The document describes a thesis that proposes algorithms for scheduling, path planning, and resource management for a fully automated multi-story parking structure. It introduces the problem of managing such complex parking structures and the need for intelligent algorithms. The methodology involves algorithms for path planning vehicles using D* Lite, locating empty spaces using uniform cost search, and scheduling elevators. A simulation model is created to test the algorithms and evaluate performance.
Advances in Heavy-Duty Charging by Ruth LiddellForth
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The document discusses the theoretical calculation of electron mobility in indium nitride (InN) semiconductor using Monte Carlo simulation. Key points:
- Monte Carlo simulation is used to calculate electron mobility by simulating random scattering events.
- The model tracks electron wave vectors and determines relaxation times between scattering to calculate mean relaxation time and thus mobility.
- Simulation results found mobility of 25,102 cm2/V-s at 77K and carrier concentration of 1016 cm-3. Mobility decreased with increasing temperature and carrier concentration as expected.
- The maximum mobility from simulation matches well with results from other theoretical methods, validating the Monte Carlo model for InN electron mobility.
Get a foot in the door careers convention 2011 finalmuovorecruit
This document summarizes a careers convention held on November 24th, 2011 at KSU's common room. It provides tips for graduates on getting employed, including having a structured CV, cover letter, experience, and preparing for interviews. Potential interview questions are listed, as well as expectations for interviews. The document emphasizes gaining relevant experience and preparing for behavioral interviews by reviewing one's own projects and how they solved problems. It promotes connecting with recruiters to find the right employers and experience before graduating.
Advances in Heavy-Duty Charging by Ruth LiddellForth
ABB is a leader in electric vehicle charging infrastructure, having sold over 17,000 DC fast chargers globally. The presentation discusses advances in heavy-duty electric vehicle charging and infrastructure, including differences between light and heavy-duty equipment. It also summarizes a project between ABB and Volvo to provide DC fast and depot chargers for electric trucks in the Los Angeles region. Key takeaways for scaling electric truck fleets are engaging with utilities early, ensuring vehicle interoperability, optimizing asset utilization through smart charging and fleet management systems, and integrating grid solutions like energy storage.
This document discusses Caltrain's plans for level boarding. Level boarding reduces gaps between trains and platforms to improve safety, accessibility, and efficiency. It describes Caltrain's 33 stations and shared platforms with other rail services. Caltrain plans to electrify its service and purchase multi-level electric multiple units that would allow level boarding with 25-inch high platforms. Implementing level boarding across the system would require raising platforms at 27 stations and building dedicated platforms for other rail services to comply with regulations while improving accessibility. Next steps include public input and planning to inform vehicle procurement and infrastructure changes.
Under JST RISTEX S3FIRE program, we are trying to implement Smart Access Vehicle (SAV) Service in Hakodate. The project adopts the method of service science loop - the repeated cycle of observation, design and implementation. In this paper we report the completion of its first cycle, and discuss how the cycle improved our initial design. We first conducted person trip research in Hakodate. We chose 20 candidates of various age and occupation, and recorded their everyday movements for four months. We then analyzed the result and made a person trip model. The model was then fed into our multi-agent simulator for Hakodate public transportation system. We conducted a small field test with five vehicles for one week. The most significant achievement is that we confirmed that our design of SAV system works. We succeeded in automatically dispatching five vehicles for eleven hours without any significant trouble or human supervision.
The document discusses the theoretical calculation of electron mobility in indium nitride (InN) semiconductor using Monte Carlo simulation. Key points:
- Monte Carlo simulation is used to calculate electron mobility by simulating random scattering events.
- The model tracks electron wave vectors and determines relaxation times between scattering to calculate mean relaxation time and thus mobility.
- Simulation results found mobility of 25,102 cm2/V-s at 77K and carrier concentration of 1016 cm-3. Mobility decreased with increasing temperature and carrier concentration as expected.
- The maximum mobility from simulation matches well with results from other theoretical methods, validating the Monte Carlo model for InN electron mobility.
Get a foot in the door careers convention 2011 finalmuovorecruit
This document summarizes a careers convention held on November 24th, 2011 at KSU's common room. It provides tips for graduates on getting employed, including having a structured CV, cover letter, experience, and preparing for interviews. Potential interview questions are listed, as well as expectations for interviews. The document emphasizes gaining relevant experience and preparing for behavioral interviews by reviewing one's own projects and how they solved problems. It promotes connecting with recruiters to find the right employers and experience before graduating.
The document appears to be notes from a visit to a mall, as it mentions taking photos and describes the mall as a colorful and friendly place with a lovely top deck view. The notes reflect positive impressions of the mall.
The document discusses various editing techniques used in creating a music video. Scenes were cut together and a flicker effect was used to transition between shots for a continuous flow. An additional transparent layer was added to one scene to give the illusion of a filter. One clip was slowed down to show smoke exhaling when the lyrics mentioned substances. The full video was rewound at one point to show the artist's journey corresponding with the changing lyrics. One scene with closed eyes was slowed to link to the next scene in a different environment.
Marcus visits the grave of his deceased wife Sara on the 10th anniversary of her death. He remembers the man responsible, Arthur Banks, a rich and powerful antagonist. Marcus has flashbacks to Sara's death and funeral, and has an emotional breakdown at her grave. In the present, Marcus tracks down Arthur to avenge Sara's death, stalking him for weeks. The opening sequence ends with Marcus holding a gun to Arthur's head in the park.
Este documento trata sobre la inteligencia emocional y las habilidades sociales. Explica conceptos como emociones, sentimientos, inteligencia emocional, sus componentes interpersonales e intrapersonales. También cubre la importancia de desarrollar la inteligencia emocional, especialmente para quienes trabajan en el campo de la atención a la dependencia. Finalmente, discute los patrones de educación emocional y cómo estas habilidades pueden educarse a lo largo de la vida.
This document evaluates which media institution might distribute a thriller media product. It considers Universal Pictures, a major Hollywood studio known for thriller films distributed worldwide, and Warp Films, a smaller UK-based independent studio focusing on realistic films set in everyday life. While Universal could distribute the product worldwide for a large audience familiar with the thriller genre, Warp Films may be better for production since the film's everyday setting would appeal more to UK audiences and fit Warp's style. Overall, Universal is chosen for distribution for its worldwide reach and experience with thriller conventions, though Warp's approach could work well for production.
The document provides an overview of establishing a career in digital marketing. It discusses the differences between digital and traditional marketing, key areas of digital marketing like content marketing, social media marketing, and analytics. It also outlines growth areas in digital marketing and the 9 key characteristics of highly effective digital marketers, which include being autonomous, multi-disciplinary, data-driven, and maintaining knowledge of new trends and tools. The document uses a case study example to demonstrate how to develop a digital marketing strategy to promote a digital marketing course.
Automated Container Terminal Planning Larry Samson
Automated stacking cranes (ASCs) are a common approach for automated container terminals. ASCs pick containers from the end of stacks in the container yard and transfer them to or from desired storage positions perpendicular to the quay. This separation of gate and vessel traffic allows for automated transport between the quay and yard. While automated terminals have struggled with quay crane productivity, manual shuttles have achieved high productivity and require less testing than fully automated options. Terminal layout and cargo mix, such as the proportion of transshipment or rail cargo, influence the best horizontal transport and ASC alignment options.
The document provides an overview of automated guided vehicle systems (AGVS). It discusses the key components of an AGVS including the vehicle, guide path, control unit and computer interface. It describes different types of AGVS vehicles including driverless trains, pallet trucks and unit load carriers. The document also covers the history of AGVS, vehicle functions, guidance systems, safety features, control systems, communications and important considerations for AGVS design.
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The document proposes a new autonomous transportation concept called BaseTracK. It is based on "virtual rails" or predetermined trajectories that vehicles can follow autonomously. BaseTracK aims to make autonomous transportation more accessible by starting with limited assistance systems rather than requiring full autonomy. It involves digitizing routes, installing automation kits in vehicles to guide them along routes, and providing access to routes via subscription. The concept has the potential to be applied to various transport modes on roads, waterways and in the air. It is presented as a simpler and more reliable alternative to existing autonomous driving systems that can operate in various weather conditions without huge computing power requirements.
The document discusses plans to modernize the Carriage Stores Depot in Lower Parel, Mumbai through automation. It will involve installing an Intelligent Warehousing Automated Storage and Retrieval System (IW-ASRS) to optimize storage capacity. The estimated cost is Rs. 138 million and will enhance inventory management through RFID and barcode technology. It aims to improve productivity and support increasing coach repair targets.
The document discusses plans to modernize the Carriage Stores Depot in Lower Parel, Mumbai through automation. It will involve installing an Intelligent Warehousing Automated Storage and Retrieval System (IW-ASRS) to optimize storage capacity. The estimated cost is Rs. 138 million and will enhance inventory management through RFID and barcode technology. It aims to improve productivity and support the increasing coach refurbishment targets of the workshop located in a space-constrained area.
Integrate with AGVs - Webinar Presentation by FlexQubeAnders Fogelberg
- What is an AGV?
- History of the AGV
- AGV tasks in material handling
- Why use AGVs?
- Different types of AGVs
- AGV guidance methods
- Designing carts for AGV
The document discusses automated parking systems as a solution to increasing parking demand. It provides an introduction to automated parking systems, including how they work and the benefits they provide like space savings, security, and convenience. It then discusses the history of automated parking, need for these systems, various types of automated parking systems like stack, puzzle, cart, and rotary systems. It covers the basic concepts, components, advantages and disadvantages of these automated parking solutions.
This document provides information on an automated parking system project presented by architecture students. It begins with an introduction to automated parking systems, noting their advantages of space and time savings. It then discusses the history and types of automated parking systems, including fully automated, semi-automated, stack, puzzle, cart, tower, chess, and rotary systems. The document outlines the key components, specifications, and advantages of these different system types. It also covers planning and design strategies as well as code requirements for automated parking systems.
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The document appears to be notes from a visit to a mall, as it mentions taking photos and describes the mall as a colorful and friendly place with a lovely top deck view. The notes reflect positive impressions of the mall.
The document discusses various editing techniques used in creating a music video. Scenes were cut together and a flicker effect was used to transition between shots for a continuous flow. An additional transparent layer was added to one scene to give the illusion of a filter. One clip was slowed down to show smoke exhaling when the lyrics mentioned substances. The full video was rewound at one point to show the artist's journey corresponding with the changing lyrics. One scene with closed eyes was slowed to link to the next scene in a different environment.
Marcus visits the grave of his deceased wife Sara on the 10th anniversary of her death. He remembers the man responsible, Arthur Banks, a rich and powerful antagonist. Marcus has flashbacks to Sara's death and funeral, and has an emotional breakdown at her grave. In the present, Marcus tracks down Arthur to avenge Sara's death, stalking him for weeks. The opening sequence ends with Marcus holding a gun to Arthur's head in the park.
Este documento trata sobre la inteligencia emocional y las habilidades sociales. Explica conceptos como emociones, sentimientos, inteligencia emocional, sus componentes interpersonales e intrapersonales. También cubre la importancia de desarrollar la inteligencia emocional, especialmente para quienes trabajan en el campo de la atención a la dependencia. Finalmente, discute los patrones de educación emocional y cómo estas habilidades pueden educarse a lo largo de la vida.
This document evaluates which media institution might distribute a thriller media product. It considers Universal Pictures, a major Hollywood studio known for thriller films distributed worldwide, and Warp Films, a smaller UK-based independent studio focusing on realistic films set in everyday life. While Universal could distribute the product worldwide for a large audience familiar with the thriller genre, Warp Films may be better for production since the film's everyday setting would appeal more to UK audiences and fit Warp's style. Overall, Universal is chosen for distribution for its worldwide reach and experience with thriller conventions, though Warp's approach could work well for production.
The document provides an overview of establishing a career in digital marketing. It discusses the differences between digital and traditional marketing, key areas of digital marketing like content marketing, social media marketing, and analytics. It also outlines growth areas in digital marketing and the 9 key characteristics of highly effective digital marketers, which include being autonomous, multi-disciplinary, data-driven, and maintaining knowledge of new trends and tools. The document uses a case study example to demonstrate how to develop a digital marketing strategy to promote a digital marketing course.
Automated Container Terminal Planning Larry Samson
Automated stacking cranes (ASCs) are a common approach for automated container terminals. ASCs pick containers from the end of stacks in the container yard and transfer them to or from desired storage positions perpendicular to the quay. This separation of gate and vessel traffic allows for automated transport between the quay and yard. While automated terminals have struggled with quay crane productivity, manual shuttles have achieved high productivity and require less testing than fully automated options. Terminal layout and cargo mix, such as the proportion of transshipment or rail cargo, influence the best horizontal transport and ASC alignment options.
The document provides an overview of automated guided vehicle systems (AGVS). It discusses the key components of an AGVS including the vehicle, guide path, control unit and computer interface. It describes different types of AGVS vehicles including driverless trains, pallet trucks and unit load carriers. The document also covers the history of AGVS, vehicle functions, guidance systems, safety features, control systems, communications and important considerations for AGVS design.
This presentation discusses about Basics of automated guided vehicles and different types of automated guided vehicles and different types of guidance systems
Presentation of a startup for potential investorsSvyazi agency
The document proposes a new autonomous transportation concept called BaseTracK. It is based on "virtual rails" or predetermined trajectories that vehicles can follow autonomously. BaseTracK aims to make autonomous transportation more accessible by starting with limited assistance systems rather than requiring full autonomy. It involves digitizing routes, installing automation kits in vehicles to guide them along routes, and providing access to routes via subscription. The concept has the potential to be applied to various transport modes on roads, waterways and in the air. It is presented as a simpler and more reliable alternative to existing autonomous driving systems that can operate in various weather conditions without huge computing power requirements.
The document discusses plans to modernize the Carriage Stores Depot in Lower Parel, Mumbai through automation. It will involve installing an Intelligent Warehousing Automated Storage and Retrieval System (IW-ASRS) to optimize storage capacity. The estimated cost is Rs. 138 million and will enhance inventory management through RFID and barcode technology. It aims to improve productivity and support increasing coach repair targets.
The document discusses plans to modernize the Carriage Stores Depot in Lower Parel, Mumbai through automation. It will involve installing an Intelligent Warehousing Automated Storage and Retrieval System (IW-ASRS) to optimize storage capacity. The estimated cost is Rs. 138 million and will enhance inventory management through RFID and barcode technology. It aims to improve productivity and support the increasing coach refurbishment targets of the workshop located in a space-constrained area.
Integrate with AGVs - Webinar Presentation by FlexQubeAnders Fogelberg
- What is an AGV?
- History of the AGV
- AGV tasks in material handling
- Why use AGVs?
- Different types of AGVs
- AGV guidance methods
- Designing carts for AGV
The document discusses automated parking systems as a solution to increasing parking demand. It provides an introduction to automated parking systems, including how they work and the benefits they provide like space savings, security, and convenience. It then discusses the history of automated parking, need for these systems, various types of automated parking systems like stack, puzzle, cart, and rotary systems. It covers the basic concepts, components, advantages and disadvantages of these automated parking solutions.
This document provides information on an automated parking system project presented by architecture students. It begins with an introduction to automated parking systems, noting their advantages of space and time savings. It then discusses the history and types of automated parking systems, including fully automated, semi-automated, stack, puzzle, cart, tower, chess, and rotary systems. The document outlines the key components, specifications, and advantages of these different system types. It also covers planning and design strategies as well as code requirements for automated parking systems.
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This document discusses mobile robot vehicles and provides several examples of different types of mobile robot platforms. It covers key concepts related to mobility including configuration space, task space, degrees of freedom, and actuation. Examples discussed include trains, hovercrafts, helicopters, fixed-wing aircraft, underwater robots, and cars. Each example describes the robot's configuration space, degrees of freedom, actuation, and task space. The document aims to explain the basic issues involved in programming robots to perform tasks by analyzing different types of mobile robot platforms and their mobility characteristics.
Improving safety and efficiency in the rail industry
Today’s rail industry is faced with mounting competitive and cost pressures that call for significant improvements in reliability, operating efficiencies and rail safety. Detailed risk management is becoming increasingly important— even mandatory due to current and upcoming regulations. Manufacturing, maintenance, repair and overhaul (MRO) processes have become more complex and global, with materials being sourced from all parts of the world.
More than 20 leading railway operators, manufacturers and solution providers have stepped up to develop new applications standard for rail
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Providing improved analytics and incident investigation
Identifying more easily series faults
Enabling more effective recall management
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AGVs are self-propelled vehicles that are guided along defined pathways and used for material handling in industries. The document discusses the different types of AGVs, components of an AGV system including vehicles, guide paths, and controls, as well as methods of guidance and communication. Applications of AGVs are also covered, such as use in driverless train operations and storage/distribution systems to efficiently move material and products.
- Rotary car parking systems utilize a mechanical system to minimize the space required for parking cars by rotating vehicles stored in cages or pallets either clockwise or counterclockwise. This allows parking of 6-40 vehicles in the space typically used for only 2 vehicles.
- The system described uses an Arduino Mega microcontroller, keypad, LCD display, motors, motor drivers, and other components to control the rotation and retrieval of vehicles from the parking structure. User inputs the space number using the keypad and the system parks or retrieves the vehicle accordingly.
- Rotary parking systems provide advantages over traditional parking by minimizing land usage, reducing parking damage, and eliminating time spent searching for vehicles or spaces. They also
This document compares traditional and automated multistoried parking systems. Traditional parking involves horizontal circulation and parking cars in angled spaces, while automated parking uses mechanical lifts to vertically store cars. An example automated system is Volkswagen's parking towers, which use robotic arms to rapidly store up to 16 cars per floor in just over a minute. Automated parking provides higher efficiency but at a higher cost, while traditional parking is less efficient but cheaper to implement. Both systems have advantages and disadvantages regarding factors like land use, costs, security, and convenience.
A comparison of approaches to the automation of container terminals. Perpendicular [Euro Design] based on HHLA implementation vs Horizontal ATS design using large robust RMGs and industrial automation techniques.
ATS larger more automated machines = fewer machines in the design and consequently lower CAPEX and OPEX.
All machines in the ATS design are built to Severe Duty standards [CMAA or ASIE] for 25 year minimum life at or near max load capacity in continuous operation.
Km247 semi operated vertical car parking prototype system controlling with sw...
Thesis Presentation - Rev JD 17July2016
1. Development of Scheduling, Path Planning and Resource
Management Algorithms for Robotic Fully-automated
Multi-story Parking Structure
Jayanta Kumar Debnath
20 July 2016
University of Toledo
Electrical Engineering and Computer Science Department
Master of Science in Electrical Engineering
(with concentration in Computer Science and Engineering)
Thesis Presentation
2. Introduction
Problem Statement
Proposed Methodology
Proposed Path Planning Algorithm
Proposed Elevator Scheduling Algorithm
Proposed Resource Management Algorithm
Real-time Concurrent Simulation Model
Simulation Results
Conclusions and Future Study
Contents
3. IntroductionWhy Automated Parking?
• Create space
• Increase revenue
• Better customer security
• Green parking
50% less real-
estate than
traditional
parking lot!
No drive way
roads like
traditional
parking space!
Lower
deployment cost
plus more
revenue
generating space!
.
More security
for people and
their vehicles!
Increase green
space and reduce
traffic congestion
and carbon
footprint!
Promising and effective
solution for busy
metropolitan areas
parking challenge!
4. IntroductionCurrent Automated Parking Technology
Stacker Type Automated Parking Tower Type Automated Parking
• Require dedicated lane for stacker crane.
• Low space utilization.
• Do not require sophisticated AI Algorithms.
Stacker Crane
• Require one elevator.
• Low space utilization.
• Do not require sophisticated AI
Algorithms.
• Parking capacity is not scalable.
5. IntroductionCurrent Automated Parking Technology
Chess Type Automated Parking Puzzle Type Automated Parking
• No dedicated driveway or lane required.
• Vehicles can be moved horizontally only.
• Multiple elevators.
• Maximum space utilization possible.
• Require sophisticated AI Algorithms.
• Parking capacity is highly scalable.
• Each cell require lifting mechanism.
• Vehicles can be moved both
vertically and horizontally.
• No elevator required.
• Space utilization less than chess type.
• Require sophisticated AI Algorithms.
• Parking capacity is highly scalable.
Puzzle parking
structure is similar to
chess parking except
there is no elevators!
7. Problem Statement
Motivation of this Thesis!
Robotic and fully automated parking structures are becoming increasingly
feasible from the technology perspective.
There is a lack of reported designs in literature for a computerized
management system for such structures.
Artificial Intelligence is well suited for and can enhance efficiency,
scalability and mass level commercialization of robotic fully-automated
parking structures.
Problem Statement: Design, develop and prototype in simulation an
integrated software implementation for a management system that can plan
multiple concurrent paths, schedule a group of elevators, and allocate
parking space and other related resources in real time with service times
acceptable to users.
8. Problem SpecificationRequirements
Ground Floor Layout (shown for 10×20 topology)
Vehicle Movement Directions
• No driving lanes on any floor of the multistory parking structure
• Number of parking spaces on a given floor and number of stories are variables.
• Minimum 80% utilization rate for parking on a given floor
• No more than 5 minutes waiting time for delivery or retrieval of a vehicle by drivers
• Multiple independent lifts (or elevators)
• Robotic carts or pallets move vehicles.
• Unlimited number of vehicles in motion throughout the structure at any given time
• Vehicle cart and elevator movements are modeled in compliance with physics.
V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
9. Proposed MethodologyStorage Process
Storage Management Algorithm
assigns a storage location for a
Storage Request
Elevator Scheduling
Algorithm assigns an
elevator and informs
customer.
Customer leaves their vehicle
in the elevator and leave.
Elevator transport vehicle to
desired floor and unload the
vehicle.
Path Planning Algorithm
finds a path to storage
location and moves the
vehicle accordingly.
Storage and Retrieval
Request Entry Kiosk!
Elevator
Needed?
Select Vehicle
Exchange Bay and
notify customer
Customer drops off
their vehicle in the
Vehicle Exchange
Bay and leaves.
Yes
No
V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
10. Proposed MethodologyRetrieval Process
Storage Management Algorithm
locates vehicle parked at a specific
location for a Retrieval Request.
Elevator Scheduling
Algorithm assigns an
elevator and notifies
customer.
Customer picks up the
vehicle and leaves.
Elevator transports vehicle to
ground floor.
Path Planning Algorithm
finds a path towards elevator
location and moves the
vehicle accordingly.
Storage and Retrieval
Request Entry Kiosk!
Elevator
Needed?
Select Vehicle
Exchange Bay and
notify customer
Path Planning
Algorithm finds a
path towards Vehicle
Exchange Bay
location and moves
the vehicle
accordingly.
Yes
No
V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
11. Proposed MethodologyTheoretical Bounds
V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
What is the
minimum
number of
elevators?
What is the
minimum
number of
blank cells?
Bounds on minimum number of elevators and blank cells are derived
applying Queueing Theory on Storage and Retrieval Processes.
12. V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Proposed Path Planning AlgorithmOverview
D* Lite Algorithm
Path Planning for storage and retrieval of vehicles on cart.
Uniform Cost Search
Locating blank cells
D* Lite Algorithm
Path Planning for blank cells
Immovable
Obstacle!
Movable
Obstacle!
Starting Cell
Destination
Cell
Dynamic
Environment
Unblock
Procedure
13. V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Proposed Path Planning AlgorithmOverview
D* Lite Algorithm
Path Planning for storage and retrieval of vehicles on cart.
Uniform Cost Search
Locating blank cells
D* Lite Algorithm
Path Planning for blank cells
Neighbor Cell on
Planned Path
Unblock
Procedure
14. V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Proposed Path Planning AlgorithmOverview
D* Lite Algorithm
Path Planning for storage and retrieval of vehicles on cart.
Uniform Cost Search
Locating blank cells
D* Lite Algorithm
Path Planning for blank cells
Nearest blank
cell located using
Uniform Cost
Search!
Unblock
Procedure
15. V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Proposed Path Planning AlgorithmOverview
D* Lite Algorithm
Path Planning for storage and retrieval of vehicles on cart.
Uniform Cost Search
Locating blank cells
D* Lite Algorithm
Path Planning for blank cells
Neighbor cell on
planned path of
blank cell
Selected
blank cell
Unblock
Procedure
16. V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Proposed Path Planning AlgorithmOverview
D* Lite Algorithm
Path Planning for storage and retrieval of vehicles on cart.
Uniform Cost Search
Locating blank cells
D* Lite Algorithm
Path Planning for blank cells
Selected
blank cell
moved!
Unblock
Procedure
17. V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Proposed Path Planning AlgorithmOverview
D* Lite Algorithm
Path Planning for storage and retrieval of vehicles on cart.
Uniform Cost Search
Locating blank cells
D* Lite Algorithm
Path Planning for blank cells
Vehicle
moved!
Check for change of
immovable obstacle
topology!
Unblock
Procedure
18. V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Proposed Path Planning AlgorithmOverview
D* Lite Algorithm
Path Planning for storage and retrieval of vehicles on cart.
Uniform Cost Search
Locating blank cells
D* Lite Algorithm
Path Planning for blank cells
No change in
immovable obstacle
topology.
Follow previously
planned path
Check for change of
immovable obstacle
topology.
Unblock
Procedure
19. V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Proposed Path Planning AlgorithmOverview
D* Lite Algorithm
Path Planning for storage and retrieval of vehicles on cart.
Uniform Cost Search
Locating blank cells
D* Lite Algorithm
Path Planning for blank cells
No change in
immovable obstacle
topology.
Follow previously
planned path
Check for change of
immovable obstacle
topology.
Unblock
Procedure
20. V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Proposed Path Planning AlgorithmOverview
D* Lite Algorithm
Path Planning for storage and retrieval of vehicles on cart.
Uniform Cost Search
Locating blank cells
D* Lite Algorithm
Path Planning for blank cells
Immovable obstacle
topology changed!!
Re-plan path
efficiently which is a
special feature of D*
Lite algorithm!!
Check for change in
immovable obstacle
topology.
Unblock
Procedure
21. V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Proposed Path Planning AlgorithmOverview
D* Lite Algorithm
Path Planning for storage and retrieval of vehicles on cart.
Uniform Cost Search
Locating blank cells
D* Lite Algorithm
Path Planning for blank cells
No change in
immovable obstacle
topology.
Follow previously
planned path
Check for change in
immovable obstacle
topology.
Unblock
Procedure
22. V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Proposed Path Planning AlgorithmOverview
D* Lite Algorithm
Path Planning for storage and retrieval of vehicles on cart.
Uniform Cost Search
Locating blank cells
D* Lite Algorithm
Path Planning for blank cells
No change in
immovable obstacle
topology.
Follow previously
planned path
Check for change in
immovable obstacle
topology.
Unblock
Procedure
23. V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Proposed Path Planning AlgorithmOverview
D* Lite Algorithm
Path Planning for storage and retrieval of vehicles on cart.
Uniform Cost Search
Locating blank cells
D* Lite Algorithm
Path Planning for blank cells
No change in
immovable obstacle
topology.
Follow previously
planned path
Check for change in
immovable obstacle
topology.
Unblock
Procedure
24. V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Proposed Path Planning AlgorithmOverview
D* Lite Algorithm
Path Planning for storage and retrieval of vehicles on cart.
Uniform Cost Search
Locating blank cells
D* Lite Algorithm
Path Planning for blank cells
No change in
immovable obstacle
topology.
Follow previously
planned path
Check for change in
immovable obstacle
topology.
Unblock
Procedure
25. V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Proposed Path Planning AlgorithmOverview
D* Lite Algorithm
Path Planning for storage and retrieval of vehicles on cart.
Uniform Cost Search
Locating blank cells
D* Lite Algorithm
Path Planning for blank cells
No change in
immovable obstacle
topology.
Follow previously
planned path
Check for change in
immovable obstacle
topology.
Unblock
Procedure
26. V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Proposed Path Planning AlgorithmOverview
D* Lite Algorithm
Path Planning for storage and retrieval of vehicles on cart.
Uniform Cost Search
Locating blank cells
D* Lite Algorithm
Path Planning for blank cells
No change in
immovable obstacle
topology.
Follow previously
planned path
Check for change in
immovable obstacle
topology.
Unblock
Procedure
27. V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Proposed Path Planning AlgorithmOverview
D* Lite Algorithm
Path Planning for storage and retrieval of vehicles on cart.
Uniform Cost Search
Locating blank cells
D* Lite Algorithm
Path Planning for blank cells
No change in
immovable obstacle
topology.
Follow previously
planned path
Check for change in
immovable obstacle
topology.
Unblock
Procedure
29. Proposed Path Planning AlgorithmD* Lite Algorithm
D* Lite Algorithm is a Heuristic Based and Incremental fast re-
planning search algorithm and very effective in a dynamic environment.
A* search Incremental D* Lite search
Incremental search is able to effectively re-use partial path plan from previous search!
7 6
8 7 6
5 4
6 5
8 7 6 5
4 3
8 7 6
5 4
11 10 9 4 3 2
11 10 9 8 3 2 1 2
11 7 2 1 G 1
12 6 5 4 3 2 1 2
12 13 10 9 8 7 6 5 4 3 2
S 8 7 6 5 4
10 9 8 7
11 10 9 8
11 6
12
13 12 11 10
14 13 12
7 6
8 7 6
5 4
6 5
8 7 6 5
4 3
8 7 6
5 4
11 10 9 4 3 2
11 10 9 8 3 2 1 2
11 7 2 1 G 1
12 6 5 4 3 2 1 2
12 13 10 9 8 7 6 5 4 3 2
S 8 7 6 5 4
10 9 8 7
11 10 9 8
11 6
12
13 12 11 10
14 13 12
Obstacle
added! Obstacle
added!
Grey cells are
explored in
re-planning.
30. Proposed Path Planning AlgorithmUnblock Procedure
o o
o o L o
o o
S o
o o o o
o o
o
o o
o o
o o
o o o o o o
o o o o o
o o o o
Use uniform cost
search to locate nearest
blank cell
31. Proposed Path Planning AlgorithmUnblock Procedure
Uniform cost search
1st Iteration
o o
o o L o
o o
S o
o o o o
o o
o
o o
o o
o o
o o o o o o
o o o o o
o o o o
No blank cell
Found!
32. Proposed Path Planning AlgorithmUnblock Procedure
Uniform cost search
2nd Iteration
No blank cell
Found!
o o
o o L o
o o
S o
o o o o
o o
o
o o
o o
o o
o o o o o o
o o o o o
o o o o
33. Proposed Path Planning AlgorithmUnblock Procedure
Uniform cost search
3rd Iteration
No blank cell
found!
o o
o o L o
o o
S o
o o o o
o o
o
o o
o o
o o
o o o o o o
o o o o o
o o o o
34. Proposed Path Planning AlgorithmUnblock Procedure
Uniform cost search
4th Iteration
Blank cell
found!
o o
o o L o
o o
S o
o o o o
o o
o
o o
o o
o o
o o o o o o
o o o o o
o o o o
35. Proposed Path Planning AlgorithmUnblock Procedure
o o
o o L o
o o
S o
o o o o
o o
o
o o
o o
o o
o o o o o o
o o o o o
o o o o
Two blank cells!
Two possible
destination cells!
Find nearest blank cell – destination cell pair.
36. Proposed Path Planning AlgorithmUnblock Procedure
o o
o o L o
o o
S o
o o o o
o o
o
o o
o o
o o
o o o o o o
o o o o o
o o o o
Nearest blank
cell – destination
cell pair
Find nearest blank cell – destination cell pair.
37. Proposed Path Planning AlgorithmUnblock Procedure
Use D* Lite path planning to move blank cell towards destination cell
o
o o L o
o o
S o
o o o o
o o
o
o o
o o
o o
o o o o o o
o o o o o
o o o o
38. Proposed Elevator Scheduling Algorithm
Two-level Integer Programming Formulation
Problem Formulation
Vehicle Set: S
Elevator 1 Elevator j Elevator NE…... …...
Vehicle Subset Assigned
to Elevator j: Sj
Trip 1 Trip k Trip |Sj|…... …...
For a given
vehicle-to-elevator
assignment
High Level: Vehicle-to-Elevator
Assignment
Low Level: Vehicle-to-Trip
Assignment
Each trip will
serve one
vehicle!
HNPGA (Hybrid Nested
Partition and Genetic
Algorithm) used for High
Level Assignment!
FIFO used for Low Level
Assignment!
39. X X X ……. X X X
The Entire Feasible Region:
Most Promising Region
1 1 2 ….NE 1 2 1 1 2 ….NE 1 1 1 1 2 ….NE 1 3
Partitioning
Iteration 0:
Iteration k:
.
.
.
.
},...,4,3,2,1{ ENX
)(:RegiongSurroundin k
)()(:RegiongSurroundin kbk
||S
……
……
Depth: 0
Depth: d
Depth: d+1
Basic partitioning scheme
shown, where at each
iteration the assignment of
next 1 unassigned vehicle is
fixed.
1 X X ……. X X X 2 X X ……. X X X 3 X X ……. X X X NE X X ……. X X X……
Iteration 1:
)σ(1
.
.
.
)(:)(region,-subbestofregiongsurroundinWhole kbkb
Depth: 1X X X X X X X X
X X
1 1 2 ….NE 1 2 5 X
X X X X 1 1 2 ….NE 1 NE X X
1 1 2 ….NE 1 2 1 X 1 1 2 ….NE 1 2 2 X 1 1 2 ….NE 1 2 NE X
1 1 2 ….NE 1 X X X
Iteration k-1:
Depth: d-1
)σ(k 1
σ(k)
1 1 2 ….NE 2 X 1 1 2 ….NE 3 X 1 1 2 ….NE NE X……X X X X X X
)(kb
Most Promising Region
at iteration k on depth d
Most Promising Region at iteration k-1 on depth
d-1. This would be most promising region at
iteration k+1 if backtracking occurs at iteration k.
Most Promising Region
at iteration 1 on depth 1
Selected best sub region of most
promising region at iteration k. This
would be most promising region at
iteration k+1 if globally verified.
Proposed Elevator Scheduling Algorithm
HNPGA : Select Next
Most Promising Region
Two steps at each iteration of HNPGA for selecting next Most Promising Region
Step 1: Select best sub region. Step 2: Global verification of selected
best sub region with Surrounding
Regions.
Both steps use Genetic Algorithm
Nested Partition Tree!
40. 1 5 3
1 5 3 2 4 5 1 5 3 1 3 4 1 5 3 5 2 1…...
4 5 1 3 5 6
6 1 5 3 2 5
2 4 1 1 6 2
1 2 4 5 3 4
2 5 1 3 6 2
4 5 2 1 6 2
1 5 3
1 5 3
1 5 3
1 5 3
1 5 3
1 5 3
1 3 4 3 5 61 5 3
4 5 6 …... 3 1 4
Proposed Elevator Scheduling AlgorithmStep 1: Select Best
Sub Region
Total 10 vehicles;
Each field represents
vehicles to schedule
Initial populations of GA
After
evaluation
cycles!
Fittest of final populations
Crossover and
Mutation
Best sub region
found by GA!
Selected Most Promising
Region at first iteration.
41. 1 5 3
1 5 3 2 4 51 5 3 1 3 4 1 5 3 5 2 1…...
1 3 4 3 5 6
1 3 4 3 2 5
2 4 1 1 6 2
1 2 4 5 3 4
2 5 1 3 6 2
4 5 2 1 6 2
1 5 3
1 5 3
1 5 3
1 5 3
1 5 3
1 5 3
2 1 3 1 4 4…...
6
5
2
4
Fixed values from
selected best sub
region
Uniform
Sampling
Values
other
than
(1,3,4)
Uniform Sampling
Values
other
than
(1,5,3)
Uniform Sampling
Proposed Elevator Scheduling AlgorithmStep 2: Global
Verification
Initial populations of GA uniformly taken from three region.
Selected
best sub
region!
Selected Most Promising
Region at first iteration.
42. Proposed Elevator Scheduling AlgorithmObjective Function
of GA
𝐽 =
𝑗=1
𝑁 𝐸
𝐽𝑗 =
𝑗=1
𝑁 𝐸
𝑡𝑗
𝑏
+
𝑖=1
|𝑆𝑗
𝑅
|
𝑡𝑖
𝑟𝑒
+ 𝑡𝑖
𝑙
+ 𝑡𝑖
𝑒
+ 𝑡𝑖
𝑢
+
𝑖=1
𝑆 𝑗
𝑆
𝑡𝑖
𝑙
+ 𝑡𝑖
𝑒
+ 𝑡𝑖
𝑢
+ 𝑡𝑖
𝑡𝑠
4 5 2 1 6 21 5 3
Each field or gene of
chromosome
represents vehicles to
schedule.
The value of each
field represents the
assigned elevator.
Total time required to complete storage
or retrieval process associated with
assigned vehicles.
44. 2 3 5 21 4
5 6 1 42 3
Chromosome
After Mutation
Randomly generated Mutation
Point=4 and Randomly
Generated Elevator No =6
2 3 5 21 6
2 3 5 21 4
Randomly
Generated Mutation
Points = 2 & 6
2 2 5 31 4
After Mutation
First Mutation
Operator
Second Mutation
Operator
Proposed Elevator Scheduling AlgorithmMutation Operator
45. Theoretical Bounds on Resource NeedsQueueing Theory
Server - 01
Server - 02
Server - S
Mean Arrival
Rate, λ
Mean Service
Rate, μ
𝛌 < 𝛍 × 𝑺Steady State
Condition!
M/M/S Queue Model
Stochastic
Arrival Process
Stochastic
Service Process
Multiple Parallel
Servers
Without steady
state queue will
grow infinitely
large eventually.
46. Proposed Resource Management AlgorithmStatistical Models
Rush hour period
customer arrival
modeling
Morning Rush Hour
• 2 clock hour period from 6:30 AM to 8:30 AM
• 95% of requests are storage
• 5% of requests are retrieval
Evening Rush Hour
• 2 clock hour period from 4:00 PM to 6:00 PM
• 95% of requests are retrieval
• 5% of requests are storage
Inspired by busy
downtown
business districts
traffic pattern
47. Proposed Resource Management AlgorithmStatistical Models
0
20
40
60
80
100
120
0-5
5-10
10-15
15-20
20-25
25-30
30-35
35-40
40-45
45-50
50-55
55-60
60-65
65-70
70-75
75-80
80-85
85-90
90-95
95-100
100-105
105-110
110-115
115-120
NV,M,T
5-Minute Time Periods during rush hours
Distribution of Mean Arrival Rate during Rush Hours
(NV,M,max = 100)
Poisson Distributed Customer Arrivals with varying mean arrival rate!
48. Theoretical Bounds on Resource Needs
Bound on
Minimum Number
of Blank Cells
V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Modeled part of retrieval process at the beginning of evening rush hour
as M/M/S queue where blank cells act as multiple servers to transport
vehicles toward elevator load/unload bay!
49. Theoretical Bounds on Resource Needs
Bound on
Minimum Number
of Blank Cells
V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
𝑁 𝐵,𝐹 =
𝑁 𝑉,𝑀,𝑚𝑎𝑥
12 × 𝑁 𝐹 × 3600
× (𝑑 𝐵+5𝑑 𝐸) × 𝑡 𝑚𝑜𝑣𝑒 + 1 + 𝑚𝑎𝑥
1≤𝑇≤12
𝑁 𝐵,𝐹,𝑇,𝑆
Applying 𝛌 < 𝛍 × 𝑵 𝑩,𝑭
50. Theoretical Bounds on Resource NeedsBound on
Minimum Number
of Elevators
V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
V V V V V V V V V V V V
L L L L L L L L
E E EEE E EE
– Vehicle Exchange Bay ; – Elevator Cell ; – Elevator Load/Unload Bay ; – Storage CellV E L
Modeled part of storage/retrieval process as M/M/S queue where
elevators act as multiple servers to transport vehicles between floors!
51. Theoretical Bounds on Resource NeedsBound on
Minimum Number
of Elevators
Speeding up
Starting Destination floor
Distance
,
2
2
.
Slowing
down
,
2
2
.
Traveling at
Constant
Speed
Starting floor Destination floor
Distance
𝑉𝐸
Slow down
Speed up
If, 𝐷 𝐻𝐻 ≤ 𝑉𝐸,𝑚𝑎𝑥
2
𝑎 𝐸 :If, 𝐷 𝐻𝐻 > 𝑉𝐸,𝑚𝑎𝑥
2
𝑎 𝐸 :
Elevator Dynamics
53. Real-time Concurrent Simulation ModelUnified Modeling
Language
The overall functionality of simulation is modeled through five major
activity modules, which are (a) Automated Parking Lot, (b)
Automated Storage Controller, (c) Automated Retrieval
Controller, (d) Elevator Controller, and (e) Elevator Scheduler
Modular
Simulation
Architecture
54. Real-time Concurrent Simulation ModelUnified Modeling
Language
State machine diagram for Automated Retrieval Controller: moving
towards elevators
55. Real-time Concurrent Simulation ModelUnified Modeling
Language
State machine diagram for Automated Storage Controller: moving
from elevators
56. Real-time Concurrent Simulation ModelUnified Modeling
Language
State machine diagram for Elevator Controller : moving between
floors
57. Real-time Concurrent Simulation ModelUnified Modeling
Language
Busy-wait
Synchronization
Techniques used
to communicate
among concurrent
threads.
Timing diagram
58. Simulation StudyExperimental Setup
𝑵 𝑪 𝑵 𝑹 𝑵 𝑽,𝑴,𝒎𝒂𝒙 𝑵 𝑭
10 10 100 2
20 20 200 3
30 30 300 4
40 40 400 5
500 6
600 7
700 8
800 9
10
Space Utilization > 80%
The capacity of parking
lot needs to be fully
utilized within two
clock-hour period
43 Test Cases Found!
Generating
Test Cases
Number
of
columns
on each
floor
layout
Number
of rows
on each
floor
layout
Number
of floors
Maximum value of
mean arrival rate for
vehicle requests for
the entire parking
structure per hour
among all rush hour
time slots
59. Simulation StudySimulation Software
A software application with multithreading was
developed through the Unified Modeling Language
(UML) using Java and MATLAB programming
languages.
Simulation Software was run in Linux
environment for better multithreading
capability!
61. Simulation StudySimulation Results
0
0.1
0.2
0.3
0.4
0.5
0.6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
Time(Minutes)
Test Case Number
Average Customer Waiting Time for Storage (WTS) Without Immovable Carts
With 10% Immovable Carts
0
1
2
3
4
5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
Time(Minutes)
Test Case Number
Average Customer Waiting Time for Retrieval(WTR)
Without Immovable Carts
With 10% Immovable Carts
Average customer waiting time within an impressive 5 minutes mark!
62. Simulation StudySimulation Results
In most cases, average customer waiting time within an impressive 2 minutes mark!
0
0.2
0.4
0.6
0.8
1
1.2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
ProbabilityDistribution
Waiting Time (Minutes)
Average Customer Waiting Time for Storage (WTS) Distribution for 43
Test Cases
Without Immovable Carts
With 10% Immovable Carts
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
ProbabilityDistribution
Waiting Time (Minutes)
Average Customer Waiting Time for Retrieval (WTR) Distribution among
43 Test Cases
Without Immovable Carts
With 10% Immovable Carts
63. Simulation StudySimulation Results
0
2
4
6
8
10
12
14
16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
Time(Minutes)
Test Case Number
Maximum Customer Waiting Time for Storage (WTS) Without Immovable Carts
With 10% Immovable Carts
0
5
10
15
20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
Time(Minutes)
Test Case Number
Maximum Customer Waiting Time for Retrieval (WTR) Without Immovable Carts
With 10% Immovable Carts
For most cases, the maximum (worst case) customer waiting time is less than 5 minutes
although for a small number of cases it was between 10 to 17 minutes!
64. Simulation StudySimulation Results
Extreme maximum values occur for very few test cases with
10% immovable carts. In general, maximum waiting times
are within the 6-minute mark.
0
0.1
0.2
0.3
0.4
0.5
0.6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
ProbabilityDensity
Waiting Time (Minutes)
Maximum Customer Waiting Time for Storage (WTS) Distribution among
43 Test Cases
Without Immovable Carts
With 10% Immovable Carts
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
ProbabilityDensity
Waiting Time (Minutes)
MaximumCustomer Waiting Time for Retrieval (WTR) Distribution
among 43 Test Cases
Without Immovable Carts
With 10% Immovable Carts
Low Probability!
65. Simulation Study
Simulation Results for Case #42 with 10%
Immovable Carts
0
0.01
0.02
0.03
0.04
0.05
0.06
1
23
45
67
89
111
133
155
177
199
221
243
265
287
309
331
353
375
397
419
441
463
485
507
529
551
573
595
617
639
661
683
705
727
749
771
793
815
837
859
881
903
925
947
969
991
1013
1035
ProbabilityDensity
Waiting Time (Seconds)
Customer Waiting Time for Retrieval (WTR) Distribution : Case No - 42
0
0.1
0.2
0.3
0.4
0.5
1
18
35
52
69
86
103
120
137
154
171
188
205
222
239
256
273
290
307
324
341
358
375
392
409
426
443
460
477
494
511
528
545
562
579
596
613
630
647
664
681
698
715
732
749
766
783
800
ProbabilityDensity
Waiting Time (Seconds)
Customer Waiting Time for Storage (WTS) Distribution : Case No - 42
Most of the customers experience average
waiting times; very few customers have to
wait more than the average value!
Frequency Distribution of
Waiting Times for
Individual customers for
Case #42
Considering 10% immovable carts
Considering 10% immovable carts
66. ConclusionsConclusions
In light of and within the context of the simulation
study presented, the design appears feasible for real
time deployment in an industrial-grade environment.
• Average Customer Waiting Time is not more than 5
minutes in most cases!
• Space Utilization for parking is more than 80% !
• Design supports customer arrival rates of up to 800
customers per hour!
67. Future StudyRecommendations
• In the current system, all the parking spots are the same size. Given that there
are different size vehicles (sedans, SUVs, mini vans, trucks, etc.) to park, the
size of a parking spot would have to match the largest car size. To maximize the
available real-estate space utilization rate and enhance the capacity, future
studies may consider the design other topologies which may have different size
parking spaces.
• Study of the effect of robotic cart failures could be extended further to
determine the adverse impact on performance more closely.
• We assumed, for the analysis based on the queueing theory, that customers
would not engage in balking or reneging in the waiting lines. In future studies
these and other similar complications can be injected into the statistical models
to determine their effects on performance.
The research could be extended in the future from the following aspects:
68. Publications
Debnath, Jayanta K., and Gursel Serpen. "Real-Time
Optimal Scheduling of a Group of Elevators in a Multi-
Story Robotic Fully-Automated Parking Structure."
Procedia Computer Science 61 (2015): 507-514.
J. Debnath and G. Serpen, Design of Multithreaded
Simulation Software through UML for a Fully
Automated Robotic Parking Structure, to appear in
proceedings of International Conference on Simulation
Modeling Practice and Theory, Las Vegas, Nevada, July
2016.