A lecture on location models for public sector operations research. Topics include facility location, coverage models, the p-median model, the p-center model, integer programming.
Designing emergency medical service systems to enhance community resilience Laura Albert
Emergency response to patients with medical needs after a disaster is a critical aspect of public safety and community resilience. An effective response to emergency medical patients can be achieved by designing a system that
- Allocates limited resources such as ambulances in resource-constrained settings,
- Leverages data and triage information to inform the design of response districts, and
- Sheds light on how these decisions change after a disaster.
In this talk, Dr. Laura Albert will discuss how analytical methods can be used to design emergency response systems and provide guidance into how to design data-driven emergency response systems. She will discuss how system design decisions must change after weather disasters when the system is congested and critical infrastructure is impaired.
Delivering emergency medical services: research, application, and outreachLaura Albert
Laura McLay's slides from the German Operations Research Society Conference for the presentation entitled "Delivering emergency medical services: research application, and outreach"
Comparison of Emergency Medical Services Delivery Performance using Maximal C...IJECEIAES
Ambulance location is one of the critical factors that determine the efficiency of emergency medical services delivery. Maximal Covering Location Problem is one of the widely used ambulance location models. However, its coverage function is considered unrealistic because of its ability to abruptly change from fully covered to uncovered. On the contrary, Gradual Cover Location Problem coverage is considered more realistic compared to Maximal Cover Location Problem because the coverage decreases over distance. This paper examines the delivery of Emergency Medical Services under the models of Maximal Covering Location Problem and Gradual Cover Location Problem. The results show that the latter model is superior, especially when the Maximal Covering Location Problem has been deemed fully covered.
Study on M/M/2 Transient Queue with Feedback under Catastrophic Effectijceronline
In this paper we study the time dependent solution of feedback customer with two servers along with catastrophic effect. We derive the asymptotic behavior of the queue length and the stationary probability distributions. The numerical examples are also given to test the effectiveness of the queue length under the catastropic conditions
Designing emergency medical service systems to enhance community resilience Laura Albert
Emergency response to patients with medical needs after a disaster is a critical aspect of public safety and community resilience. An effective response to emergency medical patients can be achieved by designing a system that
- Allocates limited resources such as ambulances in resource-constrained settings,
- Leverages data and triage information to inform the design of response districts, and
- Sheds light on how these decisions change after a disaster.
In this talk, Dr. Laura Albert will discuss how analytical methods can be used to design emergency response systems and provide guidance into how to design data-driven emergency response systems. She will discuss how system design decisions must change after weather disasters when the system is congested and critical infrastructure is impaired.
Delivering emergency medical services: research, application, and outreachLaura Albert
Laura McLay's slides from the German Operations Research Society Conference for the presentation entitled "Delivering emergency medical services: research application, and outreach"
Comparison of Emergency Medical Services Delivery Performance using Maximal C...IJECEIAES
Ambulance location is one of the critical factors that determine the efficiency of emergency medical services delivery. Maximal Covering Location Problem is one of the widely used ambulance location models. However, its coverage function is considered unrealistic because of its ability to abruptly change from fully covered to uncovered. On the contrary, Gradual Cover Location Problem coverage is considered more realistic compared to Maximal Cover Location Problem because the coverage decreases over distance. This paper examines the delivery of Emergency Medical Services under the models of Maximal Covering Location Problem and Gradual Cover Location Problem. The results show that the latter model is superior, especially when the Maximal Covering Location Problem has been deemed fully covered.
Study on M/M/2 Transient Queue with Feedback under Catastrophic Effectijceronline
In this paper we study the time dependent solution of feedback customer with two servers along with catastrophic effect. We derive the asymptotic behavior of the queue length and the stationary probability distributions. The numerical examples are also given to test the effectiveness of the queue length under the catastropic conditions
https://utilitasmathematica.com/index
Our Journal has we strive to minimize barriers to access and participation, ensuring that opportunities within the statistical community are available to all, regardless of background. This includes addressing issues such as language barriers, geographical disparities, and financial constraints that may limit access to statistical education and resources.
Omni-Channel Distribution: A Transshipment Modeling Perspectiveijmvsc
In our work, we develop a specialized optimization technique that adapts the general linear programming Transshipment model to the ever-growing needs of Omni-Channel distribution in Supply Chain Management. With the rapid adoption of “smart” mobile technologies, customers now acquire merchandise across multiple channels and devices. As a result, retailers are challenged with downstream operational complexities.
Fulfillment of customer orders now changes the placement and amount of independent demand inventory organizations may hold. Our research integrates the use of Hub or Fulfillment Centers, locations where sellers fill customer orders placed through e-commerce, as an additional segment of demand. This adaptation to the optimization of Transshipment can result in significant benefits to the firm’s logistics presence, customer retention, and profit.
An Enhanced Agglomerative Clustering Algorithm for Solving Vehicle Routing Pr...ijtsrd
An aggrandized solution is designed for the vehicles to reduce the total cost of distribution by which it can supply the goods to the customers with its known capacity can be named as a vehicle routing problem. In variable neighborhood search method, mainly an efficient vehicle routing can be achieved by calculating the distance matrix value based on the customers location or the path where the customers resides. The main objective of the paper is to reduce the total distance travelled to deliver the goods to the customers. The proposed algorithm is a hierarchy based enhanced agglomerative clustering algorithm technique which is used in the data mining scenario effectively. The proposed algorithm decreases the total distance assigning to each route and the important thing need to consider is that, this enhanced clustering algorithm can reduce the total distance when compared to the previously proposed variable neighborhood search method. V. Praveen | V. Hemalatha | M. Poovizhi"An Enhanced Agglomerative Clustering Algorithm for Solving Vehicle Routing Problem" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd4701.pdf http://www.ijtsrd.com/computer-science/other/4701/an-enhanced-agglomerative-clustering-algorithm-for-solving-vehicle-routing-problem/v-praveen
Empirical analysis of crowd-sourced freight deliveries
Presenter: Amanda Stathopoulos, Assistant Professor of Civil and Environmental Engineering, Northwestern University
This seminar presents results from empirical analysis of crowd-sourced freight deliveries in the US. Crowd-sourced deliveries build on the idea that citizens deliver goods, ideally along planned travel routes. Crowdshipping has a potential to match highly fragmented transport capacities with vastly diverse demand for urban freight deliveries, temporally, spatially and in real-time. This is typically achieved through platforms that connect carriers with consumers in need of deliveries. A third-party broker, who operates the platform, provides match-making, analysis and customer services between demand and supply. The main advantage of crowdshipping is the reduced need for fixed facilities, such as cars or warehouses, to run operations. The main obstacles are trust, liability issues, and ensuring a critical mass of couriers and customers. Despite the growth in operations, there is still a poor understanding of the performance, functionality and acceptability of these new delivery methods. The seminar presents results analyzing the performance in the early stages of operation of crowdshipping. Based on real operational data from 2 years across the US the performance is examined with an emphasis on the specificity of crowdshipping, namely related to delivery variability and the temporal matching dynamics. Based on additional survey experiments the behavior of the main agents in the system is modeled with an emphasis on revealing acceptance and priorities of both occasional drivers and senders. The research derives from a Partnership-for-Innovation (PFI) project funded by the NSF where a Chicago based research team (NU, UIC) is evaluating the capabilities of CROwd-sourced Urban Delivery (CROUD) in collaboration with a crowd-shipper technology firm.
About Amanda: Amanda’s research focuses on developing new methodologies to collect data and specify mathematical models to account for broad and realistic choice behaviour in the transport setting (for instance social determinants, environmental concern, user experience, simplified decision rules). These richer layers of user motivations is an area of primary relevance in improving understanding and prediction of travel behavior. For a range of current transportation challenges such as promoting transit ridership growth, moving towards alternative fuels, or getting companies to adopt better practices in delivering goods, there is increasing recognition of the need to build adequate tools to account for decision complexity on the user side to match with effective decision support.
Presentation given during the 2016 conference Analysis and Control on Networks: trends and perspectives in Padua, Italy. Presentation provides an engineerings perspective on the various issues with see with the modelling and management of crowds, and some of the new modelling approaches.
Transportation is defined as the movement of passengers and freight from one place to another. Passenger is an important part of the overall development problem of the nation and it affects mostly all the aspects of mobility. The Transportation problem is one of the sub classes of LPP in which the objective is to transport various amount of a single homogeneous commodity, that are initially stored at various origins, to different destinations in such a way that the total transportation cost is minimum. Although the name of the problem is derived from transport to which it was first applied, the problem can also be used for machine allocation, plant location, product mix problem, and many others, so that the problem is not confined to transportation or distribution only. Data Envelopment Analysis (DEA) is a very powerful service management and benchmarking technique originally developed by Charnes et al (1) to evaluate nonprofit and public sector organizations. Linear programming problem (LPP) is the underlying methodology that makes DEA particularly powerful compared with alternative productivity management tools. A Transportation Problem can be solved very easily by different methods (NWC RULE, LCM & VAM) by recognizing and formulating into LPP. The IBFS obtained in Transportation Algorithm can be tested also by MODIFIED DISTRIBUTION METHOD. After studying this paper, we will be able to achieve the following objectives of Transportation System - a major problem of the metropolitan cities. 1- Recognize and formulate the transportation problem as a linear programming problem. 2- Build a transportation table and describe its components. 3- Find an initial basic feasible solution of the transportation problem by using various methods. 4- Know in detail all the steps involved in solving a transportation problem by MODI problem. 5- Solve the unbalanced transportation problems by MODI method. 6- Identify the special situation in transportation problems; such as degeneracy and alternative optimal solution. 7- Resolve the special cases in transportation problems, where the objective may be of maximization or some transportation route may be prohibited.
2use of dss in ems to reduce responce timeosaleem0123
2use of dss in ems to reduce responce time
Reducing Ambulance Response Times Using Geospatial–Time Analysis of Ambulance Deployment
This study aimed to determine if a deployment strategy based on geospatial–time analysis is able to reduce ambulance response times for out‐of‐hospital cardiac arrests (OOHCA) in an urban emergency medical services (EMS) system.
Submit complete solutions to the following problems to your instru.docxmattinsonjanel
Submit complete solutions to the following problems to your instructor:
1. Cross Median Method: Text Chapter 10 Exercise 6
2. Huff Method: Text Chapter 10 Exercise 9
3. Set Covering Method: Text Chapter 10 Exercise 12
Chapter 10 Exercise 6
10.6. You have been asked to help locate a catering service in the central business district of a city. The locations of potential customers on an xy coordinate grid areP1 = (4, 4), P2 = (12, 4), P3 = (2, 7), P4 = (11, 11), and P5 = (7, 14). The expected demand is weighted as w1 = 4, w2 = 3, w3 = 2, w4 = 4, and w5 = 1. Using the cross-median approach, recommend a location for the catering service that will minimize the total weighted distance traveled to serve the customers.
Chapter 10 Exercise 9
10.9. A community is currently being served by a single self-serve gas station with six pumps. A competitor is opening a new facility with 12 pumps across town. Table 10.12 shows the travel times in minutes from the four different areas in the community to the sites and the number of customers in each area.
a. Using the Huff retail location model and assuming that λ = 2, calculate the probability of a customer traveling from each area to each site.
b. Estimate the proportion of the existing market lost to the new competitor.
Table 10.2
Area1234
Old Station51915
New Competitor208126
Number of Customers1001508050
Chapter 10 Exercise 12
The Volunteer Fire Department serving the communities in Figure 10.8 has just purchased two used fire engines auctioned off by a nearby city.
a. Select all possible pairs of communities in which the fire engines could be located to ensure that all communities can be reached within 30 minutes or less.
b. What additional consideration could be used to make the final site selection from the community pairs found in part a?
Figure 10.8 Service Area Network
SEE ADDITIONAL ATTACHMENT FOR FIGURE 10.8 SERVICE AREA NETWORK
The physical location can be an important decision for many types of services such as
stores, restaurants and gas stations; a critical differentiator for defining success and failure.
Such services require the physical presence and participation of customers in the service
delivery process. The customers have to travel to these services to participate in the delivery
of the service. Locations that are closer to the customers or are in places that are frequented
by the customers tend to attract more customers.
Services that involve no or minimal face-to-face contact can be located just about anywhere.
Even services with high front office contact can have significant back office operations,
which can be located at distant places. Sometimes, employees working for the back office
can even work from home as they do not require significant face-to-face contact.
Location for most businesses is a strategic decision and involves a long term commitment.
Therefore, the location should be good and viable from a long term perspective and s ...
Towards Explainable Recommendations of Resource Allocation Mechanisms in On-D...daoudalaa
Multi-agent systems can be considered a natural paradigm when modeling various transportation systems, whose management involves solving hard, dynamic, and distributed allocation problems. Such problems have been studied for decades, and various solutions have been proposed. However, even the most straightforward resource allocation mechanisms lead to debates on efficiency vs. fairness, business quality vs. passenger’s user experience, or performance vs.robustness. We aim to design an analytical tool that functions as a recommendation system for on-demand transport (ODT) authorities. This tool recommends specific allocation mechanisms that match the authority’s objectives and preferences to solve allocation problems for particular contextual scenarios. The paper emphasizes the need for transparency and explainability of resource allocation decisions in ODT systems to be understandable by humans and move toward more controllable resource allocation. We propose in this preliminary work a multi-agent architecture and general implementation guidelines towards meeting these requirements
For more details please refer to the paper with the same title
Optimization with impact: my journey in public sector operations research Laura Albert
eynote talk at the Advances in Data Science & Operations Research Virtual Conference, presented by Universidad Galileo in collaboration with INFORMSttt. It's the first INFORMS conference made for Latino America that brings together the scientific community from the areas of operations research, business intelligence, and data science.
https://utilitasmathematica.com/index
Our Journal has we strive to minimize barriers to access and participation, ensuring that opportunities within the statistical community are available to all, regardless of background. This includes addressing issues such as language barriers, geographical disparities, and financial constraints that may limit access to statistical education and resources.
Omni-Channel Distribution: A Transshipment Modeling Perspectiveijmvsc
In our work, we develop a specialized optimization technique that adapts the general linear programming Transshipment model to the ever-growing needs of Omni-Channel distribution in Supply Chain Management. With the rapid adoption of “smart” mobile technologies, customers now acquire merchandise across multiple channels and devices. As a result, retailers are challenged with downstream operational complexities.
Fulfillment of customer orders now changes the placement and amount of independent demand inventory organizations may hold. Our research integrates the use of Hub or Fulfillment Centers, locations where sellers fill customer orders placed through e-commerce, as an additional segment of demand. This adaptation to the optimization of Transshipment can result in significant benefits to the firm’s logistics presence, customer retention, and profit.
An Enhanced Agglomerative Clustering Algorithm for Solving Vehicle Routing Pr...ijtsrd
An aggrandized solution is designed for the vehicles to reduce the total cost of distribution by which it can supply the goods to the customers with its known capacity can be named as a vehicle routing problem. In variable neighborhood search method, mainly an efficient vehicle routing can be achieved by calculating the distance matrix value based on the customers location or the path where the customers resides. The main objective of the paper is to reduce the total distance travelled to deliver the goods to the customers. The proposed algorithm is a hierarchy based enhanced agglomerative clustering algorithm technique which is used in the data mining scenario effectively. The proposed algorithm decreases the total distance assigning to each route and the important thing need to consider is that, this enhanced clustering algorithm can reduce the total distance when compared to the previously proposed variable neighborhood search method. V. Praveen | V. Hemalatha | M. Poovizhi"An Enhanced Agglomerative Clustering Algorithm for Solving Vehicle Routing Problem" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd4701.pdf http://www.ijtsrd.com/computer-science/other/4701/an-enhanced-agglomerative-clustering-algorithm-for-solving-vehicle-routing-problem/v-praveen
Empirical analysis of crowd-sourced freight deliveries
Presenter: Amanda Stathopoulos, Assistant Professor of Civil and Environmental Engineering, Northwestern University
This seminar presents results from empirical analysis of crowd-sourced freight deliveries in the US. Crowd-sourced deliveries build on the idea that citizens deliver goods, ideally along planned travel routes. Crowdshipping has a potential to match highly fragmented transport capacities with vastly diverse demand for urban freight deliveries, temporally, spatially and in real-time. This is typically achieved through platforms that connect carriers with consumers in need of deliveries. A third-party broker, who operates the platform, provides match-making, analysis and customer services between demand and supply. The main advantage of crowdshipping is the reduced need for fixed facilities, such as cars or warehouses, to run operations. The main obstacles are trust, liability issues, and ensuring a critical mass of couriers and customers. Despite the growth in operations, there is still a poor understanding of the performance, functionality and acceptability of these new delivery methods. The seminar presents results analyzing the performance in the early stages of operation of crowdshipping. Based on real operational data from 2 years across the US the performance is examined with an emphasis on the specificity of crowdshipping, namely related to delivery variability and the temporal matching dynamics. Based on additional survey experiments the behavior of the main agents in the system is modeled with an emphasis on revealing acceptance and priorities of both occasional drivers and senders. The research derives from a Partnership-for-Innovation (PFI) project funded by the NSF where a Chicago based research team (NU, UIC) is evaluating the capabilities of CROwd-sourced Urban Delivery (CROUD) in collaboration with a crowd-shipper technology firm.
About Amanda: Amanda’s research focuses on developing new methodologies to collect data and specify mathematical models to account for broad and realistic choice behaviour in the transport setting (for instance social determinants, environmental concern, user experience, simplified decision rules). These richer layers of user motivations is an area of primary relevance in improving understanding and prediction of travel behavior. For a range of current transportation challenges such as promoting transit ridership growth, moving towards alternative fuels, or getting companies to adopt better practices in delivering goods, there is increasing recognition of the need to build adequate tools to account for decision complexity on the user side to match with effective decision support.
Presentation given during the 2016 conference Analysis and Control on Networks: trends and perspectives in Padua, Italy. Presentation provides an engineerings perspective on the various issues with see with the modelling and management of crowds, and some of the new modelling approaches.
Transportation is defined as the movement of passengers and freight from one place to another. Passenger is an important part of the overall development problem of the nation and it affects mostly all the aspects of mobility. The Transportation problem is one of the sub classes of LPP in which the objective is to transport various amount of a single homogeneous commodity, that are initially stored at various origins, to different destinations in such a way that the total transportation cost is minimum. Although the name of the problem is derived from transport to which it was first applied, the problem can also be used for machine allocation, plant location, product mix problem, and many others, so that the problem is not confined to transportation or distribution only. Data Envelopment Analysis (DEA) is a very powerful service management and benchmarking technique originally developed by Charnes et al (1) to evaluate nonprofit and public sector organizations. Linear programming problem (LPP) is the underlying methodology that makes DEA particularly powerful compared with alternative productivity management tools. A Transportation Problem can be solved very easily by different methods (NWC RULE, LCM & VAM) by recognizing and formulating into LPP. The IBFS obtained in Transportation Algorithm can be tested also by MODIFIED DISTRIBUTION METHOD. After studying this paper, we will be able to achieve the following objectives of Transportation System - a major problem of the metropolitan cities. 1- Recognize and formulate the transportation problem as a linear programming problem. 2- Build a transportation table and describe its components. 3- Find an initial basic feasible solution of the transportation problem by using various methods. 4- Know in detail all the steps involved in solving a transportation problem by MODI problem. 5- Solve the unbalanced transportation problems by MODI method. 6- Identify the special situation in transportation problems; such as degeneracy and alternative optimal solution. 7- Resolve the special cases in transportation problems, where the objective may be of maximization or some transportation route may be prohibited.
2use of dss in ems to reduce responce timeosaleem0123
2use of dss in ems to reduce responce time
Reducing Ambulance Response Times Using Geospatial–Time Analysis of Ambulance Deployment
This study aimed to determine if a deployment strategy based on geospatial–time analysis is able to reduce ambulance response times for out‐of‐hospital cardiac arrests (OOHCA) in an urban emergency medical services (EMS) system.
Submit complete solutions to the following problems to your instru.docxmattinsonjanel
Submit complete solutions to the following problems to your instructor:
1. Cross Median Method: Text Chapter 10 Exercise 6
2. Huff Method: Text Chapter 10 Exercise 9
3. Set Covering Method: Text Chapter 10 Exercise 12
Chapter 10 Exercise 6
10.6. You have been asked to help locate a catering service in the central business district of a city. The locations of potential customers on an xy coordinate grid areP1 = (4, 4), P2 = (12, 4), P3 = (2, 7), P4 = (11, 11), and P5 = (7, 14). The expected demand is weighted as w1 = 4, w2 = 3, w3 = 2, w4 = 4, and w5 = 1. Using the cross-median approach, recommend a location for the catering service that will minimize the total weighted distance traveled to serve the customers.
Chapter 10 Exercise 9
10.9. A community is currently being served by a single self-serve gas station with six pumps. A competitor is opening a new facility with 12 pumps across town. Table 10.12 shows the travel times in minutes from the four different areas in the community to the sites and the number of customers in each area.
a. Using the Huff retail location model and assuming that λ = 2, calculate the probability of a customer traveling from each area to each site.
b. Estimate the proportion of the existing market lost to the new competitor.
Table 10.2
Area1234
Old Station51915
New Competitor208126
Number of Customers1001508050
Chapter 10 Exercise 12
The Volunteer Fire Department serving the communities in Figure 10.8 has just purchased two used fire engines auctioned off by a nearby city.
a. Select all possible pairs of communities in which the fire engines could be located to ensure that all communities can be reached within 30 minutes or less.
b. What additional consideration could be used to make the final site selection from the community pairs found in part a?
Figure 10.8 Service Area Network
SEE ADDITIONAL ATTACHMENT FOR FIGURE 10.8 SERVICE AREA NETWORK
The physical location can be an important decision for many types of services such as
stores, restaurants and gas stations; a critical differentiator for defining success and failure.
Such services require the physical presence and participation of customers in the service
delivery process. The customers have to travel to these services to participate in the delivery
of the service. Locations that are closer to the customers or are in places that are frequented
by the customers tend to attract more customers.
Services that involve no or minimal face-to-face contact can be located just about anywhere.
Even services with high front office contact can have significant back office operations,
which can be located at distant places. Sometimes, employees working for the back office
can even work from home as they do not require significant face-to-face contact.
Location for most businesses is a strategic decision and involves a long term commitment.
Therefore, the location should be good and viable from a long term perspective and s ...
Towards Explainable Recommendations of Resource Allocation Mechanisms in On-D...daoudalaa
Multi-agent systems can be considered a natural paradigm when modeling various transportation systems, whose management involves solving hard, dynamic, and distributed allocation problems. Such problems have been studied for decades, and various solutions have been proposed. However, even the most straightforward resource allocation mechanisms lead to debates on efficiency vs. fairness, business quality vs. passenger’s user experience, or performance vs.robustness. We aim to design an analytical tool that functions as a recommendation system for on-demand transport (ODT) authorities. This tool recommends specific allocation mechanisms that match the authority’s objectives and preferences to solve allocation problems for particular contextual scenarios. The paper emphasizes the need for transparency and explainability of resource allocation decisions in ODT systems to be understandable by humans and move toward more controllable resource allocation. We propose in this preliminary work a multi-agent architecture and general implementation guidelines towards meeting these requirements
For more details please refer to the paper with the same title
Optimization with impact: my journey in public sector operations research Laura Albert
eynote talk at the Advances in Data Science & Operations Research Virtual Conference, presented by Universidad Galileo in collaboration with INFORMSttt. It's the first INFORMS conference made for Latino America that brings together the scientific community from the areas of operations research, business intelligence, and data science.
Operations Research for Homeland Security and Beyond!Laura Albert
Operations Research for Homeland Security and Beyond! Laura Albert McLay's talk on aviation security at the University of Wisconsin-Madison's 50th anniversary reunion for ISYE.
Screening Commercial Aviation Passengers in the Aftermath of September 11, 2001Laura Albert
Screening Commercial Aviation Passengers in the Aftermath of September 11, 2001. Slides from a presentation at the the University of Wisconsin-Madison on September 11, 2015.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
PRESENTATION ABOUT PRINCIPLE OF COSMATIC EVALUATION
Modeling Service networks
1. Modeling service networks
Laura Albert
The Industrial & Systems Engineering Department
University of Wisconsin-Madison
laura@engr.wisc.edu
punkrockOR.wordpress.com
@lauraalbertphd
This work was in part supported by the National Science Foundation under Award No. CMMI 1361448, 1444219, 1541165
2. Service networks for public sector OR
Simple definition: Public sector operations research (OR)
is a problem whose outputs are subject to public
scrutiny.
Public sector OR could include problems in these areas:
• Public health and safety: police, fire, emergency
services, public health
• Community development: planning, transportation
• Human services: public assistance, welfare,
drug/alcohol treatment, homeless services
• Nonprofit management: management of community-
oriented service providers
23/26/2018 Laura A. Albert, UW-Madison
3. Service networks in public sector OR
• Public health and safety
• Fire fighters, police officers, paramedics, post-disaster
response and recovery
• Location models for locating vehicles and designing
response districts
• Community development
• School buses, library loan networks
• Vehicle routing problem for bus schedules
• Human services
• Public assistance, meals on wheels
• Staffing models to schedule employees and volunteer shifts
• Nonprofit management
• Humanitarian logistics, volunteers, blood bank operations
• Multicommodity flow models to deliver relief aid
33/26/2018 Laura A. Albert, UW-Madison
4. The origins of public sector OR
44
The President’s
Commission on Law
Enforcement and the
Administration of Justice
(1965)
Al Blumstein chaired the
Commission’s Science
and Technology Task
Force (CMU)
1972
The research was put
into practice
The research was
influential
The research led to many
applied papers published in
the best journals
The research won the top
awards in the field
3/26/2018 Laura A. Albert, UW-Madison
5. Early public sector OR models for public safety
service networks
5
Set cover / maximum cover models
How can we “cover” the maximum
number of locations with
ambulances?
Church, R., & ReVelle, C. (1974). The maximal covering
location problem. Papers in regional science, 32(1),
101-118.
Markov models
How many fire engines should we send?
Swersey, A. J. (1982). A Markovian decision model for deciding how
many fire companies to dispatch. Management Science, 28(4), 352-
365.
Data analytics
How far will a fire
engine travel to a call?
Kolesar, P., & Blum, E. H.
(1973). Square root laws
for fire engine response
distances. Management
Science, 19(12), 1368-1378.
Hypercube queuing models
What is the probability that our first choice
ambulance is unavailable for this call?
Larson, R. C. (1974). A hypercube queuing model for facility location
and redistricting in urban emergency services. Computers &
Operations Research, 1(1), 67-95.
3/26/2018 Laura A. Albert, UW-Madison
6. Motivating example:
Locating ambulances at stations
• We want to locate 𝑠𝑠 idental ambulances at stations in a
geographic region to “cover” the most calls in 9 minutes
• Our initial assumptions:
1. Locate 𝑠𝑠 ambulances at stations
2. Call volumes to vary by location
3. Deterministic travel times (a call is
“covered” by an ambulance or not)
4. Consider the closest ambulance to each call
(ignore backup coverage for now)
63/26/2018 Laura A. Albert, UW-Madison
7. Anatomy of a 911 call
Goal: Response times
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
73/26/2018 Laura A. Albert, UW-Madison
8. Objective functions
• All EMS departments evaluate service according to
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
• Yields a coverage objective function
Why RTTs?
• Easy to measure
• Intuitive
• Unambiguous
83/26/2018 Laura A. Albert, UW-Madison
9. Facility location to model
service networks
93/26/2018 Laura A. Albert, UW-Madison
10. Location models overview
10
• Decide where to locate facilities to serve customers
• (stations / warehouses / shelters / hubs / etc.)
• In order to achieve some balance between
1. Service (coverage, distance)
2. Cost (number of facilities)
Usually two decisions
to make:
1. Where to locate?
2. Which customers are
assigned/allocated to
which facilities?
• Sometimes referred to as
“location–allocation models”
3/26/2018 Laura A. Albert, UW-Madison
11. Applications of Facility Location Models
• Widely applied in public and private sectors:
• Emergency medical services (EMS) / fire stations
• Airline hubs
• Blood banks
• Hazardous waste disposal sites
• Hurricane shelters
• Fast-food restaurants
• Public swimming pools
• Schools
• Vehicle inspection stations
• Bus stops
• etc.
113/26/2018 Laura A. Albert, UW-Madison
12. Classical Models
12
1. P-median problem: minimize demand-weighted distance (DWD)
s.t. locate ≤ P facilities
2. Uncapacitated fixed-charge location problem (UFLP):
minimize fixed cost + DWD
3. P-center problem: minimize maximum distance
s.t. locate ≤ P facilities
4. Maximum covering location problem (MCLP):
maximize covered demands
s.t. locate ≤ P facilities
0 1 2 3 4 5 6 7 8 9 10
0
1
2
3
4
5
6
7
8
9
10
3/26/2018 Laura A. Albert, UW-Madison
14. Notation
14
• Sets
• 𝐼𝐼 = {customers}
• 𝐽𝐽 = {potential facility sites}
• Parameters
• ℎ𝑖𝑖= annual demand of customer 𝑖𝑖 ∈ 𝐼𝐼
• 𝑐𝑐𝑖𝑖𝑖𝑖 = cost to transport one unit from 𝑗𝑗 ∈ 𝐽𝐽 to 𝑖𝑖 ∈ 𝐼𝐼(distance)
• 𝑓𝑓𝑗𝑗 = fixed (annual) cost to open a facility at site 𝑗𝑗 ∈ 𝐽𝐽
• 𝑃𝑃 = number of facilities
• 𝑉𝑉𝑖𝑖= set of facilities that can cover customer 𝑖𝑖 with 𝑉𝑉𝑖𝑖 = {𝑗𝑗 ∈ 𝐽𝐽: 𝑐𝑐𝑖𝑖𝑖𝑖 ≤
𝑅𝑅} and 𝑅𝑅 is the coverage radius
• Decision variables
• 𝑥𝑥𝑗𝑗 = 1 if facility 𝑗𝑗 ∈ 𝐽𝐽 is opened, 0 otherwise
• 𝑦𝑦𝑖𝑖𝑖𝑖 = 1 if facility 𝑗𝑗 ∈ 𝐽𝐽 serves customer 𝑖𝑖 ∈ 𝐼𝐼, 0 otherwise
3/26/2018 Laura A. Albert, UW-Madison
15. Uncapacitated fixed-charge location problem
formulation
15
jiy
jx
jixy
iy
ij
j
jij
Jj
ij
,}1,0{
}1,0{
,
1s.t.
∀∈
∀∈
∀≤
∀=∑∈
∑∑∑ ∈ ∈∈
+
Ii Jj
ijiji
Jj
jj ychxfmin Min fixed + transportation cost
Satisfy all demands
Don’t assign customer to closed facility
Integrality
NOTE: It is always optimal to assign each customer solely to its nearest open facility
Therefore, we think of yij as binary since there is an optimal solution with yij ∈{0,1} for all i, j
Talk about “the facility to which customer i is assigned”
Rather than “the amount of i’s demand served”
3/26/2018 Laura A. Albert, UW-Madison
20. Maximal Covering Formulation
20
iz
jx
ixz
Px
i
j
Vj
ji
Jj
j
i
∀∈
∀∈
∀≤
=
∑
∑
∈
∈
}1,0{
}1,0{
s.t.
∑∈Ii
ii zhmax Maximize covered demand
Locate P facilities
Definition of coverage
Integrality
where Vi = set of facilities that can cover customer i with 𝑉𝑉𝑖𝑖 = {𝑗𝑗 ∈ 𝐽𝐽: 𝑐𝑐𝑖𝑖𝑖𝑖 ≤ 𝑠𝑠}
zi = 1 if customer i is covered, 0 otherwise
How can we adjust this basic model to address ambulances not always being
available?
3/26/2018 Laura A. Albert, UW-Madison
22. Tradeoffs
22
All models achieve some balance between
1. Service distance, cost or coverage
2. Cost often the number of facilities to locate (𝑃𝑃)
Capacity
• Most models also have a capacitated version
• Facilities have fixed throughput capacity (an input)
• Balance workload among service providers
• Sometimes capacity is a decision variable
• Discrete choices (50,000 sq ft / 100,000 sq ft / 200,000 sq ft)
• Continuous variable (cost is a function of capacity)
3/26/2018 Laura A. Albert, UW-Madison
23. P-Median model
23
0 1 2 3 4 5 6 7 8 9 10
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10
0
1
2
3
4
5
6
7
8
9
10
Uncapacitated model Capacitated model
Too much demand assigned
to two stations
New stations selected
Not all demand assigned to closest
open facility
3/26/2018 Laura A. Albert, UW-Madison
24. Let’s revisit our motivating example
• We want to locate 𝑠𝑠 ambulances at stations in a geographic
region to “cover” the most calls in 9 minutes
• We extend the base models to include:
1. Ambulances that are not always available (backup coverage
is important)
2. Each ambulance responds to roughly the same number of
calls (hint: use capacitated model variations)
3. Non-deterministic travel times leading to non-binary
coverage
4. Different types of ambulances
243/26/2018 Laura A. Albert, UW-Madison
25. Lift assumption that every vehicle/facility is always available
Deterministic covering models
with backup coverage
253/26/2018 Laura A. Albert, UW-Madison
26. Maximal Covering Extension
26
Previous models match each customer with one facility
• UFCL, P-median, P-center
Or count calls as “covered” if they are covered at least once
• 𝑧𝑧𝑖𝑖 = 1 if customer 𝑖𝑖 ∈ 𝐼𝐼 is covered at least once, and 0 otherwise.
Ambulances are unavailable for new calls when they are
serving a customer
• Backup service is important!
Models must give credit for backup service.
• Examine through coverage model extensions
• Introduce k-coverage (coverage by k ambulances)
• 𝑧𝑧𝑖𝑖 𝑖𝑖 = 1 if customer 𝑖𝑖 is covered at least 𝑘𝑘 times, 0 otherwise
3/26/2018 Laura A. Albert, UW-Madison
27. Maximal Covering Extensions
27
How could you extend the maximal covering model?
𝑧𝑧𝑖𝑖 𝑖𝑖 = 1 if customer 𝑖𝑖 is covered at least 𝑘𝑘 times, 0 otherwise
Consider 𝑘𝑘 = 2
If location 𝑖𝑖 is covered once: 𝑧𝑧𝑖𝑖 𝑖 = 1, 𝑧𝑧𝑖𝑖 𝑖 = 0
If location 𝑖𝑖 is covered twice: 𝑧𝑧𝑖𝑖 𝑖 = 1, 𝑧𝑧𝑖𝑖 𝑖 = 1
Three cases:
1. We maximize double coverage (𝑧𝑧𝑖𝑖 𝑖)
2. We weigh single and double coverage
𝜃𝜃 = weight associated with single coverage (≥ 1/2)
(1 − 𝜃𝜃 = weight associated with double coverage)
3. We weigh single and double coverage by considering the
proportion of time ambulances are available for service
3/26/2018 Laura A. Albert, UW-Madison
28. Maximal Double Covering Formulation
Hogan and ReVelle 1986
28
Maximize double covered demand
Locate P facilities
Definition of double coverage
Integrality
Note: we are only looking at double
coverage here!
max �
𝑖𝑖∈𝐼𝐼
ℎ𝑖𝑖 𝑧𝑧𝑖𝑖 𝑖
𝑠𝑠. 𝑡𝑡. �
𝑗𝑗∈𝐽𝐽
𝑥𝑥𝑗𝑗 = 𝑃𝑃
1 + 𝑧𝑧𝑖𝑖 𝑖 ≤ �
𝑗𝑗∈𝑉𝑉𝑖𝑖
𝑥𝑥𝑗𝑗 , 𝑖𝑖 ∈ 𝐼𝐼
𝑥𝑥𝑗𝑗 ∈ 0,1 , 𝑗𝑗 ∈ 𝐽𝐽
𝑧𝑧𝑖𝑖 𝑖 ∈ 0,1 , 𝑖𝑖 ∈ 𝐼𝐼
0 1 2 3 4 5 6 7 8 9 10
0
1
2
3
4
5
6
7
8
9
10
3/26/2018 Laura A. Albert, UW-Madison
29. Maximal Multiobjective Covering Formulation
Hogan and ReVelle 1986
29
Weighted average of single and
double covered demand
Locate P facilities
Integrality
max 𝜃𝜃 �
𝑖𝑖∈𝐼𝐼
ℎ𝑖𝑖 𝑧𝑧𝑖𝑖 𝑖 + (1 − 𝜃𝜃) �
𝑖𝑖∈𝐼𝐼
ℎ𝑖𝑖 𝑧𝑧𝑖𝑖2
𝑠𝑠. 𝑡𝑡. �
𝑗𝑗∈𝐽𝐽
𝑥𝑥𝑗𝑗 = 𝑃𝑃
𝑧𝑧𝑖𝑖 𝑖 + 𝑧𝑧𝑖𝑖 𝑖 ≤ �
𝑗𝑗∈𝑉𝑉𝑖𝑖
𝑥𝑥𝑗𝑗 , 𝑖𝑖 ∈ 𝐼𝐼
𝑧𝑧𝑖𝑖2 ≤ 𝑧𝑧𝑖𝑖1, 𝑖𝑖 ∈ 𝐼𝐼
𝑥𝑥𝑗𝑗 ∈ 0,1 , 𝑗𝑗 ∈ 𝐽𝐽
𝑧𝑧𝑖𝑖1, 𝑧𝑧𝑖𝑖 𝑖 ∈ 0,1 , 𝑖𝑖 ∈ 𝐼𝐼
Hierarchical coverage
0 1 2 3 4 5 6 7 8 9 10
0
1
2
3
4
5
6
7
8
9
10
Full coverage for double coverage
Partial credit for single coverage
3/26/2018 Laura A. Albert, UW-Madison
30. Maximal Expected Covering Location Model
Daskin 1983
30
Let’s introduce ambulance busy probability 𝑞𝑞 and assume it’s the same for
all ambulances
zik = 1 if customer i is covered at least k times, 0 otherwise
If location 𝑖𝑖 is covered 𝑘𝑘 times, then the expected covered demand is:
𝐸𝐸𝑘𝑘 = ℎ𝑖𝑖(1 − 𝑃𝑃 not covered by 𝑘𝑘 ambulances )
𝐸𝐸𝑘𝑘 = ℎ𝑖𝑖(1 − 𝑃𝑃(all 𝑘𝑘 ambulances busy)
𝐸𝐸𝑘𝑘 = ℎ𝑖𝑖(1 − 𝑞𝑞𝑘𝑘
)
The marginal contribution of the kth ambulance to this expected value is:
𝐸𝐸𝑘𝑘 − 𝐸𝐸𝑘𝑘−1 = ℎ𝑖𝑖 1 − 𝑞𝑞 𝑞𝑞𝑘𝑘−1 = ℎ𝑖𝑖 𝜃𝜃𝑘𝑘
We can then use this in an integer programming model
3/26/2018 Laura A. Albert, UW-Madison
31. Maximal Expected Covering Location Model
Daskin 1983
Let’s introduce ambulance busy probability 𝑞𝑞 and assume it’s the same for
all ambulances
zik = 1 if customer i is covered at least k times, 0 otherwise
Weight for k-coverage: 𝜃𝜃𝑘𝑘 = 1 − 𝑞𝑞 𝑞𝑞𝑘𝑘−1
31
max �
𝑖𝑖∈𝐼𝐼
�
𝑘𝑘=1
𝑃𝑃
ℎ𝑖𝑖 1 − 𝑞𝑞 𝑞𝑞𝑘𝑘−1
𝑧𝑧𝑖𝑖 𝑖𝑖
𝑠𝑠. 𝑡𝑡. �
𝑗𝑗∈𝐽𝐽
𝑥𝑥𝑗𝑗 = 𝑃𝑃
�
𝑘𝑘
𝑧𝑧𝑖𝑖 𝑖𝑖 ≤ �
𝑗𝑗∈𝑉𝑉𝑖𝑖
𝑥𝑥𝑗𝑗 , 𝑖𝑖 ∈ 𝐼𝐼
𝑥𝑥𝑗𝑗 ∈ 0,1 , 𝑗𝑗 ∈ 𝐽𝐽
𝑧𝑧𝑖𝑖 𝑖𝑖 ∈ 0,1 , 𝑖𝑖 ∈ 𝐼𝐼, 𝑘𝑘 = 1, … , 𝑝𝑝
Maximize covered demand
Locate P facilities
Definition of coverage
Integrality
3/26/2018 Laura A. Albert, UW-Madison
32. You survived your crash course into
location models to support the
modeling of service networks!
3/26/2018 Laura A. Albert, UW-Madison 32
33. References
• Church, Richard, and Charles R. Velle. "The maximal covering location
problem." Papers in regional science 32, no. 1 (1974): 101-118.
• Schilling, David, D. Jack Elzinga, Jared Cohon, Richard Church, and Charles
ReVelle. "The TEAM/FLEET models for simultaneous facility and equipment
siting." Transportation Science 13, no. 2 (1979): 163-175.
• Hogan, Kathleen, and Charles ReVelle. "Concepts and applications of backup
coverage." Management science 32, no. 11 (1986): 1434-1444.
• Daskin, Mark S. "A maximum expected covering location model: formulation,
properties and heuristic solution." Transportation science 17, no. 1 (1983): 48-
70.
• Ansari, Sardar, Laura Albert McLay, and Maria E. Mayorga. "A maximum
expected covering problem for district design." Transportation Science 51, no. 1
(2015): 376-390.
• Goldberg, Jeffrey B. "Operations research models for the deployment of
emergency services vehicles." EMS management Journal 1, no. 1 (2004): 20-39.
• McLay, Laura Albert. "Discrete Optimization Models for Homeland Security and
Disaster Management." Tutorials in Operations Research (2015).
333/26/2018 Laura A. Albert, UW-Madison