The document discusses optimization models for locating ambulances to maximize coverage of emergency calls within a response time threshold. It summarizes three integer programming models of increasing complexity:
1. The maximal covering location problem considers call volumes and coverage areas but not backup coverage or balanced workloads.
2. The p-median model maximizes expected coverage considering non-deterministic travel times and balanced workloads but not backup coverage.
3. The final model extends the p-median model to maximize expected backup coverage by assigning priority responses from multiple ambulances to each call based on their availability probabilities.
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The main reference source used was 'Games, Theory and Applications' by L.C.Thomas. Further notes available at: http://bit.ly/nW6ULD
This presentation is an attempt to introduce Game Theory in one session. It's suitable for undergraduates. In practice, it's best used as a taster since only a portion of the material can be covered in an hour - topics can be chosen according to the interests of the class.
The main reference source used was 'Games, Theory and Applications' by L.C.Thomas. Further notes available at: http://bit.ly/nW6ULD
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I HOPE IT IS HELPFUL FOR YOU> BUT PLS IWANT CREDITS> OR ADD ME AND MESSAGE ME THANKS
THERE IS A NOTE FOR PRESENTERS VIEW
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KEEP CALM AND DRINK ON
NAME: Ellen Magalona
GNDR: FML
BRTHDY: FEB. 1998
@ellenmaaee
Introduction to operations research and mathematical modeling. Development of linear programming mathematical model. Solving linear mathematical models using the graphical method and simplex method. Integer programming and solving integer models using branch and bound method.
This presentation outlines the processes and benefits of applying enhanced maintenance planning techniques such as Reliability Centred Maintenance at your place of work. Please go to www.simenergy.co.uk for more information.
Top 10 most important manufacturing performance indicators in 2019MRPeasy
mrpeasy.com
In manufacturing, there are many outside influencing factors, tracking the performance of an operation with KPI metrics means the difference between success and failure. Here are the most important manufacturing performance indicators.
Queueing Theory- Waiting Line Model, Heizer and RenderAi Lun Wu
I HOPE IT IS HELPFUL FOR YOU> BUT PLS IWANT CREDITS> OR ADD ME AND MESSAGE ME THANKS
THERE IS A NOTE FOR PRESENTERS VIEW
HAVE A GOOD DAY
KEEP CALM AND DRINK ON
NAME: Ellen Magalona
GNDR: FML
BRTHDY: FEB. 1998
@ellenmaaee
Introduction to operations research and mathematical modeling. Development of linear programming mathematical model. Solving linear mathematical models using the graphical method and simplex method. Integer programming and solving integer models using branch and bound method.
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Signal, Sampling and signal quantizationSamS270368
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Note: You may need to download the file to see all of the animations.
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Statistical simulation technique that provides approximate solution to problems expressed mathematically.
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Here's the continuation of the report:
3.2.1 Parallel Plate Capacitor (continued)
As the IV fluid droplets move between the plates of the capacitor, the capacitance increases due to the change in the dielectric constant, resulting in the observation of a peak in capacitance.
3.2.2 Semi-cylindrical Capacitor
The semi-cylindrical capacitor consists of two semi-cylindrical conductors (plates) facing each other with a gap between them. The gap between the plates is filled with a dielectric material, typically the IV fluid.
When a potential difference is applied across the plates, electric field lines form between them. The dielectric material between the plates enhances the capacitance by reducing the electric field strength and increasing the charge storage capacity.
3.2.3 Cylindrical Cross Capacitor
The cylindrical cross capacitor is composed of two cylindrical conductors (rods) intersecting at right angles to form a cross shape. The space between the rods is filled with a dielectric material, such as the IV fluid.
When a potential difference is applied between the rods, electric field lines form between them. The dielectric material between the rods enhances the capacitance by reducing the electric field strength and increasing the charge storage capacity, similar to the semi-cylindrical design.
3.3 Advantages of Capacitive Sensing Approach
Capacitive sensing for IV fluid monitoring offers several advantages over other automated monitoring methods:
1. Non-invasive operation: The sensors do not require direct contact with the IV fluid, reducing the risk of contamination or disruption to the therapy.
2. High sensitivity: Capacitive sensors can detect minute changes in capacitance, enabling precise tracking of IV fluid droplets.
3. Low cost: The sensors can be constructed using relatively inexpensive materials, making them a cost-effective solution.
4. Low power consumption: Capacitive sensors typically have low power requirements, making them suitable for continuous monitoring applications.
5. Ease of implementation: The sensors can be easily integrated into existing IV setups without significant modifications.
6. Stable measurements: Capacitive sensors can provide stable and repeatable measurements across different IV fluid types.
Chapter 4: Experimental Setup and Results
4.1 Description of Experimental Setup
To evaluate the performance of capacitive sensors for IV fluid monitoring, an experimental setup was constructed. The setup included various capacitive sensor designs, such as parallel plate, semi-cylindrical, and cylindrical cross capacitors, positioned around an IV drip chamber.
The sensors were connected to a capacitance measurement circuit, which recorded the changes in capacitance as IV fluid droplets passed through the sensor's electric field. Multiple experiments were conducted using different IV fluid types and flow rates to assess the sensors' accuracy, repeatability, and sensitivity.
4.2 Measurements with
ME-314 Introduction to Control Engineering is a course taught to Mechanical Engineering senior undergrads. The course is taught by Dr. Bilal Siddiqui at DHA Suffa University. This lecture is about time response of systems derived by inspection of poles and zeros. First and second order systems are considered, along with higher order and nonminimum phase systems
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1. Integer programming for locating
ambulances
Laura Albert McLay
The Industrial & Systems Engineering Department
University of Wisconsin-Madison
laura@engr.wisc.edu
punkrockOR.wordpress.com
@lauramclay
1This work was in part supported by the U.S. Department of the Army under Grant Award Number W911NF-10-1-0176 and
by the National Science Foundation under Award No. CMMI -1054148, 1444219.
2. The problem
• We want to locate 𝑠 ambulances at stations in a
geographic region to “cover” the most calls in 9
minutes
• What we need to include:
1. Different call volumes at different locations
2. Non-deterministic travel times
3. Each ambulance responds to roughly the
same number of calls
4. Ambulances that are not always available
(backup coverage is important)
2
4. Anatomy of a 911 call
Response time
Service provider:
Emergency 911 call
Unit
dispatched
Unit is en
route
Unit arrives
at scene
Service/care
provided
Unit leaves
scene
Unit arrives
at hospital
Patient
transferred
Unit returns
to service
4
5. Objective functions
• NFPA standard yields a coverage objective function
for response time threshold (RTT)
• Most common RTT: nine minutes for 80% of calls
• A call with response time of 8:59 is covered
• A call with response time of 9:00 is not covered
Why RTTs?
• Easy to measure
• Intuitive
• Unambiguous
5
6. Coverage models for EMS
• Expected coverage objective
• Maximize expected number of calls covered by a 9
minute response time interval
• Coverage issues:
• Ambulance unavailability: Ambulances not available
when servicing a patient (spatial queuing)
• Fractional coverage: coverage is not binary due to
uncertain travel times
• Other issues:
• Which ambulance to send? As backup?
• Side constraints:
• Balanced workload
6
8. Why use optimization models?
Because it helps you identify solutions that are not
intuitive. This adds value!
8
MODEL
9. Model 1: covering location models
Adjusts for different call volumes at different locations (#1),
but does not include our other needs
9
10. Model 1 formulation
Parameters
• 𝑁 = set of demand locations
• 𝑆 = set of stations
• 𝑑𝑖 = demand at 𝑖 ∈ 𝑁
• 𝑟𝑖𝑗= fraction of calls at location 𝑖 ∈
𝑁 that can be reached by 9
minutes from an ambulance from
station 𝑗 ∈ 𝑆.
• When travel times are
deterministic, then 𝑟𝑖𝑗 ∈ {0, 1}
• 𝐽𝑖 ⊂ 𝑆 = subset of stations that can
respond to calls at 𝑖 within 9
minutes, 𝑖 ∈ 𝑁:
• 𝐽𝑖 = 𝑗: 𝑟𝑖𝑗 = 1
• 𝐽𝑖 = all stations that encircle 𝑖
Decision variables
• 𝑦𝑗 = 1 if we locate an ambulance
at station 𝑗 ∈ 𝑆 (and 0 otherwise)
• 𝑥𝑖 = 1 if calls at 𝑖 ∈ 𝑁 are covered
(and 0 otherwise)
• We must locate all 𝑆 ambulances
at stations
• Linking constraint: a location 𝑖 ∈
𝑁 is covered only if one of the
stations in 𝐽𝑖 has an ambulance
• Integrality constraints on the
variables
10
Constraints (in words)
11. Maximal Covering Location Problem #1
max
𝑖∈𝑁
𝑑𝑖 𝑥𝑖
Subject to:
𝑥𝑖 ≤
𝑗∈𝐽 𝑖
𝑦𝑗
𝑗∈𝑆
𝑦𝑗 = 𝑠
𝑥𝑖 ∈ 0, 1 , 𝑖 ∈ 𝑁
𝑦𝑖 ∈ 0, 1 , 𝑗 ∈ 𝑆
11
Church, Richard, and Charles R. Velle. "The maximal covering location problem." Papers in
regional science 32, no. 1 (1974): 101-118.
13. Example solution
Model 1 solutions
0 1 2 3 4 5 6 7 8 9 10
0
1
2
3
4
5
6
7
8
9
10
Limitations
13
• Does not look at backup coverage
• Assumes all calls in circles are 100%
“covered”
• Does not assign calls to stations
• Each ambulance does not respond to
same number of calls
14. Model 2: p-median models to
maximize expected coverage
Addresses:
1. Different call volumes at different locations
2. Non-deterministic travel times
3. Each ambulance responds to roughly the same number of calls
Does not address #4: backup coverage
14
17. Model formulation
Parameters
• 𝑁 = set of demand locations
• 𝑆 = set of stations
• 𝑑𝑖 = demand at 𝑖 ∈ 𝑁
• 𝑟𝑖𝑗= fraction of calls at location
𝑖 ∈ 𝑁 that can be reached by 9
minutes from an ambulance
from station 𝑗 ∈ 𝑆.
• 𝒓𝒊𝒋 ∈ 𝟎, 𝟏 (fractional!)
• 𝒍 = lower bound on number of
calls assigned to each open
station
• 𝑐 = capacity of each station (max
number of ambulances, 𝑐 = 1)
Decision variables
• 𝑦𝑗 = 1 if we locate an ambulance at
station 𝑗 ∈ 𝑆 (and 0 otherwise)
• 𝒙𝒊𝒋 = 1 if calls at 𝒊 ∈ 𝑵 are assigned
to station 𝒋 (and 0 otherwise)
• We must locate all 𝑠 ambulances at
stations
• Each open station must have at least
𝒍 calls assigned to it
• Linking constraint: a location 𝒊 ∈ 𝑵
can be assigned to station 𝒋 only if 𝒋
has an ambulance
• Each location must be assigned to at
most one (open) station
• Integrality constraints on the
variables
17
Constraints (in words)
18. Integer programming bag of tricks
• 𝛿 = 1 → 𝑗∈𝑁 𝑎𝑗 𝑥𝑗 ≤ 𝑏
• 𝑗∈𝑁 𝑎𝑗 𝑥𝑗 + 𝑀 𝛿 ≤ 𝑀 + 𝑏
• 𝑗∈𝑁 𝑎𝑗 𝑥𝑗 ≤ 𝑏 → 𝛿 = 1
• 𝑗∈𝑁 𝑎𝑗 𝑥𝑗 − 𝑚 − 𝜖 𝛿 ≤ 𝑏 + 𝜖
• 𝛿 = 1 → 𝑗∈𝑁 𝑎𝑗 𝑥𝑗 ≥ 𝑏
• 𝑗∈𝑁 𝑎𝑗 𝑥𝑗 + 𝑚 𝛿 ≥ 𝑚 + 𝑏
• 𝑗∈𝑁 𝑎𝑗 𝑥𝑗 ≥ 𝑏 → 𝛿 = 1
• 𝑗∈𝑁 𝑎𝑗 𝑥𝑗 − 𝑀 + 𝜖 𝛿 ≤ 𝑏 − 𝜖
• 𝛿 is a binary variable
• 𝑗∈𝑁 𝑎𝑗 𝑥𝑗 constraint
LHS
• 𝑏: constraint RHS
• 𝑀: upper bound on
𝑗∈𝑁 𝑎𝑗 𝑥𝑗 − 𝑏
• 𝑚: lower bound on
𝑗∈𝑁 𝑎𝑗 𝑥𝑗 − 𝑏
• 𝜖: constraint violation
amount (0.01 or 1)
18
Each open station must have at least 𝒍 calls assigned to it
20. Bag of tricks
• We want to use this one:
• 𝛿 = 1 → 𝑗∈𝑁 𝑎𝑗 𝑥𝑗 ≥ 𝑏
• 𝑗∈𝑁 𝑎𝑗 𝑥𝑗 + 𝑚 𝛿 ≥ 𝑚 + 𝑏
For this:
• 𝑦𝑗 = 1 → 𝑖∈𝑁 𝑑𝑖 𝑥𝑖𝑗 ≥ 𝑙
• Step 1:
• Find 𝑚: lower bound on 𝑖∈𝑁 𝑑𝑖 𝑥𝑖𝑗 − 𝑙
• This is −𝑙 since we could assign no calls to 𝑗
• Step 2: Put it together and simplify
• 𝑖∈𝑁 𝑑𝑖 𝑥𝑖𝑗 − 𝑙 𝑦𝑗 ≥ −𝑙 + 𝑙 simplifies to
• 𝑖∈𝑁 𝑑𝑖 𝑥𝑖𝑗 ≥ 𝑙 𝑦𝑗
• Note: this will also work when we let up to 𝑐
ambulances located at a station
• 𝑦𝑖 ∈ 0, 1, … , 𝑐
20
23. Model 3: p-median models to maximize
expected (backup) coverage
Addresses:
1. Different call volumes at different locations
2. Non-deterministic travel times
3. Each ambulance responds to roughly the same number of calls
4. Ambulances that are not always available (backup coverage is
important)
23
24. Ambulances that are not always available
Let’s model this as follows:
• Let’s pick the top 3 ambulances that should respond to each call
• Ambulance 1, 2, 3 responds to a call with probability 𝜋1, 𝜋2, 𝜋3
with 𝜋1 + 𝜋2 + 𝜋3 < 1.
Ambulances are busy with probability 𝜌
1. First choice ambulance response with probability π1 ≈ 1 − 𝜌
2. Second choice ambulance response with probability π2 ≈
𝜌(1 − 𝜌)
3. Third choice ambulance response with probability π3 ≈
𝜌2 1 − 𝜌
• If 𝜌 = 0.3 then 𝜋1 = 0.7, 𝜋2 = 0.21, 𝜋3 = 0.063 (sum to 0.973)
• If 𝜌 = 0.5 then 𝜋1 = 0.5, 𝜋2 = 0.25, 𝜋3 = 0.125 (sum to 0.875)
24
25. New variables
We need to change this variable:
• 𝑥𝑖𝑗 = 1 if calls at 𝑖 ∈ 𝑁 are assigned to station 𝑗 (and
0 otherwise)
to this:
• 𝑥𝑖𝑗𝑘 = 1 if calls at 𝑖 ∈ 𝑁 are assigned to station 𝑗 at
the 𝑘 𝑡ℎ priority, 𝑘 = 1, 2, 3.
Note: this is cool!
This tells us which ambulance to send, not just where
to locate the ambulances.
25
26. Model formulation
Parameters
• 𝑁 = set of demand locations
• 𝑆 = set of stations
• 𝑑𝑖 = demand at 𝑖 ∈ 𝑁
• 𝑟𝑖𝑗= fraction of calls at location
𝑖 ∈ 𝑁 that can be reached by 9
minutes from an ambulance
from station 𝑗 ∈ 𝑆.
• 𝑟𝑖𝑗 ∈ 0,1
• 𝑙 = lower bound on number of
calls assigned to each open
station
• 𝝅 𝟏, 𝝅 𝟐, 𝝅 𝟑 = proportion of calls
when the 1st, 2nd, and 3rd
preferred ambulance responds
Decision variables
• 𝑦𝑗 = 1 if we locate an ambulance at
station 𝑗 ∈ 𝑆 (and 0 otherwise)
• 𝒙𝒊𝒋𝒌 = 1 if station 𝒋 is the 𝒌th preferred
ambulance for calls at 𝒊 ∈ 𝑵, 𝒌 = 𝟏, 𝟐, 𝟑.
• We must locate all 𝑆 ambulances at
stations (at most one per station)
• Each open station must have at least
𝑙 calls assigned to it
• Linking constraint: a location 𝑖 ∈ 𝑁 can be
assigned to station 𝑗 only if 𝑗 has an
ambulance
• Each location must be assigned to 3
(open) stations
• 3 different stations
• Stations must be assigned in a specified
order
• Integrality constraints on the variables26
Constraints (in words)
29. Related blog posts
• A YouTube video about my research
• In defense of model simplicity
• Operations research, disasters, and science
communication
• Domino optimization art
29