Para
Chicago,
USA
Location
June 2020 
Jan 2021
Date
Industrial Engineer Fellowship
Role
Mentor
Stochastic Optimization
Data Analysis
Skills
Professor
Seyed Iravani
Maximizing Probability: Shift Assignment
The goal is to develop an algorithm that can maximize the probability that a nurse accepts a shift, once matched, based on their preference, location, qualifications, and payment.
As labor cost makes up a large share of healthcare facilities costs, it is crucial to create an adequate staffing and scheduling system for nurses to help drive the operating costs down.
The U.S. is projected to experience a shortage of Registered Nurses that is expected to intensify as Baby Boomers age and the need for health care grows. With the projected shortage in nurses, there is an increase in the workload and working hours for nurses which are affecting the quality of healthcare in the hospitals.
Research
Problem Analysis
I Identified other healthcare facilities that face similar problems and industry solutions to address them
Mathematical Formulation
Designed a stochastic optimizing model in atRisk to increase the probability that nurses fill all shifts with 55% improvement from the original rate
Challenges

First, I needed to design a nurseshift matching model based on the nurses' degrees, certifications, work experience, and years of experience.

The matching model would reduce the number of vacant shifts on the platform through the available set of nurses.


Second, it is not a guarantee that nurses will accept to work in a matched shift.

The stochastic algorithms can help predict the probability that the shifts are filled with different match combinations and identify the best match combination to show nurses the shifts.

Model Design
The model focuses on two keys aspects, matching nurses to a shift and maximizing the probability that nurses accept a shift through controlling the shift content that nurses see.

Sets:

𝑖 𝜖 𝐼 {1,...,10}: (Nurses)

𝑗 𝜖 𝐽 {1,...,3}: (Hospitals)

𝑘 𝜖 𝐾 {1,...,3}: (Shifts)

𝑟 𝜖 𝑅 {1,...,5}: (Certifications)

𝑡 𝜖 𝑇 {1,...,3}: (Vaccines)


Parameters:

𝑃𝑖𝑗𝑘: The preference value for nurse i to work at hospital j at shift k; 0 otherwise

𝐷𝑖𝑝: 1 if nurse i works at department p; 0 otherwise

𝐶𝑖𝑟: 1 if nurse i has certification r; 0 otherwise

𝑉𝑖𝑡: 1 if nurse i has vaccine t; 0 otherwise

𝑌𝑖𝑠𝑝: Nurse i has s years of experience department p; 0 otherwise


Decision Variable:

𝑋𝑖𝑗𝑘: 1 𝑖𝑓 𝑛𝑢𝑟𝑠𝑒 𝒊 𝑔𝑒𝑡𝑠 𝑎𝑠𝑠𝑖𝑔𝑛𝑒𝑑 𝑎𝑡 ℎ𝑜𝑠𝑝𝑖𝑡𝑎𝑙 𝒋 𝑡𝑜 𝑤𝑜𝑟𝑘 𝑠ℎ𝑖𝑓𝑡 𝒌; 0 otherwise


Objective Function:

Maximize sum {i in I} {j in J} 𝑋𝑖𝑗𝑘 × 𝑃𝑖𝑗𝑘

Three nurses availability are to work in three shifts are in the table
Three nurses availability are to work in three shifts are in the table
I multiplied the number of nurses by the number of shifts to account for all scenario possibilities
Through comparing the Stochastic Optimization to PARA's algorithm, it is noticeable that for fulfilling all three shifts. The probability is twice as high in the new algorithm compared to the traditional method that PARA uses.
Takeaways

Stochastic Algorithm offers a new approach to redesign the nurseshift assignment by taking into account the nurse's probabilities of accepting and rejecting shifts.

Analysis of nurses' working schedules can enhance the performance of the algorithm
My Ventures
Stocks
Analyzing banks S&P 500 companies recommendations. Surprisingly, there were no trackers following the performance of analyst picks over the long term and I decided to build one.
Logistics
Stroke Code
Redesigning the CODE stroke activation process to reduce the Doorindoor out time for stroke patients
PROJECT WILL BE AVAILABLE AUGUST 2021