Rijul Jain, the operations supervisor at Grooves Distillery Records (Grooves), is trying to decide if and how to address capacity issues at this boutique vinyl record production facility after receiving a large contract that increased demand significantly. Based in Montreal, Quebec, Canada, the small business was started by Josephine “Fina” Leite and her wife, Theresa, to produce vinyl records of modern indie music for vinyl record enthusiasts. Jain must figure out where a bottleneck exists, balance Grooves' line of production workers, and increase production to meet demand.
Rijul Jain, the operations supervisor at Grooves Distillery Records (Grooves), is trying to decide if and how to address capacity issues at this boutique vinyl record production facility after receiving a large contract that increased demand significantly. Based in Montreal, Quebec, Canada, the small business was started by Josephine "Fina" Leite and her wife, Theresa, to produce vinyl records of modern indie music for vinyl record enthusiasts. Jain must figure out where a bottleneck exists, balance Grooves' line of production workers, and increase production to meet demand.
Queues (or waiting lines) are common in modern life. We wait in lines at the campus cafeteria, to board an airplane, in the emergency room, and when we call a company for customer service. We even wait for applications to be initiated and processed by our smartphone processors, and our online orders wait to be fulfilled at companies’ warehouses before being shipped to us. How long we wait in line depends on several factors and parameters. It may depend on the number of servers working and the amount of time it takes to serve each individual customer (the system’s capacity) or on how many customers arrive at any given time (the system’s demand or flow rate). Most importantly, queues form due to random variations in arrival patterns and service times.
Hospitals frequently deal with congestion and blockage that affects patient flow and increased costs. In 2018, at University Hospital, part of South West Health Centre, patient flow in the medical surgical intensive care unit seemed to be highly susceptible to congestion, and this was creating ripple effects throughout the hospital and leading to increased costs. The complexity of patient flow presented an opportunity to build a discrete-event simulation model to provide insights regarding the patient flow, costs, and opportunities for improvement. But how could the hospital’s management use a simulation model of patient flow to determine alternatives to optimize both patient flow and costs?
Hospitals frequently deal with congestion and blockage that affects patient flow and increased costs. In 2018, at University Hospital, part of South West Health Centre, patient flow in the medical surgical intensive care unit seemed to be highly susceptible to congestion, and this was creating ripple effects throughout the hospital and leading to increased costs. The complexity of patient flow presented an opportunity to build a discrete-event simulation model to provide insights regarding the patient flow, costs, and opportunities for improvement. But how could the hospital's management use a simulation model of patient flow to determine alternatives to optimize both patient flow and costs?
Queues (or waiting lines) are common in modern life. We wait in lines at the campus cafeteria, to board an airplane, in the emergency room, and when we call a company for customer service. We even wait for applications to be initiated and processed by our smartphone processors, and our online orders wait to be fulfilled at companies' warehouses before being shipped to us. How long we wait in line depends on several factors and parameters. It may depend on the number of servers working and the amount of time it takes to serve each individual customer (the system's capacity) or on how many customers arrive at any given time (the system's demand or flow rate). Most importantly, queues form due to random variations in arrival patterns and service times.
In 2017, wait times for kidney transplants in Ontario were getting out of hand. While patients from London Health Sciences Centre’s kidney transplant program in London, Ontario, had a reasonable wait of approximately one year, patients in Toronto's kidney transplant program waited almost four years. In an attempt to improve the overall wait times for all Ontario patients, the provincial Ministry of Health intended to merge the two currently independent programs and create a unified wait-list. Two doctors at London Health Sciences Centre were concerned about the effects of the merger for their patients in London, and asked an analytics specialist to determine the effects of the merger. Would the merger have the adverse outcome they expected for their patients’ wait times?
In 2017, wait times for kidney transplants in Ontario were getting out of hand. While patients from London Health Sciences Centre's kidney transplant program in London, Ontario, had a reasonable wait of approximately one year, patients in Toronto's kidney transplant program waited almost four years. In an attempt to improve the overall wait times for all Ontario patients, the provincial Ministry of Health intended to merge the two currently independent programs and create a unified wait-list. Two doctors at London Health Sciences Centre were concerned about the effects of the merger for their patients in London, and asked an analytics specialist to determine the effects of the merger. Would the merger have the adverse outcome they expected for their patients' wait times?
In 2017, while donating blood on campus, a management science graduate student noticed that the mobile blood donor clinic set up at his university’s community centre was a congested tandem queuing system. Finding one-and-one-half hours too long for donors to wait, the student considered how the process could be reduced by at least half an hour. He needed to devise a reasonably precise model to represent the donor flow in the clinic. Using either the mode or the average service times supplied by the nurses, the student could build a relatively straightforward discrete-event simulation model to identify bottlenecks and improve the donor flow.
While donating blood on campus, a management science graduate student noticed that the mobile blood donor clinic set up at his university's community centre was a congested tandem queuing system. Finding one-and-one-half hours too long for donors to wait, the student considered how the process could be reduced by at least half an hour. He needed to devise a reasonably precise model to represent the donor flow in the clinic. Using either the mode or the average service times supplied by the nurses, the student could build a relatively straightforward discrete-event simulation model to identify bottlenecks and improve the donor flow.