

Operating room (OR) scheduling is complex, involving numerous real-world constraints.
Existing scheduling methods often fail to account for decision-making styles, multi-stage surgeries, and resource time windows.
Uncertainty in surgical processes impacts efficiency and resource allocation.

Purpose of the Study:

To develop an integrated mathematical programming model for OR planning and scheduling.
To incorporate decision-making styles, multi-stage surgeries, resource constraints, and uncertainty into the OR scheduling model.
To optimize net revenues, minimize decision-making inconsistency, and maximize OR utilization.

Main Methods:

Formulation of a fuzzy possibilistic-stochastic mathematical programming approach.
Integration of multi-objective optimization considering revenue, human resource consistency, and OR utilization.
Application of Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) for solving the model.
Validation using test problems based on data from a public hospital in Iran.

Main Results:

NSGA-II demonstrated superior performance compared to MOPSO across multiple metrics for OR scheduling.
The proposed model significantly outperforms manual scheduling in terms of efficiency and effectiveness.
The model successfully balances competing objectives: maximizing revenue, ensuring consistency, and optimizing resource use.

Conclusions:

The developed integrated model provides an effective and efficient solution for OR planning and scheduling.
Meta-heuristic algorithms, particularly NSGA-II, are well-suited for solving complex multi-objective OR scheduling problems.
The findings highlight the potential for significant improvements in healthcare operations through advanced mathematical modeling and optimization techniques.